diff --git a/.nojekyll b/.nojekyll index c1507fa0..5d32de40 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -72c27ed9 \ No newline at end of file +5c8e4c92 \ No newline at end of file diff --git a/CNAME b/CNAME deleted file mode 100644 index 4e630a5d..00000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -mlsysbook.ai \ No newline at end of file diff --git a/Machine-Learning-Systems.pdf b/Machine-Learning-Systems.pdf index 7e11911d..8abb03cd 100644 Binary files a/Machine-Learning-Systems.pdf and b/Machine-Learning-Systems.pdf differ diff --git a/contents/ai_for_good/ai_for_good.html b/contents/ai_for_good/ai_for_good.html index d37ae310..dffe2fc0 100644 --- a/contents/ai_for_good/ai_for_good.html +++ b/contents/ai_for_good/ai_for_good.html @@ -799,7 +799,7 @@

Other sensors, such as GPS units and accelerometers, can track microclimate conditions, soil humidity, and livestock wellbeing. Local real-time data helps farmers respond and adapt better to changes in the field. TinyML analytics at the edge avoids lag, network disruptions, and the high data costs of cloud-based systems. Localized systems allow customization of specific crops, diseases, and regional issues.

Widespread TinyML applications can help digitize smallholder farms to increase productivity, incomes, and resilience. The low cost of hardware and minimal connectivity requirements make solutions accessible. Projects across the developing world have shown the benefits:

@@ -846,7 +846,7 @@

(“Vector-Borne Diseases,” n.d.). Diseases like malaria, dengue, and Zika are especially prevalent in resource-limited regions lacking robust infrastructure for mosquito control. Monitoring local mosquito populations is essential to prevent outbreaks and properly target interventions.

“Vector-Borne Diseases.” n.d. https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases. -

Traditional monitoring methods are expensive, labor-intensive, and difficult to deploy remotely. The proposed TinyML solution aims to overcome these barriers. Small microphones coupled with machine learning algorithms can classify mosquitoes by species based on minute differences in wing oscillations. The TinyML software runs efficiently on low-cost microcontrollers, eliminating the need for continuous connectivity.

+

Traditional monitoring methods are expensive, labor-intensive, and difficult to deploy remotely. The proposed TinyML solution overcomes these barriers. Small microphones coupled with machine learning algorithms can classify mosquitoes by species based on minute differences in wing oscillations. The TinyML software runs efficiently on low-cost microcontrollers, eliminating the need for continuous connectivity.

A collaborative research team from the University of Khartoum and the ICTP is exploring an innovative solution using TinyML. In a recent paper, they presented a low-cost device that can identify disease-spreading mosquito species through their wing beat sounds (Altayeb, Zennaro, and Rovai 2022).

Altayeb, Moez, Marco Zennaro, and Marcelo Rovai. 2022. “Classifying Mosquito Wingbeat Sound Using TinyML.” In Proceedings of the 2022 ACM Conference on Information Technology for Social Good, 132–37. ACM. https://doi.org/10.1145/3524458.3547258. diff --git a/contents/benchmarking/benchmarking.html b/contents/benchmarking/benchmarking.html index 776dc59b..de5e4046 100644 --- a/contents/benchmarking/benchmarking.html +++ b/contents/benchmarking/benchmarking.html @@ -795,7 +795,7 @@

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An alternative paradigm is emerging called data-centric AI. Rather than treating data as static and focusing narrowly on model performance, this approach recognizes that models are only as good as their training data. So, the emphasis shifts to curating high-quality datasets that better reflect real-world complexity, developing more informative evaluation benchmarks, and carefully considering how data is sampled, preprocessed, and augmented. The goal is to optimize model behavior by improving the data rather than just optimizing metrics on flawed datasets. Data-centric AI critically examines and enhances the data itself to produce beneficial AI. This reflects an important evolution in mindset as the field addresses the shortcomings of narrow benchmarking.

-

This section will explore the key differences between model-centric and data-centric approaches to AI. This distinction has important implications for how we benchmark AI systems. Specifically, we will see how focusing on data quality and Efficiency can directly improve machine learning performance as an alternative to optimizing model architectures solely. The data-centric approach recognizes that models are only as good as their training data. So, enhancing data curation, evaluation benchmarks, and data handling processes can produce AI systems that are safer, fairer, and more robust. Rethinking benchmarking to prioritize data alongside models represents an important evolution as the field aims to deliver trustworthy real-world impact.

+

This section will explore the key differences between model-centric and data-centric approaches to AI. This distinction has important implications for how we benchmark AI systems. Specifically, we will see how focusing on data quality and Efficiency can directly improve machine learning performance as an alternative to optimizing model architectures solely. The data-centric approach recognizes that models are only as good as their training data. So, enhancing data curation, evaluation benchmarks, and data handling processes can produce AI systems that are safer, fairer, and more robust. Rethinking benchmarking to prioritize data alongside models represents an important evolution as the field strives to deliver trustworthy real-world impact.

11.6.1 Limitations of Model-Centric AI

In the model-centric AI era, a prominent characteristic was the development of complex model architectures. Researchers and practitioners dedicated substantial effort to devising sophisticated and intricate models in the quest for superior performance. This frequently involved the incorporation of additional layers and the fine-tuning of a multitude of hyperparameters to achieve incremental improvements in accuracy. Concurrently, there was a significant emphasis on leveraging advanced algorithms. These algorithms, often at the forefront of the latest research, were employed to improve the performance of AI models. The primary aim of these algorithms was to optimize the learning process of models, thereby extracting maximal information from the training data.

@@ -1479,11 +1479,11 @@

(Mattson et al. 2020b).

Additionally, a data-centric approach can often lead to simpler models that are easier to interpret and maintain. This is because the emphasis is on the data rather than the model architecture, meaning simpler models can achieve high performance when trained on high-quality data.

-

The shift towards data-centric AI represents a significant paradigm shift. By prioritizing the quality of the input data, this approach aims to improve model performance and generalization capabilities, ultimately leading to more robust and reliable AI systems. As we continue to advance in our understanding and application of AI, the data-centric approach is likely to play an important role in shaping the future of this field.

+

The shift towards data-centric AI represents a significant paradigm shift. By prioritizing the quality of the input data, this approach tries to model performance and generalization capabilities, ultimately leading to more robust and reliable AI systems. As we continue to advance in our understanding and application of AI, the data-centric approach is likely to play an important role in shaping the future of this field.

11.6.3 Benchmarking Data

-

Data benchmarking aims to evaluate common issues in datasets, such as identifying label errors, noisy features, representation imbalance (for example, out of the 1000 classes in Imagenet-1K, there are over 100 categories which are just types of dogs), class imbalance (where some classes have many more samples than others), whether models trained on a given dataset can generalize to out-of-distribution features, or what types of biases might exist in a given dataset (Mattson et al. 2020b). In its simplest form, data benchmarking aims to improve accuracy on a test set by removing noisy or mislabeled training samples while keeping the model architecture fixed. Recent competitions in data benchmarking have invited participants to submit novel augmentation strategies and active learning techniques.

+

Data benchmarking focuses on evaluating common issues in datasets, such as identifying label errors, noisy features, representation imbalance (for example, out of the 1000 classes in Imagenet-1K, there are over 100 categories which are just types of dogs), class imbalance (where some classes have many more samples than others), whether models trained on a given dataset can generalize to out-of-distribution features, or what types of biases might exist in a given dataset (Mattson et al. 2020b). In its simplest form, data benchmarking seeks to improve accuracy on a test set by removing noisy or mislabeled training samples while keeping the model architecture fixed. Recent competitions in data benchmarking have invited participants to submit novel augmentation strategies and active learning techniques.

Mattson, Peter, Vijay Janapa Reddi, Christine Cheng, Cody Coleman, Greg Diamos, David Kanter, Paulius Micikevicius, et al. 2020b. “MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance.” IEEE Micro 40 (2): 8–16. https://doi.org/10.1109/mm.2020.2974843.

Data-centric techniques continue to gain attention in benchmarking, especially as foundation models are increasingly trained on self-supervised objectives. Compared to smaller datasets like Imagenet-1K, massive datasets commonly used in self-supervised learning, such as Common Crawl, OpenImages, and LAION-5B, contain higher amounts of noise, duplicates, bias, and potentially offensive data.

diff --git a/contents/conclusion/conclusion.html b/contents/conclusion/conclusion.html index 3f1483d0..d2ef4cb9 100644 --- a/contents/conclusion/conclusion.html +++ b/contents/conclusion/conclusion.html @@ -694,7 +694,7 @@

Our journey started by tracing ML’s historical trajectory, from its theoretical foundations to its current state as a transformative force across industries (Chapter 3). This journey has highlighted the remarkable progress in the field, challenges, and opportunities.

Throughout this book, we have looked into the intricacies of ML systems, examining the critical components and best practices necessary to create a seamless and efficient pipeline. From data preprocessing and model training to deployment and monitoring, we have provided insights and guidance to help readers navigate the complex landscape of ML system development.

ML systems involve complex workflows, spanning various topics from data engineering to model deployment on diverse systems (Chapter 4). By providing an overview of these ML system components, we have aimed to showcase the tremendous depth and breadth of the field and expertise that is needed. Understanding the intricacies of ML workflows is crucial for practitioners and researchers alike, as it enables them to navigate the landscape effectively and develop robust, efficient, and impactful ML solutions.

-

By focusing on the systems aspect of ML, we aim to bridge the gap between theoretical knowledge and practical implementation. Just as a healthy human body system allows the organs to function optimally, a well-designed ML system enables the models to consistently deliver accurate and reliable results. This book aims to empower readers with the knowledge and tools necessary to build ML systems that showcase the underlying models’ power and ensure smooth integration and operation, much like a well-functioning human body.

+

By focusing on the systems aspect of ML, we aim to bridge the gap between theoretical knowledge and practical implementation. Just as a healthy human body system allows the organs to function optimally, a well-designed ML system enables the models to consistently deliver accurate and reliable results. This book’s goal is to empower readers with the knowledge and tools necessary to build ML systems that showcase the underlying models’ power and ensure smooth integration and operation, much like a well-functioning human body.

20.2 Knowing the Importance of ML Datasets

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Contributors & Thanks

Naeem Khoshnevis
Naeem Khoshnevis
-Douwe den Blanken
Douwe den Blanken
+jasonjabbour
jasonjabbour
-jasonjabbour
jasonjabbour
+Douwe den Blanken
Douwe den Blanken
-Marcelo Rovai
Marcelo Rovai
- - shanzehbatool
shanzehbatool
-Matthew Stewart
Matthew Stewart
+Marcelo Rovai
Marcelo Rovai
Elias Nuwara
Elias Nuwara
@@ -758,10 +755,13 @@

Contributors & Thanks

kai4avaya
kai4avaya
+ +Jared Ping
Jared Ping
+ -Jared Ping
Jared Ping
+Matthew Stewart
Matthew Stewart
Itai Shapira
Itai Shapira
@@ -773,7 +773,7 @@

Contributors & Thanks

Jayson Lin
Jayson Lin
-Andrea
Andrea
+Sophia Cho
Sophia Cho
@@ -781,7 +781,7 @@

Contributors & Thanks

Jeffrey Ma
Jeffrey Ma
-Sophia Cho
Sophia Cho
+Andrea
Andrea
Alex Rodriguez
Alex Rodriguez
@@ -790,12 +790,12 @@

Contributors & Thanks

Korneel Van den Berghe
Korneel Van den Berghe
-Colby Banbury
Colby Banbury
+Zishen Wan
Zishen Wan
-Zishen Wan
Zishen Wan
+Colby Banbury
Colby Banbury
Sara Khosravi
Sara Khosravi
@@ -812,13 +812,13 @@

Contributors & Thanks

-Aghyad Deeb
Aghyad Deeb
+marin-llobet
marin-llobet
-Emeka Ezike
Emeka Ezike
+Aghyad Deeb
Aghyad Deeb
-arnaumarin
arnaumarin
+oishib
oishib
Aditi Raju
Aditi Raju
@@ -832,7 +832,7 @@

Contributors & Thanks

Jared Ni
Jared Ni
-oishib
oishib
+Emil Njor
Emil Njor
Haoran Qiu
Haoran Qiu
@@ -841,89 +841,72 @@

Contributors & Thanks

Michael Schnebly
Michael Schnebly
-Emil Njor
Emil Njor
+Henry Bae
Henry Bae
-Henry Bae
Henry Bae
+Jae-Won Chung
Jae-Won Chung
Mark Mazumder
Mark Mazumder
-Jae-Won Chung
Jae-Won Chung
+Yu-Shun Hsiao
Yu-Shun Hsiao
-Yu-Shun Hsiao
Yu-Shun Hsiao
+Emeka Ezike
Emeka Ezike
-Marco Zennaro
Marco Zennaro
+eurashin
eurashin
-eurashin
eurashin
+Jennifer Zhou
Jennifer Zhou
-Andrew Bass
Andrew Bass
+Marco Zennaro
Marco Zennaro
-Pong Trairatvorakul
Pong Trairatvorakul
+Shvetank Prakash
Shvetank Prakash
-Jennifer Zhou
Jennifer Zhou
+Andrew Bass
Andrew Bass
-Shvetank Prakash
Shvetank Prakash
+Pong Trairatvorakul
Pong Trairatvorakul
-Alex Oesterling
Alex Oesterling
- - Allen-Kuang
Allen-Kuang
Bruno Scaglione
Bruno Scaglione
-gnodipac886
gnodipac886
+Sercan Aygün
Sercan Aygün
Gauri Jain
Gauri Jain
- - Fin Amin
Fin Amin
- -Sercan Aygün
Sercan Aygün
- - -Baldassarre Cesarano
Baldassarre Cesarano
- - -Yang Zhou
Yang Zhou
- - -abigailswallow
abigailswallow
- -yanjingl
yanjingl
+gnodipac886
gnodipac886
-Jason Yik
Jason Yik
+Alex Oesterling
Alex Oesterling
-happyappledog
happyappledog
+abigailswallow
abigailswallow
-Curren Iyer
Curren Iyer
+Yang Zhou
Yang Zhou
Emmanuel Rassou
Emmanuel Rassou
@@ -931,42 +914,42 @@

Contributors & Thanks

-Jessica Quaye
Jessica Quaye
- - -Jason Yik
Jason Yik
- - -Shreya Johri
Shreya Johri
+happyappledog
happyappledog
Jessica Quaye
Jessica Quaye
-The Random DIY
The Random DIY
+Jason Yik
Jason Yik
Sonia Murthy
Sonia Murthy
-Vijay Edupuganti
Vijay Edupuganti
+Shreya Johri
Shreya Johri
+The Random DIY
The Random DIY
+ + Costin-Andrei Oncescu
Costin-Andrei Oncescu
+Baldassarre Cesarano
Baldassarre Cesarano
+ + Annie Laurie Cook
Annie Laurie Cook
Vijay Edupuganti
Vijay Edupuganti
+ + Jothi Ramaswamy
Jothi Ramaswamy
- - Batur Arslan
Batur Arslan
diff --git a/contents/data_engineering/data_engineering.html b/contents/data_engineering/data_engineering.html index f9f1de34..de81667d 100644 --- a/contents/data_engineering/data_engineering.html +++ b/contents/data_engineering/data_engineering.html @@ -818,7 +818,7 @@

A solid project foundation is essential for its trajectory and eventual success. Central to this foundation is first identifying a clear problem, such as ensuring that voice commands in voice assistance systems are recognized consistently across varying environments. Clear objectives, like creating representative datasets for diverse scenarios, provide a unified direction. Benchmarks, such as system accuracy in keyword detection, offer measurable outcomes to gauge progress. Engaging with stakeholders, from end-users to investors, provides invaluable insights and ensures alignment with market needs. Additionally, understanding platform constraints is important when exploring areas like voice assistance. Embedded systems, such as microcontrollers, come with inherent processing power, memory, and energy efficiency limitations. Recognizing these limitations ensures that functionalities, like keyword detection, are tailored to operate optimally, balancing performance with resource conservation.

In this context, using KWS as an example, we can break each of the steps out as follows:

    -

  1. Identifying the Problem: At its core, KWS aims to detect specific keywords amidst ambient sounds and other spoken words. The primary problem is to design a system that can recognize these keywords with high accuracy, low latency, and minimal false positives or negatives, especially when deployed on devices with limited computational resources.

    2. +

  2. Identifying the Problem: At its core, KWS detects specific keywords amidst ambient sounds and other spoken words. The primary problem is to design a system that can recognize these keywords with high accuracy, low latency, and minimal false positives or negatives, especially when deployed on devices with limited computational resources.

  3. Setting Clear Objectives: The objectives for a KWS system might include:

    • Achieving a specific accuracy rate (e.g., 98% accuracy in keyword detection).
      • diff --git a/contents/dl_primer/dl_primer.html b/contents/dl_primer/dl_primer.html index 02bb9c81..349fba0b 100644 --- a/contents/dl_primer/dl_primer.html +++ b/contents/dl_primer/dl_primer.html @@ -830,7 +830,7 @@

      3.1.4 Relevance to Embedded AI

      -

      Embedded AI, the integration of AI algorithms directly into hardware devices, naturally gains from deep learning capabilities. Combining deep learning algorithms and embedded systems has laid the groundwork for intelligent, autonomous devices capable of advanced on-device data processing and analysis. Deep learning aids in extracting complex patterns and information from input data, which is essential in developing smart embedded systems, from household appliances to industrial machinery. This collaboration aims to usher in a new era of intelligent, interconnected devices that can learn and adapt to user behavior and environmental conditions, optimizing performance and offering unprecedented convenience and efficiency.

      +

      Embedded AI, the integration of AI algorithms directly into hardware devices, naturally gains from deep learning capabilities. Combining deep learning algorithms and embedded systems has laid the groundwork for intelligent, autonomous devices capable of advanced on-device data processing and analysis. Deep learning aids in extracting complex patterns and information from input data, which is essential in developing smart embedded systems, from household appliances to industrial machinery. This collaboration ushers in a new era of intelligent, interconnected devices that can learn and adapt to user behavior and environmental conditions, optimizing performance and offering unprecedented convenience and efficiency.

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3.2.3 Training Process

A neural network receives an input, performs a calculation, and produces a prediction. The prediction is determined by the calculations performed within the sets of perceptrons found between the input and output layers. These calculations depend primarily on the input and the weights. Since you do not have control over the input, the objective during training is to adjust the weights in such a way that the output of the network provides the most accurate prediction.

-

The training process involves several key steps, beginning with the forward pass, where the existing weights of the network are used to calculate the output for a given input. This output is then compared to the true target values to calculate an error, which measures how well the network’s prediction matches the expected outcome. Following this, a backward pass is performed. This involves using the error to make adjustments to the weights of the network through a process called backpropagation. This adjustment aims to reduce the error in subsequent predictions. The cycle of forward pass, error calculation, and backward pass is repeated iteratively. This process continues until the network’s predictions are sufficiently accurate or a predefined number of iterations is reached, effectively minimizing the loss function used to measure the error.

+

The training process involves several key steps, beginning with the forward pass, where the existing weights of the network are used to calculate the output for a given input. This output is then compared to the true target values to calculate an error, which measures how well the network’s prediction matches the expected outcome. Following this, a backward pass is performed. This involves using the error to make adjustments to the weights of the network through a process called backpropagation. This adjustment reduces the error in subsequent predictions. The cycle of forward pass, error calculation, and backward pass is repeated iteratively. This process continues until the network’s predictions are sufficiently accurate or a predefined number of iterations is reached, effectively minimizing the loss function used to measure the error.

Forward Pass

The forward pass is the initial phase where data moves through the network from the input to the output layer. At the start of training, the network’s weights are randomly initialized, setting the initial conditions for learning. During the forward pass, each layer performs specific computations on the input data using these weights and biases, and the results are then passed to the subsequent layer. The final output of this phase is the network’s prediction. This prediction is compared to the actual target values present in the dataset to calculate the loss, which can be thought of as the difference between the predicted outputs and the target values. The loss quantifies the network’s performance at this stage, providing a crucial metric for the subsequent adjustment of weights during the backward pass.

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Recurrent N

Generative Adversarial Networks (GANs)

-

GANs consist of two networks, a generator and a discriminator, trained simultaneously through adversarial training (Goodfellow et al. 2020). The generator produces data that tries to mimic the real data distribution, while the discriminator aims to distinguish between real and generated data. GANs are widely used in image generation, style transfer, and data augmentation.

+

GANs consist of two networks, a generator and a discriminator, trained simultaneously through adversarial training (Goodfellow et al. 2020). The generator produces data that tries to mimic the real data distribution, while the discriminator distinguishes between real and generated data. GANs are widely used in image generation, style transfer, and data augmentation.

Goodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. “Generative Adversarial Networks.” Commun. ACM 63 (11): 139–44. https://doi.org/10.1145/3422622.

In embedded settings, GANs could be used for on-device data augmentation to improve the training of models directly on the embedded device, enabling continual learning and adaptation to new data without the need for cloud computing resources.

diff --git a/contents/efficient_ai/efficient_ai.html b/contents/efficient_ai/efficient_ai.html index 0430f7de..516688bb 100644 --- a/contents/efficient_ai/efficient_ai.html +++ b/contents/efficient_ai/efficient_ai.html @@ -666,7 +666,7 @@

Table of contents

  • 8.3 Efficient Model Architectures
  • 8.4 Efficient Model Compression
  • 8.5 Efficient Inference Hardware
    • -
  • 8.6 Efficient Numerics +
  • 8.6 Efficient Numerics
    • 8.6.1 Numerical Formats
    • 8.6.2 Efficiency Benefits
      • @@ -833,8 +833,8 @@

      Section 10.3, we will explore these in greater detail.

      Efficient hardware for inference speeds up the process, saves energy, extends battery life, and can operate in real-time conditions. As AI continues to be integrated into myriad applications, from smart cameras to voice assistants, the role of optimized hardware will only become more prominent. By leveraging these specialized hardware components, developers and engineers can bring the power of AI to devices and situations that were previously unthinkable.

    • -
    -

    8.6 Efficient Numerics

    +
    +

    8.6 Efficient Numerics

    Machine learning, and especially deep learning, involves enormous amounts of computation. Models can have millions to billions of parameters, often trained on vast datasets. Every operation, every multiplication or addition, demands computational resources. Therefore, the precision of the numbers used in these operations can significantly impact the computational speed, energy consumption, and memory requirements. This is where the concept of efficient numerics comes into play.

    8.6.1 Numerical Formats

    diff --git a/contents/frameworks/frameworks.html b/contents/frameworks/frameworks.html index 20617aff..4707b9f9 100644 --- a/contents/frameworks/frameworks.html +++ b/contents/frameworks/frameworks.html @@ -1416,7 +1416,7 @@

    6.5.3 AutoML, No-Code/Low-Code ML

    -

    In many cases, machine learning can have a relatively high barrier of entry compared to other fields. To successfully train and deploy models, one needs to have a critical understanding of a variety of disciplines, from data science (data processing, data cleaning), model structures (hyperparameter tuning, neural network architecture), hardware (acceleration, parallel processing), and more depending on the problem at hand. The complexity of these problems has led to the introduction of frameworks such as AutoML, which aims to make “Machine learning available for non-Machine Learning experts” and to “automate research in machine learning.” They have constructed AutoWEKA, which aids in the complex process of hyperparameter selection, and Auto-sklearn and Auto-pytorch, an extension of AutoWEKA into the popular sklearn and PyTorch Libraries.

    +

    In many cases, machine learning can have a relatively high barrier of entry compared to other fields. To successfully train and deploy models, one needs to have a critical understanding of a variety of disciplines, from data science (data processing, data cleaning), model structures (hyperparameter tuning, neural network architecture), hardware (acceleration, parallel processing), and more depending on the problem at hand. The complexity of these problems has led to the introduction of frameworks such as AutoML, which tries to make “Machine learning available for non-Machine Learning experts” and to “automate research in machine learning.” They have constructed AutoWEKA, which aids in the complex process of hyperparameter selection, and Auto-sklearn and Auto-pytorch, an extension of AutoWEKA into the popular sklearn and PyTorch Libraries.

    While these efforts to automate parts of machine learning tasks are underway, others have focused on making machine learning models easier by deploying no-code/low-code machine learning, utilizing a drag-and-drop interface with an easy-to-navigate user interface. Companies such as Apple, Google, and Amazon have already created these easy-to-use platforms to allow users to construct machine learning models that can integrate into their ecosystem.

    These steps to remove barriers to entry continue to democratize machine learning, make it easier for beginners to access, and simplify workflow for experts.

    @@ -1425,7 +1425,7 @@

    Transfer Learning

    Transfer learning is the practice of using knowledge gained from a pre-trained model to train and improve the performance of a model for a different task. For example, models such as MobileNet and ResNet are trained on the ImageNet dataset. To do so, one may freeze the pre-trained model, utilizing it as a feature extractor to train a much smaller model built on top of the feature extraction. One can also fine-tune the entire model to fit the new task. Machine learning frameworks make it easy to load pre-trained models, freeze specific layers, and train custom layers on top. They simplify this process by providing intuitive APIs and easy access to large repositories of pre-trained models.

    -

    Transfer learning has challenges, such as the modified model’s inability to conduct its original tasks after transfer learning. Papers such as “Learning without Forgetting” by Z. Li and Hoiem (2018) aims to address these challenges and have been implemented in modern machine learning platforms.

    +

    Transfer learning has challenges, such as the modified model’s inability to conduct its original tasks after transfer learning. Papers such as “Learning without Forgetting” by Z. Li and Hoiem (2018) try to address these challenges and have been implemented in modern machine learning platforms.

    Li, Zhizhong, and Derek Hoiem. 2018. “Learning Without Forgetting.” IEEE Trans. Pattern Anal. Mach. Intell. 40 (12): 2935–47. https://doi.org/10.1109/tpami.2017.2773081.

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    Table of contents

  • 10.3.5 Central Processing Units (CPUs)
      @@ -803,8 +802,7 @@

    • 10.2 Background and Basics

    @@ -844,13 +842,13 @@

    10.2.2 The Need for Acceleration

    -

    The evolution of hardware acceleration is closely tied to the broader history of computing. In the early decades, chip design was governed by Moore’s Law and Dennard Scaling, which observed that the number of transistors on an integrated circuit doubled yearly, and their performance (speed) increased as transistors became smaller. At the same time, power density (power per unit area) remains constant. These two laws were held through the single-core era. Figure 10.1 shows the trends of different microprocessor metrics. As the figure denotes, Dennard Scaling fails around the mid-2000s; notice how the clock speed (frequency) remains almost constant even as the number of transistors keeps increasing.

    +

    The evolution of hardware acceleration is closely tied to the broader history of computing. Central to this history is the role of transistors, the fundamental building blocks of modern electronics. Transistors act as tiny switches that can turn on or off, enabling the complex computations that drive everything from simple calculators to advanced machine learning models. In the early decades, chip design was governed by Moore’s Law, which predicted that the number of transistors on an integrated circuit would double approximately every two years, and Dennard Scaling, which observed that as transistors became smaller, their performance (speed) increased, while power density (power per unit area) remained constant. These two laws were held through the single-core era. Figure 10.1 shows the trends of different microprocessor metrics. As the figure denotes, Dennard Scaling fails around the mid-2000s; notice how the clock speed (frequency) remains almost constant even as the number of transistors keeps increasing.

    However, as Patterson and Hennessy (2016) describes, technological constraints eventually forced a transition to the multicore era, with chips containing multiple processing cores to deliver performance gains. Power limitations prevented further scaling, which led to “dark silicon” (Dark Silicon), where not all chip areas could be simultaneously active (Xiu 2019).

    Patterson, David A, and John L Hennessy. 2016. Computer Organization and Design ARM Edition: The Hardware Software Interface. Morgan kaufmann.
    Xiu, Liming. 2019. “Time Moore: Exploiting Moore’s Law from the Perspective of Time.” IEEE Solid-State Circuits Mag. 11 (1): 39–55. https://doi.org/10.1109/mssc.2018.2882285. -

    The concept of dark silicon emerged as a consequence of these constraints. “Dark silicon” refers to portions of the chip that cannot be powered simultaneously due to thermal and power limitations. Essentially, as the density of transistors increased, the proportion of the chip that could be actively used without overheating or exceeding power budgets shrank.

    +

    “Dark silicon” refers to portions of the chip that cannot be powered simultaneously due to thermal and power limitations. Essentially, as the density of transistors increased, the proportion of the chip that could be actively used without overheating or exceeding power budgets shrank.

    This phenomenon meant that while chips had more transistors, not all could be operational simultaneously, limiting potential performance gains. This power crisis necessitated a shift to the accelerator era, with specialized hardware units tailored for specific tasks to maximize efficiency. The explosion in AI workloads further drove demand for customized accelerators. Enabling factors included new programming languages, software tools, and manufacturing advances.

    @@ -869,28 +867,33 @@

    The design of specialized hardware accelerators involves navigating complex tradeoffs between performance, power efficiency, silicon area, and workload-specific optimizations. This section outlines core considerations and methodologies for achieving an optimal balance based on application requirements and hardware constraints.

    Performance Within Power Budgets

    -

    Performance refers to the throughput of computational work per unit of time, commonly measured in floating point operations per second (FLOPS) or frames per second (FPS). Higher performance enables completing more work, but power consumption rises with activity.

    -

    Hardware accelerators aim to maximize performance within set power budgets. This requires careful balancing of parallelism, the chip’s clock frequency, the operating voltage, workload optimization, and other techniques to maximize operations per watt.

    +

    To understand how to achieve the right balance between performance and power budgets, it’s important to first define a few key concepts that play a crucial role in this process. Performance broadly refers to the overall capability of a system to complete computational tasks effectively within given constraints. One of the key components of performance is throughput, which is the rate at which these tasks are processed, commonly measured in floating point operations per second (FLOPS) or frames per second (FPS). Throughput depends heavily on parallelism—the ability of the hardware to carry out multiple operations simultaneously—and clock frequency, which is the speed at which the processor cycles through these operations. Higher throughput typically leads to better performance, but it also increases power consumption as activity rises.

    +

    Simply maximizing throughput is not enough; the efficiency of the hardware also matters. Efficiency is the measure of how many operations are performed per watt of power consumed, reflecting the relationship between computational work and energy use. In scenarios where power is a limiting factor, such as in edge devices, achieving high efficiency is critical. To help you remember how these concepts interconnect, consider the following relationships:

    • Performance = Throughput * Efficiency
    • Throughput ~= Parallelism * Clock Frequency
    • Efficiency = Operations / Watt
    +

    Hardware accelerators aim to maximize performance within set power budgets. This requires careful balancing of parallelism, the chip’s clock frequency, the operating voltage, workload optimization, and other techniques to maximize operations per watt.

    For example, GPUs achieve high throughput via massively parallel architectures. However, their efficiency is lower than that of customized application-specific integrated circuits (ASICs) like Google’s TPU, which optimize for a specific workload.

    Managing Silicon Area and Costs

    -

    Chip area directly impacts manufacturing cost. Larger die sizes require more materials, lower yields, and higher defect rates. Multi-die packages help scale designs but add packaging complexity. Silicon area depends on:

    +

    The size of a chip’s area has a direct impact on its manufacturing cost. To understand why, it helps to know a bit about the manufacturing process.

    +

    Chips are created from large, thin slices of semiconductor material known as wafers. During manufacturing, each wafer is divided into multiple smaller blocks called dies, with each die containing the circuitry for an individual chip. After the wafer is processed, it’s cut into these individual dies, which are then packaged to form the final chips used in electronic devices.

    +

    Larger dies require more material and are more prone to defects, which can lower the yield—meaning fewer usable chips are produced from each wafer. While manufacturers can scale designs by combining multiple smaller dies into a single package (multi-die packages), this adds complexity and cost to the packaging and production process.

    +

    The amount of silicon area needed on a die depends on several factors:

    • Computational resources - e.g., number of cores, memory, caches
    • Manufacturing process node - smaller transistors enable higher density
    • Programming model - programmed accelerators require more flexibility
    -

    Accelerator design involves squeezing maximum performance within area constraints. Techniques like pruning and compression help fit larger models on the chip.

    +

    Accelerator design involves squeezing maximum performance within these silicon area constraints. Techniques like pruning and compression help fit larger models onto the chip without exceeding the available space.

    Workload-Specific Optimizations

    -

    The target workload dictates optimal accelerator architectures. Some of the key considerations include:

    +

    Designing effective hardware accelerators requires tailoring the architecture to the specific demands of the target workload. Different types of workloads—whether in AI, graphics, or robotics—have unique characteristics that dictate how the accelerator should be optimized.

    +

    Some of the key considerations when optimizing hardware for specific workloads include:

    • Memory vs Compute boundedness: Memory-bound workloads require more memory bandwidth, while compute-bound apps need arithmetic throughput.
    • Data locality: Data movement should be minimized for efficiency. Near-compute memory helps.
      • @@ -899,26 +902,25 @@

      Workload-S
    • Pipelining: Overlapped execution of operations increases throughput.

    Understanding workload characteristics enables customized acceleration. For example, convolutional neural networks use sliding window operations optimally mapped to spatial arrays of processing elements.

    -

    By navigating these architectural tradeoffs, hardware accelerators can deliver massive performance gains and enable emerging applications in AI, graphics, scientific computing, and other domains.

    +

    By understanding these architectural tradeoffs, designers can make informed decisions about the hardware accelerator’s architecture, ensuring that it delivers the best possible performance for its intended use.

    Sustainable Hardware Design

    In recent years, AI sustainability has become a pressing concern driven by two key factors - the exploding scale of AI workloads and their associated energy consumption.

    First, the size of AI models and datasets has rapidly grown. For example, based on OpenAI’s AI computing trends, the amount of computing used to train state-of-the-art models doubles every 3.5 months. This exponential growth requires massive computational resources in data centers.

    Second, the energy usage of AI training and inference presents sustainability challenges. Data centers running AI applications consume substantial energy, contributing to high carbon emissions. It’s estimated that training a large AI model can have a carbon footprint of 626,000 pounds of CO2 equivalent, almost 5 times the lifetime emissions of an average car.

    -

    As a result, AI research and practice must prioritize energy efficiency and carbon impact alongside accuracy. There is an increasing focus on model efficiency, data center design, hardware optimization, and other solutions to improve sustainability. Striking a balance between AI progress and environmental responsibility has emerged as a key consideration and an area of active research across the field.

    -

    The scale of AI systems is expected to keep growing. Developing sustainable AI is crucial for managing the environmental footprint and enabling widespread beneficial deployment of this transformative technology.

    +

    To address these challenges, sustainable hardware design focuses on optimizing energy efficiency without compromising performance. This involves developing specialized accelerators that minimize energy consumption while maximizing computational throughput.

    We will learn about Sustainable AI in a later chapter, where we will discuss it in more detail.

    10.3 Accelerator Types

    -

    Hardware accelerators can take on many forms. They can exist as a widget (like the Neural Engine in the Apple M1 chip) or as entire chips specially designed to perform certain tasks very well. This section will examine processors for machine learning workloads along the spectrum from highly specialized ASICs to more general-purpose CPUs. We first focus on custom hardware purpose-built for AI to understand the most extreme optimizations possible when design constraints are removed. This establishes a ceiling for performance and efficiency.

    -

    We then progressively consider more programmable and adaptable architectures, discussing GPUs and FPGAs. These make tradeoffs in customization to maintain flexibility. Finally, we cover general-purpose CPUs that sacrifice optimizations for a particular workload in exchange for versatile programmability across applications.

    +

    Hardware accelerators can take on many forms. They can exist as a widget (like the Neural Engine in the Apple M1 chip) or as entire chips specially designed to perform certain tasks very well. This section will examine processors for machine learning workloads along the spectrum from highly specialized ASICs to more general-purpose CPUs.

    +

    We first focus on custom hardware purpose-built for AI to understand the most extreme optimizations possible when design constraints are removed. This establishes a ceiling for performance and efficiency. We then progressively consider more programmable and adaptable architectures, discussing GPUs and FPGAs. These make tradeoffs in customization to maintain flexibility. Finally, we cover general-purpose CPUs that sacrifice optimizations for a particular workload in exchange for versatile programmability across applications.

    By structuring the analysis along this spectrum, we aim to illustrate the fundamental tradeoffs between utilization, efficiency, programmability, and flexibility in accelerator design. The optimal balance point depends on the constraints and requirements of the target application. This spectrum perspective provides a framework for reasoning about hardware choices for machine learning and the capabilities required at each level of specialization.

    Figure 10.2 illustrates the complex interplay between flexibility, performance, functional diversity, and area of architecture design. Notice how the ASIC is on the bottom-right corner, with minimal area, flexibility, and power consumption and maximal performance, due to its highly specialized application-specific nature. A key tradeoff is functional diversity vs performance: general purpose architectures can serve diverse applications but their application performance is degraded as compared to more customized architectures.

    -

    The progression begins with the most specialized option, ASICs purpose-built for AI, to ground our understanding in the maximum possible optimizations before expanding to more generalizable architectures. This structured approach aims to elucidate the accelerator design space.

    +

    The progression begins with the most specialized option, ASICs purpose-built for AI, to ground our understanding in the maximum possible optimizations before expanding to more generalizable architectures. This structured approach elucidates the accelerator design space.

    @@ -947,7 +949,8 @@
    Maxim
    Specialized On-Chip Memory
    -

    ASICs incorporate on-chip SRAM and caches specifically optimized to feed data to the computational units. For example, Apple’s M1 system-on-a-chip contains special low-latency SRAM to accelerate the performance of its Neural Engine machine learning hardware. Large local memory with high bandwidth enables data to be kept close to the processing elements. This provides tremendous speed advantages compared to off-chip DRAM access, which can be up to 100x slower.

    +

    ASICs incorporate on-chip memory, such as SRAM (Static Random Access Memory), and caches that are specifically optimized to feed data to the computational units. SRAM is a type of memory that is faster and more reliable than DRAM (Dynamic Random Access Memory) because it does not need to be periodically refreshed. However, it requires more transistors per bit of data, making it take up more space and more expensive to produce as compared to DRAM.

    +

    SRAM is ideal for on-chip memory, where speed is critical. The advantage of having large amounts of high-bandwidth, on-chip SRAM is that data can be stored close to the processing elements, allowing for rapid access. This provides tremendous speed advantages compared to acessing off-chip DRAM, which, although larger in capacity, can be up to 100x slower. For example, Apple’s M1 system-on-a-chip contains special low-latency SRAM to accelerate the performance of its Neural Engine machine learning hardware.

    Data locality and optimizing memory hierarchy are crucial for high throughput and low power. Table 10.1 shows “Numbers Everyone Should Know,” from Jeff Dean.

    @@ -957,87 +960,71 @@
    Specialized On-
    ---++ - - - - - - - - + - - + - - + - - + - - + - - + - - + - - + - - +
    Operation LatencyNotes
    L1 cache reference 0.5 ns
    Branch mispredict 5 ns
    L2 cache reference 7 ns
    Mutex lock/unlock 25 ns
    Main memory reference 100 ns
    Compress 1K bytes with Zippy3,000 ns3 us3,000 ns (3 us)
    Send 1 KB bytes over 1 Gbps network10,000 ns10 us10,000 ns (10 us)
    Read 4 KB randomly from SSD150,000 ns150 us150,000 ns (150 us)
    Read 1 MB sequentially from memory250,000 ns250 us250,000 ns (250 us)
    Round trip within same datacenter500,000 ns0.5 ms500,000 ns (0.5 ms)
    Read 1 MB sequentially from SSD1,000,000 ns1 ms1,000,000 ns (1 ms)
    Disk seek10,000,000 ns10 ms10,000,000 ns (10 ms)
    Read 1 MB sequentially from disk20,000,000 ns20 ms20,000,000 ns (20 ms)
    Send packet CA → Netherlands → CA150,000,000 ns150 ms150,000,000 ns (150 ms)
    @@ -1047,7 +1034,7 @@
    Specialized On-
    Custom Datatypes and Operations
    -

    Unlike general-purpose processors, ASICs can be designed to natively support custom datatypes like INT4 or bfloat16, which are widely used in ML models. For instance, Nvidia’s Ampere GPU architecture has dedicated bfloat16 Tensor Cores to accelerate AI workloads. Low-precision datatypes enable higher arithmetic density and performance. ASICs can also directly incorporate non-standard operations common in ML algorithms as primitive operations - for example, natively supporting activation functions like ReLU makes execution more efficient. Please refer to the Efficient Numeric Representations chapter for additional details.

    +

    Unlike general-purpose processors, ASICs can be designed to natively support custom datatypes like INT4 or bfloat16, which are widely used in ML models. For instance, Nvidia’s Ampere GPU architecture has dedicated bfloat16 Tensor Cores to accelerate AI workloads. Low-precision datatypes enable higher arithmetic density and performance. Please refer to Section 8.6 for additional details. ASICs can also directly incorporate non-standard operations common in ML algorithms as primitive operations - for example, natively supporting activation functions like ReLU makes execution more efficient.

    High Parallelism
    @@ -1087,7 +1074,10 @@
    Comple

    10.3.2 Field-Programmable Gate Arrays (FPGAs)

    FPGAs are programmable integrated circuits that can be reconfigured for different applications. Their customizable nature provides advantages for accelerating AI algorithms compared to fixed ASICs or inflexible GPUs. While Google, Meta, and NVIDIA are considering putting ASICs in data centers, Microsoft deployed FPGAs in its data centers (Putnam et al. 2014) in 2011 to efficiently serve diverse data center workloads.

    -
    +
    +Xiong, Siyu, Guoqing Wu, Xitian Fan, Xuan Feng, Zhongcheng Huang, Wei Cao, Xuegong Zhou, et al. 2021. “MRI-Based Brain Tumor Segmentation Using FPGA-Accelerated Neural Network.” BMC Bioinf. 22 (1): 421. https://doi.org/10.1186/s12859-021-04347-6. +

    FPGAs have found widespread application in various fields, including medical imaging, robotics, and finance, where they excel in handling computationally intensive machine learning tasks. In medical imaging, an illustrative example is the application of FPGAs for brain tumor segmentation, a traditionally time-consuming and error-prone process. Compared to traditional GPU and CPU implementations, FPGAs have demonstrated over 5x and 44x performance improvements, respectively, and 11x and 82x gains in energy efficiency, highlighting their potential for demanding applications (Xiong et al. 2021).

    +

    Advantages

    FPGAs provide several benefits over GPUs and ASICs for accelerating machine learning workloads.

    @@ -1106,7 +1096,7 @@

    FPGAs comprise basic building blocks - configurable logic blocks, RAM blocks, and interconnects. Vendors provide a base amount of these resources, and engineers program the chips by compiling HDL code into bitstreams that rearrange the fabric into different configurations. This makes FPGAs adaptable as algorithms evolve.

    -

    While FPGAs may not achieve the utmost performance and efficiency of workload-specific ASICs, their programmability provides more flexibility as algorithms change. This adaptability makes FPGAs a compelling choice for accelerating evolving machine learning applications. Microsoft has deployed FPGAs in its Azure data centers for machine learning workloads to serve diverse applications instead of ASICs. The programmability enables optimization across changing ML models.

    +

    While FPGAs may not achieve the utmost performance and efficiency of workload-specific ASICs, their programmability provides more flexibility as algorithms change. This adaptability makes FPGAs a compelling choice for accelerating evolving machine learning applications.

    Customized Parallelism and Pipelining
    @@ -1118,14 +1108,14 @@
    Low Latency On-
    Native Support for Low Precision
    -

    A key advantage of FPGAs is the ability to natively implement any bit width for arithmetic units, such as INT4 or bfloat16, used in quantized ML models. For example, Intel’s Stratix 10 NX FPGAs have dedicated INT8 cores that can achieve up to 143 INT8 TOPS at ~1 TOPS/W Intel Stratix 10 NX FPGA. Lower bit widths increase arithmetic density and performance. FPGAs can even support mixed precision or dynamic precision tuning at runtime.

    +

    A key advantage of FPGAs is the ability to natively implement any bit width for arithmetic units, such as INT4 or bfloat16, used in quantized ML models. For example, Intel’s Stratix 10 NX FPGAs have dedicated INT8 cores that can achieve up to 143 INT8 TOPS (Tera Operations Per Second) at ~1 TOPS/W (Tera Operations Per Second per Watt) Intel Stratix 10 NX FPGA. TOPS is a measure of performance similar to FLOPS, but while FLOPS measures floating-point calculations, TOPS measures the number of integer operations a system can perform per second. Lower bit widths, like INT8 or INT4, increase arithmetic density and performance. FPGAs can even support mixed precision or dynamic precision tuning at runtime.

    -
    +

    Disadvantages

    Lower Peak Throughput than ASICs
    -

    FPGAs cannot match the raw throughput numbers of ASICs customized for a specific model and precision. The overheads of the reconfigurable fabric compared to fixed function hardware result in lower peak performance. For example, the TPU v5e pods allow up to 256 chips to be connected with more than 100 petaOps of INT8 performance, while FPGAs can offer up to 143 INT8 TOPS or 286 INT4 TOPS Intel Stratix 10 NX FPGA.

    +

    FPGAs cannot match the raw throughput numbers of ASICs customized for a specific model and precision. The overheads of the reconfigurable fabric compared to fixed function hardware result in lower peak performance. For example, the TPU v5e pods allow up to 256 chips to be connected with more than 100 petaOps (Peta Operations Per Second) of INT8 performance, while FPGAs can offer up to 143 INT8 TOPS or 286 INT4 TOPS Intel Stratix 10 NX FPGA. PetaOps represents quadrillions of operations per second, whereas TOPS measures trillions, highlighting the much greater throughput capability of TPU pods compared to FPGAs.

    This is because FPGAs comprise basic building blocks—configurable logic blocks, RAM blocks, and interconnects. Vendors provide a set amount of these resources. To program FPGAs, engineers write HDL code and compile it into bitstreams that rearrange the fabric, which has inherent overheads versus an ASIC purpose-built for one computation.

    @@ -1140,12 +1130,6 @@
    Reconfiguration
    Diminishing Gains on Advanced Nodes

    While smaller process nodes greatly benefit ASICs, they provide fewer advantages for FPGAs. At 7nm and below, effects like process variation, thermal constraints, and aging disproportionately impact FPGA performance. The overheads of the configurable fabric also diminish gains compared to fixed-function ASICs.

    -
    -
    Case Study
    -

    FPGAs have found widespread application in various fields, including medical imaging, robotics, and finance, where they excel in handling computationally intensive machine learning tasks. In medical imaging, an illustrative example is the application of FPGAs for brain tumor segmentation, a traditionally time-consuming and error-prone process. For instance, Xiong et al. developed a quantized segmentation accelerator, which they retrained using the BraTS19 and BraTS20 datasets. Their work yielded remarkable results, achieving over 5x and 44x performance improvements and 11x and 82x energy efficiency gains compared to GPU and CPU implementations, respectively (Xiong et al. 2021).

    -
    -Xiong, Siyu, Guoqing Wu, Xitian Fan, Xuan Feng, Zhongcheng Huang, Wei Cao, Xuegong Zhou, et al. 2021. “MRI-Based Brain Tumor Segmentation Using FPGA-Accelerated Neural Network.” BMC Bioinf. 22 (1): 421. https://doi.org/10.1186/s12859-021-04347-6. -
    @@ -1199,8 +1183,9 @@

    (Lindholm et al. 2008). As PC games became more sophisticated, NVIDIA GPUs became more programmable. Soon, users realized they could take advantage of this programmability, run various non-graphics-related workloads on GPUs, and benefit from the underlying architecture. And so, in the late 2000s, GPUs became general-purpose graphics processing units or GP-GPUs.

    Lindholm, Erik, John Nickolls, Stuart Oberman, and John Montrym. 2008. “NVIDIA Tesla: A Unified Graphics and Computing Architecture.” IEEE Micro 28 (2): 39–55. https://doi.org/10.1109/mm.2008.31. -

    Intel Arc Graphics and AMD Radeon RX have also developed their GPUs over time.

    -
    +

    Following this shift, other major players like Intel with its Arc Graphics and AMD with their Radeon RX series also evolved their GPUs to support a broader range of applications beyond traditional graphics rendering. This expansion of GPU capabilities opened up new possibilities, particularly in fields requiring massive computational power.

    +

    A striking example of this potential is the recent groundbreaking research conducted by OpenAI (Brown et al. 2020) with GPT-3, a language model with 175 billion parameters. Training such a massive model, which would have taken months on conventional CPUs, was completed in a matter of days using powerful GPUs, showcasing the transformative impact of GPUs in accelerating complex machine learning tasks.

    +

    Advantages

    High Computational Throughput
    @@ -1208,11 +1193,12 @@
    High Computa
    Raina, Rajat, Anand Madhavan, and Andrew Y. Ng. 2009. “Large-Scale Deep Unsupervised Learning Using Graphics Processors.” In Proceedings of the 26th Annual International Conference on Machine Learning, edited by Andrea Pohoreckyj Danyluk, Léon Bottou, and Michael L. Littman, 382:873–80. ACM International Conference Proceeding Series. ACM. https://doi.org/10.1145/1553374.1553486.

    This raw throughput stems from the highly parallel streaming multiprocessor (SM) architecture tailored for data-parallel workloads (Zhihao Jia, Zaharia, and Aiken 2019). Each SM contains hundreds of scalar cores optimized for float32/64 math. With thousands of SMs on a chip, GPUs are purpose-built for matrix multiplication and vector operations used throughout neural networks.

    -

    For example, Nvidia’s latest H100 GPU provides 4000 TFLOPs of FP8, 2000 TFLOPs of FP16, 1000 TFLOPs of TF32, 67 TFLOPs of FP32 and 34 TFLOPs of FP64 Compute performance, which can dramatically accelerate large batch training on models like BERT, GPT-3, and other transformer architectures. The scalable parallelism of GPUs is key to speeding up computationally intensive deep learning.

    +

    For example, Nvidia’s latest H100 GPU provides 4000 TFLOPs of FP8, 2000 TFLOPs of FP16, 1000 TFLOPs of TF32, 67 TFLOPs of FP32 and 34 TFLOPs of FP64 compute performance, which can dramatically accelerate large batch training on models like BERT, GPT-3, and other transformer architectures. The scalable parallelism of GPUs is key to speeding up computationally intensive deep learning.

    Mature Software Ecosystem
    -

    Nvidia provides extensive runtime libraries like cuDNN and cuBLAS that are highly optimized for deep learning primitives. Frameworks like TensorFlow and PyTorch integrate with these libraries to enable GPU acceleration without direct programming. CUDA provides lower-level control for custom computations.

    +

    Nvidia provides extensive runtime libraries like cuDNN and cuBLAS that are highly optimized for deep learning primitives. Frameworks like TensorFlow and PyTorch integrate with these libraries to enable GPU acceleration without direct programming. These libraries are built on top of CUDA, Nvidia’s parallel computing platform and programming model.

    +

    CUDA (Compute Unified Device Architecture) is the underlying framework that allows these high-level libraries to interact with the GPU’s hardware. It provides developers with low-level access to the GPU’s resources, enabling custom computations and optimizations that fully leverage the GPU’s parallel processing capabilities. By using CUDA, developers can write software that exploits the GPU’s architecture for high-performance computing tasks.

    This ecosystem enables quick leveraging of GPUs via high-level Python without GPU programming expertise. Known workflows and abstractions provide a convenient on-ramp for scaling up deep learning experiments. The software maturity supplements the throughput advantages.

    @@ -1236,7 +1222,7 @@
    Less Effi
    High Memory Bandwidth Needs
    -

    The massively parallel architecture requires tremendous memory bandwidth to supply thousands of cores, as shown in Figure 1. For example, the Nvidia A100 GPU requires 1.6TB/sec to fully saturate its computer. GPUs rely on wide 384-bit memory buses to high-bandwidth GDDR6 RAM, but even the fastest GDDR6 tops out at around 1 TB/sec. This dependence on external DRAM incurs latency and power overheads.

    +

    The massively parallel architecture requires tremendous memory bandwidth to supply thousands of cores. For example, the Nvidia A100 GPU requires 1.6TB/sec to fully saturate its computer. GPUs rely on wide 384-bit memory buses to high-bandwidth GDDR6 RAM, but even the fastest GDDR6 tops out at around 1 TB/sec. This dependence on external DRAM incurs latency and power overheads.

    Programming Complexity
    @@ -1253,14 +1239,10 @@
    Fixed Architecture

    Unlike FPGAs, the fundamental GPU architecture cannot be altered post-manufacture. This constraint limits adapting to novel ML workloads or layers. The CPU-GPU boundary also creates data movement overheads.

    -
    -

    Case Study

    -

    The recent groundbreaking research conducted by OpenAI (Brown et al. 2020) with their GPT-3 model. GPT-3, a language model with 175 billion parameters, demonstrated unprecedented language understanding and generation capabilities. Its training, which would have taken months on conventional CPUs, was accomplished in a matter of days using powerful GPUs, thus pushing the boundaries of natural language processing (NLP) capabilities.

    -

    10.3.5 Central Processing Units (CPUs)

    -

    The term CPUs has a long history that dates back to 1955 (Weik 1955) while the first microprocessor CPU-the Intel 4004-was invented in 1971 (Who Invented the Microprocessor?). Compilers compile high-level programming languages like Python, Java, or C to assemble instructions (x86, ARM, RISC-V, etc.) for CPUs to process. The set of instructions a CPU understands is called the “instruction set architecture” (ISA), which defines the commands that the processor can execute directly. It must be agreed upon by both the hardware and software running atop it (See section 5 for a more in-depth description of instruction set architectures-ISAs).

    +

    The term CPUs has a long history that dates back to 1955 (Weik 1955) while the first microprocessor CPU-the Intel 4004-was invented in 1971 (Who Invented the Microprocessor?). Compilers compile high-level programming languages like Python, Java, or C to assemble instructions (x86, ARM, RISC-V, etc.) for CPUs to process. The set of instructions a CPU understands is called the “instruction set architecture” (ISA), which defines the commands that the processor can execute directly. It must be agreed upon by both the hardware and software running atop it.

    Weik, Martin H. 1955. A Survey of Domestic Electronic Digital Computing Systems. Ballistic Research Laboratories.

    An overview of significant developments in CPUs:

    @@ -1725,7 +1707,7 @@

    <

    Examples include memristors for in-memory computing and nanophotonics for integrated photonic communication. Together, these technologies offer the potential for orders of magnitude improvements in speed, efficiency, and scalability compared to current AI hardware. We will examine these in this section.

    10.8.1 Integration Methods

    -

    Integration methods refer to the approaches used to combine and interconnect an AI chip or system’s various computational and memory components. By closely linking the key processing elements, integration aims to maximize performance, power efficiency, and density.

    +

    Integration methods refer to the approaches used to combine and interconnect an AI chip or system’s various computational and memory components. By closely linking the key processing elements, integration tries to maximize performance, power efficiency, and density.

    In the past, AI computing was primarily performed on CPUs and GPUs built using conventional integration methods. These discrete components were manufactured separately and connected together on a board. However, this loose integration creates bottlenecks, such as data transfer overheads.

    As AI workloads have grown, there is increasing demand for tighter integration between computing, memory, and communication elements. Some key drivers of integration include:

      @@ -1869,7 +1851,7 @@

      “The Next Generation of Deep Learning Hardware: Analog Computing.” Proc. IEEE 107 (1): 108–22. https://doi.org/10.1109/jproc.2018.2871057.
      Hazan, Avi, and Elishai Ezra Tsur. 2021. “Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation.” Front. Neurosci. 15 (February): 627221. https://doi.org/10.3389/fnins.2021.627221. -

      Neuromorphic computing, which aims to emulate biological neural systems for efficient ML inference, can use analog circuits to implement the key components and behaviors of brains. For example, researchers have designed analog circuits to model neurons and synapses using capacitors, transistors, and operational amplifiers (Hazan and Ezra Tsur 2021). The capacitors can exhibit the spiking dynamics of biological neurons, while the amplifiers and transistors provide a weighted summation of inputs to mimic dendrites. Variable resistor technologies like memristors can realize analog synapses with spike-timing-dependent plasticity, which can strengthen or weaken connections based on spiking activity.

      +

      Neuromorphic computing, which emulates biological neural systems for efficient ML inference, can use analog circuits to implement the key components and behaviors of brains. For example, researchers have designed analog circuits to model neurons and synapses using capacitors, transistors, and operational amplifiers (Hazan and Ezra Tsur 2021). The capacitors can exhibit the spiking dynamics of biological neurons, while the amplifiers and transistors provide a weighted summation of inputs to mimic dendrites. Variable resistor technologies like memristors can realize analog synapses with spike-timing-dependent plasticity, which can strengthen or weaken connections based on spiking activity.

      Startups like SynSense have developed analog neuromorphic chips containing these biomimetic components (Bains 2020). This analog approach results in low power consumption and high scalability for edge devices versus complex digital SNN implementations.

      Bains, Sunny. 2020. “The Business of Building Brains.” Nature Electronics 3 (7): 348–51. https://doi.org/10.1038/s41928-020-0449-1. diff --git a/contents/introduction/introduction.html b/contents/introduction/introduction.html index 16e7ec98..5ec5316a 100644 --- a/contents/introduction/introduction.html +++ b/contents/introduction/introduction.html @@ -707,7 +707,7 @@

      1.1 Overview

      -

      In the early 1990s, Mark Weiser, a pioneering computer scientist, introduced the world to a revolutionary concept that would forever change how we interact with technology. He envisioned a future where computing would be seamlessly integrated into our environments, becoming an invisible, integral part of daily life. This vision, which he termed “ubiquitous computing,” promised a world where technology would serve us without demanding our constant attention or interaction. Fast forward to today, and we find ourselves on the cusp of realizing Weiser’s vision, thanks to the advent and proliferation of machine learning systems.

      +

      In the early 1990s, Mark Weiser, a pioneering computer scientist, introduced the world to a revolutionary concept that would forever change how we interact with technology. This was succintly captured in the paper he wrote on “The Computer for the 21st Century” (Figure 1.1). He envisioned a future where computing would be seamlessly integrated into our environments, becoming an invisible, integral part of daily life. This vision, which he termed “ubiquitous computing,” promised a world where technology would serve us without demanding our constant attention or interaction. Fast forward to today, and we find ourselves on the cusp of realizing Weiser’s vision, thanks to the advent and proliferation of machine learning systems.

      diff --git a/contents/labs/arduino/nicla_vision/image_classification/image_classification.html b/contents/labs/arduino/nicla_vision/image_classification/image_classification.html index 28d48d40..45743988 100644 --- a/contents/labs/arduino/nicla_vision/image_classification/image_classification.html +++ b/contents/labs/arduino/nicla_vision/image_classification/image_classification.html @@ -745,7 +745,7 @@

      Introduction

      Computer Vision

      -

      At its core, computer vision aims to enable machines to interpret and make decisions based on visual data from the world, essentially mimicking the capability of the human optical system. Conversely, AI is a broader field encompassing machine learning, natural language processing, and robotics, among other technologies. When you bring AI algorithms into computer vision projects, you supercharge the system’s ability to understand, interpret, and react to visual stimuli.

      +

      At its core, computer vision enables machines to interpret and make decisions based on visual data from the world, essentially mimicking the capability of the human optical system. Conversely, AI is a broader field encompassing machine learning, natural language processing, and robotics, among other technologies. When you bring AI algorithms into computer vision projects, you supercharge the system’s ability to understand, interpret, and react to visual stimuli.

      When discussing Computer Vision projects applied to embedded devices, the most common applications that come to mind are Image Classification and Object Detection.

      Both models can be implemented on tiny devices like the Arduino Nicla Vision and used on real projects. In this chapter, we will cover Image Classification.

      diff --git a/contents/labs/shared/kws_feature_eng/kws_feature_eng.html b/contents/labs/shared/kws_feature_eng/kws_feature_eng.html index e19ca7a4..cebfa228 100644 --- a/contents/labs/shared/kws_feature_eng/kws_feature_eng.html +++ b/contents/labs/shared/kws_feature_eng/kws_feature_eng.html @@ -734,7 +734,7 @@

      Introduction

      The KWS

      -

      The most common TinyML application is Keyword Spotting (KWS), a subset of the broader field of speech recognition. While general speech recognition aims to transcribe all spoken words into text, Keyword Spotting focuses on detecting specific “keywords” or “wake words” in a continuous audio stream. The system is trained to recognize these keywords as predefined phrases or words, such as yes or no. In short, KWS is a specialized form of speech recognition with its own set of challenges and requirements.

      +

      The most common TinyML application is Keyword Spotting (KWS), a subset of the broader field of speech recognition. While general speech recognition transcribes all spoken words into text, Keyword Spotting focuses on detecting specific “keywords” or “wake words” in a continuous audio stream. The system is trained to recognize these keywords as predefined phrases or words, such as yes or no. In short, KWS is a specialized form of speech recognition with its own set of challenges and requirements.

      Here a typical KWS Process using MFCC Feature Converter:

      diff --git a/contents/ml_systems/ml_systems.html b/contents/ml_systems/ml_systems.html index e2433b68..658a8d41 100644 --- a/contents/ml_systems/ml_systems.html +++ b/contents/ml_systems/ml_systems.html @@ -845,7 +845,7 @@

      2.3.1 Characteristics

      Definition of Edge ML

      -

      Edge Machine Learning (Edge ML) runs machine learning algorithms directly on endpoint devices or closer to where the data is generated rather than relying on centralized cloud servers. This approach aims to bring computation closer to the data source, reducing the need to send large volumes of data over networks, often resulting in lower latency and improved data privacy.

      +

      Edge Machine Learning (Edge ML) runs machine learning algorithms directly on endpoint devices or closer to where the data is generated rather than relying on centralized cloud servers. This approach brings computation closer to the data source, reducing the need to send large volumes of data over networks, often resulting in lower latency and improved data privacy.

      Decentralized Data Processing

      In Edge ML, data processing happens in a decentralized fashion. Instead of sending data to remote servers, the data is processed locally on devices like smartphones, tablets, or Internet of Things (IoT) devices (Figure 2.4). This local processing allows devices to make quick decisions based on the data they collect without relying heavily on a central server’s resources. This decentralization is particularly important in real-time applications where even a slight delay can have significant consequences.

      Local Data Storage and Computation

      @@ -962,7 +962,7 @@

      2.5 Comparison

      -

      Up to this point, we’ve explored each of the different ML variants individually. Now, let’s bring them all together for a comprehensive view. Table 2.1 offers a comparative analysis of Cloud ML, Edge ML, and TinyML based on various features and aspects. This comparison aims to provide a clear perspective on the unique advantages and distinguishing factors, aiding in making informed decisions based on the specific needs and constraints of a given application or project.

      +

      Up to this point, we’ve explored each of the different ML variants individually. Now, let’s bring them all together for a comprehensive view. Table 2.1 offers a comparative analysis of Cloud ML, Edge ML, and TinyML based on various features and aspects. This comparison provides a clear perspective on the unique advantages and distinguishing factors, aiding in making informed decisions based on the specific needs and constraints of a given application or project.

      diff --git a/contents/ondevice_learning/ondevice_learning.html b/contents/ondevice_learning/ondevice_learning.html index 183c80c8..ab806b2a 100644 --- a/contents/ondevice_learning/ondevice_learning.html +++ b/contents/ondevice_learning/ondevice_learning.html @@ -996,7 +996,7 @@

      Quantization-Aw

      -

      However, the quantization process can also introduce quantization errors that can degrade the model’s performance. Quantization-aware scaling is a technique that aims to minimize these errors by adjusting the scale factors used in the quantization process.

      +

      However, the quantization process can also introduce quantization errors that can degrade the model’s performance. Quantization-aware scaling is a technique that minimizes these errors by adjusting the scale factors used in the quantization process.

      The QAS process involves two main steps:

      • Quantization-aware training: In this step, the neural network is trained with quantization in mind, simulating it to mimic its effects during forward and backward passes. This allows the model to learn to compensate for the quantization errors and improve its performance on low-precision hardware. Refer to the QAT section in Model Optimizations for details.

        • @@ -1452,7 +1452,7 @@

        Such biases could have dangerous real-world impacts. Rigorous data validation, anomaly detection, and tracking of data provenance are critical defensive measures. Adopting frameworks like Five Safes ensures models are trained on high-quality, representative data (Desai et al. 2016).

        Desai, Tanvi, Felix Ritchie, Richard Welpton, et al. 2016. “Five Safes: Designing Data Access for Research.” Economics Working Paper Series 1601: 28. -

        Data poisoning is a pressing concern for secure on-device learning since data at the endpoint cannot be easily monitored in real-time. If models are allowed to adapt on their own, then we run the risk of the device acting maliciously. However, continued research in adversarial ML aims to develop robust solutions to detect and mitigate such data attacks.

        +

      Data poisoning is a pressing concern for secure on-device learning since data at the endpoint cannot be easily monitored in real-time. If models are allowed to adapt on their own, then we run the risk of the device acting maliciously. However, continued research in adversarial ML is needed to develop robust solutions to detect and mitigate such data attacks.

    12.6.2 Adversarial Attacks

    diff --git a/contents/ops/ops.html b/contents/ops/ops.html index 1d275571..1a617ebb 100644 --- a/contents/ops/ops.html +++ b/contents/ops/ops.html @@ -863,12 +863,12 @@

    Patrick Debois, a consultant and Agile practitioner. Debois organized the first DevOpsDays conference in Ghent, Belgium, in 2009. The conference brought together development and operations professionals to discuss ways to improve collaboration and automate processes.

    DevOps has its roots in the Agile movement, which began in the early 2000s. Agile provided the foundation for a more collaborative approach to software development and emphasized small, iterative releases. However, Agile primarily focuses on collaboration between development teams. As Agile methodologies became more popular, organizations realized the need to extend this collaboration to operations teams.

    The siloed nature of development and operations teams often led to inefficiencies, conflicts, and delays in software delivery. This need for better collaboration and integration between these teams led to the DevOps movement. DevOps can be seen as an extension of the Agile principles, including operations teams.

    -

    The key principles of DevOps include collaboration, automation, continuous integration, delivery, and feedback. DevOps focuses on automating the entire software delivery pipeline, from development to deployment. It aims to improve the collaboration between development and operations teams, utilizing tools like Jenkins, Docker, and Kubernetes to streamline the development lifecycle.

    +

    The key principles of DevOps include collaboration, automation, continuous integration, delivery, and feedback. DevOps focuses on automating the entire software delivery pipeline, from development to deployment. It improves the collaboration between development and operations teams, utilizing tools like Jenkins, Docker, and Kubernetes to streamline the development lifecycle.

    While Agile and DevOps share common principles around collaboration and feedback, DevOps specifically targets integrating development and IT operations - expanding Agile beyond just development teams. It introduces practices and tools to automate software delivery and improve the speed and quality of software releases.

    13.2.2 MLOps

    -

    MLOps, on the other hand, stands for Machine Learning Operations, and it extends the principles of DevOps to the ML lifecycle. MLOps aims to automate and streamline the end-to-end ML lifecycle, from data preparation and model development to deployment and monitoring. The main focus of MLOps is to facilitate collaboration between data scientists, data engineers, and IT operations and to automate the deployment, monitoring, and management of ML models. Some key factors led to the rise of MLOps.

    +

    MLOps, on the other hand, stands for Machine Learning Operations, and it extends the principles of DevOps to the ML lifecycle. MLOps automates and streamlines the end-to-end ML lifecycle, from data preparation and model development to deployment and monitoring. The main focus of MLOps is to facilitate collaboration between data scientists, data engineers, and IT operations and to automate the deployment, monitoring, and management of ML models. Some key factors led to the rise of MLOps.

    • Data drift: Data drift degrades model performance over time, motivating the need for rigorous monitoring and automated retraining procedures provided by MLOps.
    • Reproducibility: The lack of reproducibility in machine learning experiments motivated MLOps systems to track code, data, and environment variables to enable reproducible ML workflows.
      • @@ -1584,7 +1584,7 @@

      “Edge Impulse: An MLOps Platform for Tiny Machine Learning.” Proceedings of Machine Learning and Systems 5.

      Edge Impulse

      -

      Edge Impulse is an end-to-end development platform for creating and deploying machine learning models onto edge devices such as microcontrollers and small processors. It aims to make embedded machine learning more accessible to software developers through its easy-to-use web interface and integrated tools for data collection, model development, optimization, and deployment. Its key capabilities include the following:

      +

      Edge Impulse is an end-to-end development platform for creating and deploying machine learning models onto edge devices such as microcontrollers and small processors. It makes embedded machine learning more accessible to software developers through its easy-to-use web interface and integrated tools for data collection, model development, optimization, and deployment. Its key capabilities include the following:

      • Intuitive drag-and-drop workflow for building ML models without coding required
      • Tools for acquiring, labeling, visualizing, and preprocessing data from sensors
        • @@ -1619,7 +1619,7 @@
        Optimizations
        Use Cases

        Beyond the accessibility of the platform itself, the Edge Impulse team has expanded the knowledge base of the embedded ML ecosystem. The platform lends itself to academic environments, having been used in online courses and on-site workshops globally. Numerous case studies featuring industry and research use cases have been published, most notably Oura Ring, which uses ML to identify sleep patterns. The team has made repositories open source on GitHub, facilitating community growth. Users can also make projects public to share techniques and download libraries to share via Apache. Organization-level access enables collaboration on workflows.

        -

        Overall, Edge Impulse is uniquely comprehensive and integrateable for developer workflows. Larger platforms like Google and Microsoft focus more on cloud versus embedded systems. TinyMLOps frameworks such as Neuton AI and Latent AI offer some functionality but lack Edge Impulse’s end-to-end capabilities. TensorFlow Lite Micro is the standard inference engine due to flexibility, open source status, and TensorFlow integration, but it uses more memory and storage than Edge Impulse’s EON Compiler. Other platforms need to be updated, academic-focused, or more versatile. In summary, Edge Impulse aims to streamline and scale embedded ML through an accessible, automated platform.

        +

        Overall, Edge Impulse is uniquely comprehensive and integrateable for developer workflows. Larger platforms like Google and Microsoft focus more on cloud versus embedded systems. TinyMLOps frameworks such as Neuton AI and Latent AI offer some functionality but lack Edge Impulse’s end-to-end capabilities. TensorFlow Lite Micro is the standard inference engine due to flexibility, open source status, and TensorFlow integration, but it uses more memory and storage than Edge Impulse’s EON Compiler. Other platforms need to be updated, academic-focused, or more versatile. In summary, Edge Impulse streamlines and scale embedded ML through an accessible, automated platform.

      @@ -1676,14 +1676,14 @@

      Traditional MLOps frameworks are insufficient for integrating continuous therapeutic monitoring (CTM) and AI in clinical settings for a few key reasons:

      • MLOps focuses on the ML model lifecycle—training, deployment, monitoring. But healthcare involves coordinating multiple human stakeholders—patients and clinicians—not just models.

        • -

      • MLOps aims to automate IT system monitoring and management. However, optimizing patient health requires personalized care and human oversight, not just automation.

        • +

      • MLOps automates IT system monitoring and management. However, optimizing patient health requires personalized care and human oversight, not just automation.

      • CTM and healthcare delivery are complex sociotechnical systems with many moving parts. MLOps doesn’t provide a framework for coordinating human and AI decision-making.

      • Ethical considerations regarding healthcare AI require human judgment, oversight, and accountability. MLOps frameworks lack processes for ethical oversight.

      • Patient health data is highly sensitive and regulated. MLOps alone doesn’t ensure the handling of protected health information to privacy and regulatory standards.

      • Clinical validation of AI-guided treatment plans is essential for provider adoption. MLOps doesn’t incorporate domain-specific evaluation of model recommendations.

      • Optimizing healthcare metrics like patient outcomes requires aligning stakeholder incentives and workflows, which pure tech-focused MLOps overlooks.

      -

      Thus, effectively integrating AI/ML and CTM in clinical practice requires more than just model and data pipelines; it requires coordinating complex human-AI collaborative decision-making, which ClinAIOps aims to address via its multi-stakeholder feedback loops.

      +

      Thus, effectively integrating AI/ML and CTM in clinical practice requires more than just model and data pipelines; it requires coordinating complex human-AI collaborative decision-making, which ClinAIOps addresses via its multi-stakeholder feedback loops.

      Feedback Loops

      The ClinAIOps framework, shown in Figure 13.8, provides these mechanisms through three feedback loops. The loops are useful for coordinating the insights from continuous physiological monitoring, clinician expertise, and AI guidance via feedback loops, enabling data-driven precision medicine while maintaining human accountability. ClinAIOps provides a model for effective human-AI symbiosis in healthcare: the patient is at the center, providing health challenges and goals that inform the therapy regimen; the clinician oversees this regimen, giving inputs for adjustments based on continuous monitoring data and health reports from the patient; whereas AI developers play a crucial role by creating systems that generate alerts for therapy updates, which the clinician then vets.

      @@ -1762,7 +1762,7 @@

      MLOps vs. ClinAIO
    • Liability for treatment outcomes must be clarified with just an ML model. ClinAIOps maintains human accountability.
    • Health systems would need to demonstrate value to change workflows. ClinAIOps aligns stakeholders.

    -

    The hypertension case clearly shows the need to look beyond training and deploying a performant ML model to consider the entire human-AI sociotechnical system. This is the key gap ClinAIOps aims to address over traditional MLOps. Traditional MLOps is overly tech-focused on automating ML model development and deployment, while ClinAIOps incorporates clinical context and human-AI coordination through multi-stakeholder feedback loops.

    +

    The hypertension case clearly shows the need to look beyond training and deploying a performant ML model to consider the entire human-AI sociotechnical system. This is the key gap ClinAIOps addresses over traditional MLOps. Traditional MLOps is overly tech-focused on automating ML model development and deployment, while ClinAIOps incorporates clinical context and human-AI coordination through multi-stakeholder feedback loops.

    Table 13.3 compares them. This table highlights how, when MLOps is implemented, we need to consider more than just ML models.

    diff --git a/contents/optimizations/optimizations.html b/contents/optimizations/optimizations.html index 96726a8b..7c7c29b1 100644 --- a/contents/optimizations/optimizations.html +++ b/contents/optimizations/optimizations.html @@ -870,7 +870,7 @@

    9.2.1 Pruning

    Overview

    -

    Model pruning is a technique in machine learning that aims to reduce the size and complexity of a neural network model while maintaining its predictive capabilities as much as possible. The goal of model pruning is to remove redundant or non-essential components of the model, including connections between neurons, individual neurons, or even entire layers of the network.

    +

    Model pruning is a technique in machine learning that reduces the size and complexity of a neural network model while maintaining its predictive capabilities as much as possible. The goal of model pruning is to remove redundant or non-essential components of the model, including connections between neurons, individual neurons, or even entire layers of the network.

    This process typically involves analyzing the machine learning model to identify and remove weights, nodes, or layers that have little impact on the model’s outputs. By selectively pruning a model in this way, the total number of parameters can be reduced significantly without substantial declines in model accuracy. The resulting compressed model requires less memory and computational resources to train and run while enabling faster inference times.

    Model pruning is especially useful when deploying machine learning models to devices with limited compute resources, such as mobile phones or TinyML systems. The technique facilitates the deployment of larger, more complex models on these devices by reducing their resource demands. Additionally, smaller models require less data to generalize well and are less prone to overfitting. By providing an efficient way to simplify models, model pruning has become a vital technique for optimizing neural networks in machine learning.

    There are several common pruning techniques used in machine learning, these include structured pruning, unstructured pruning, iterative pruning, bayesian pruning, and even random pruning. In addition to pruning the weights, one can also prune the activations. Activation pruning specifically targets neurons or filters that activate rarely or have overall low activation. There are numerous other methods, such as sensitivity and movement pruning. For a comprehensive list of methods, the reader is encouraged to read the following paper: “A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations” (2023).

    @@ -1325,7 +1325,7 @@

    Numeric Encod

    Numeric encoding, the art of transmuting numbers into a computer-amenable format, and their subsequent storage are critical for computational efficiency. For instance, floating-point numbers might be encoded using the IEEE 754 standard, which apportions bits among sign, exponent, and fraction components, thereby enabling the representation of a vast array of values with a single format. There are a few new IEEE floating point formats that have been defined specifically for AI workloads:

    • bfloat16- A 16-bit floating point format introduced by Google. It has 8 bits for exponent, 7 bits for mantissa and 1 bit for sign. Offers a reduced precision compromise between 32-bit float and 8-bit integers. Supported on many hardware accelerators.
      • -
    • posit - A configurable format that can represent different levels of precision based on exponent bits. Aims to be more efficient than IEEE 754 binary floats. Has adjustable dynamic range and precision.
      • +
    • posit - A configurable format that can represent different levels of precision based on exponent bits. It is more efficient than IEEE 754 binary floats. Has adjustable dynamic range and precision.
    • Flexpoint - A format introduced by Intel that can dynamically adjust precision across layers or within a layer. Allows tuning precision to accuracy and hardware requirements.
    • BF16ALT - A proposed 16-bit format by ARM as an alternative to bfloat16. Uses additional bit in exponent to prevent overflow/underflow.
    • TF32 - Introduced by Nvidia for Ampere GPUs. Uses 10 bits for exponent instead of 8 bits like FP32. Improves model training performance while maintaining accuracy.
      • @@ -1533,7 +1533,7 @@

      Stochastic Quantiz

    Zero Shot Quantization

    -

    Zero-shot quantization refers to the process of converting a full-precision deep learning model directly into a low-precision, quantized model without the need for any retraining or fine-tuning on the quantized model. The primary advantage of this approach is its efficiency, as it eliminates the often time-consuming and resource-intensive process of retraining a model post-quantization. By leveraging techniques that anticipate and minimize quantization errors, zero-shot quantization aims to maintain the model’s original accuracy even after reducing its numerical precision. It is particularly useful for Machine Learning as a Service (MLaaS) providers aiming to expedite the deployment of their customer’s workloads without having to access their datasets.

    +

    Zero-shot quantization refers to the process of converting a full-precision deep learning model directly into a low-precision, quantized model without the need for any retraining or fine-tuning on the quantized model. The primary advantage of this approach is its efficiency, as it eliminates the often time-consuming and resource-intensive process of retraining a model post-quantization. By leveraging techniques that anticipate and minimize quantization errors, zero-shot quantization maintains the model’s original accuracy even after reducing its numerical precision. It is particularly useful for Machine Learning as a Service (MLaaS) providers aiming to expedite the deployment of their customer’s workloads without having to access their datasets.

    diff --git a/contents/privacy_security/privacy_security.html b/contents/privacy_security/privacy_security.html index da01f0cd..a21382c0 100644 --- a/contents/privacy_security/privacy_security.html +++ b/contents/privacy_security/privacy_security.html @@ -1000,7 +1000,7 @@
    Steali Oliynyk, Daryna, Rudolf Mayer, and Andreas Rauber. 2023. “I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences.” ACM Comput. Surv. 55 (14s): 1–41. https://doi.org/10.1145/3595292.

    • Level of Effectiveness: Attackers aim to replicate the model’s decision-making capabilities rather than focus on the precise parameter values. This is done through understanding the overall behavior of the model. Consider a scenario where an attacker wants to copy the behavior of an image classification model. By analyzing the model’s decision boundaries, the attack tunes its model to reach an effectiveness comparable to the original model. This could entail analyzing 1) the confusion matrix to understand the balance of prediction metrics (true positive, true negative, false positive, false negative) and 2) other performance metrics, such as F1 score and precision, to ensure that the two models are comparable.

      • -

    • Prediction Consistency: The attacker tries to align their model’s prediction patterns with the target model’s. This involves matching prediction outputs (both positive and negative) on the same set of inputs and ensuring distributional consistency across different classes. For instance, consider a natural language processing (NLP) model that generates sentiment analysis for move reviews (labels reviews as positive, neutral, or negative). The attacker will try to fine-tune their model to match the prediction of the original models on the same set of movie reviews. This includes ensuring that the model makes the same mistakes (mispredictions) that the targeted model makes.

      • +

    • Prediction Consistency: The attacker tries to align their model’s prediction patterns with the target model’s. This involves matching prediction outputs (both positive and negative) on the same set of inputs and ensuring distributional consistency across different classes. For instance, consider a natural language processing (NLP) model that generates sentiment analysis for movie reviews (labels reviews as positive, neutral, or negative). The attacker will try to fine-tune their model to match the prediction of the original models on the same set of movie reviews. This includes ensuring that the model makes the same mistakes (mispredictions) that the targeted model makes.

    @@ -1212,7 +1212,7 @@

    (Barenghi et al. 2010), power spikes (Hutter, Schmidt, and Plos 2009), clock glitches (Amiel, Clavier, and Tunstall 2006), electromagnetic pulses (Agrawal et al. 2007), temperate increase (S. Skorobogatov 2009) and laser strikes (S. P. Skorobogatov and Anderson 2003) are common hardware attack vectors. They are precisely timed to induce faults like flipped bits or skipped instructions during critical operations.

    For ML systems, consequences include impaired model accuracy, denial of service, extraction of private training data or model parameters, and reverse engineering of model architectures. Attackers could use fault injection to force misclassifications, disrupt autonomous systems, or steal intellectual property.

    -

    For example, in (Breier et al. 2018), the authors successfully injected a fault attack into a deep neural network deployed on a microcontroller. They used a laser to heat specific transistors, forcing them to switch states. In one instance, they used this method to attack a ReLU activation function, resulting in the function always outputting a value of 0, regardless of the input. In the assembly code in Figure 14.2, the attack caused the executing program always to skip the jmp end instruction on line 6. This means that HiddenLayerOutput[i] is always set to 0, overwriting any values written to it on lines 4 and 5. As a result, the targeted neurons are rendered inactive, resulting in misclassifications.

    +

    For example, in (Breier et al. 2018), the authors successfully injected a fault attack into a deep neural network deployed on a microcontroller. They used a laser to heat specific transistors, forcing them to switch states. In one instance, they used this method to attack a ReLU activation function, resulting in the function always outputting a value of 0, regardless of the input. In the assembly code in Figure 14.2, the attack caused the executing program always to skip the jmp end instruction on line 6. This means that HiddenLayerOutput[i] is always set to 0, overwriting any values written to it on lines 4 and 5. As a result, the targeted neurons are rendered inactive, resulting in misclassifications.

    @@ -1316,7 +1316,7 @@

    Baby Monitors: Many WiFi-enabled baby monitors have been found to have unsecured interfaces for remote access. This allowed attackers to gain live audio and video feeds from people’s homes, representing a major privacy violation.

  • Pacemakers: Interface vulnerabilities were discovered in some pacemakers that could allow attackers to manipulate cardiac functions if exploited. This presents a potentially life-threatening scenario.

  • Smart Lightbulbs: A researcher found he could access unencrypted data from smart lightbulbs via a debug interface, including WiFi credentials, allowing him to gain access to the connected network (Greengard 2015).

    • -

  • Smart Cars: If left unsecured, The OBD-II diagnostic port has been shown to provide an attack vector into automotive systems. Researchers could use it to control brakes and other components (Miller and Valasek 2015).

    • +

  • Smart Cars: If left unsecured, The OBD-II diagnostic port has been shown to provide an attack vector into automotive systems. Attackers could use it to control brakes and other components (Miller and Valasek 2015).

    • Greengard, Samuel. 2015. The Internet of Things. The MIT Press. https://doi.org/10.7551/mitpress/10277.001.0001. @@ -1627,7 +1627,7 @@

    14.7.6 Privacy Concerns in Machine Learning

    Generative AI

    -

    Privacy and security concerns have also risen with the public use of generative AI models, including OpenAI’s GPT4 and other LLMs. ChatGPT, in particular, has been discussed more recently about Privacy, given all the personal information collected from ChatGPT users. In June, a class action lawsuit was filed against ChatGPT due to concerns that it was trained on proprietary medical and personal information without proper permissions or consent. As a result of these privacy concerns, many companies have prohibited their employees from accessing ChatGPT, and uploading private, company related information to the chatbot. Further, ChatGPT is susceptible to prompt injection and other security attacks that could compromise the privacy of the proprietary data upon which it was trained.

    +

    Privacy and security concerns have also risen with the public use of generative AI models, including OpenAI’s GPT4 and other LLMs. ChatGPT, in particular, has been discussed more recently about Privacy, given all the personal information collected from ChatGPT users. In June 2023, a class action lawsuit was filed against ChatGPT due to concerns that it was trained on proprietary medical and personal information without proper permissions or consent. As a result of these privacy concerns, many companies have prohibited their employees from accessing ChatGPT, and uploading private, company related information to the chatbot. Further, ChatGPT is susceptible to prompt injection and other security attacks that could compromise the privacy of the proprietary data upon which it was trained.

    Case Study

    While ChatGPT has instituted protections to prevent people from accessing private and ethically questionable information, several individuals have successfully bypassed these protections through prompt injection and other security attacks. As demonstrated in Figure 14.9, users can bypass ChatGPT protections to mimic the tone of a “deceased grandmother” to learn how to bypass a web application firewall (Gupta et al. 2023).

    @@ -1741,7 +1741,7 @@

    Core Idea

    Federated Learning (FL) is a type of machine learning in which a model is built and distributed across multiple devices or servers while keeping the training data localized. It was previously discussed in the Model Optimizations chapter. Still, we will recap it here briefly to complete it and focus on things that pertain to this chapter.

    -

    FL aims to train machine learning models across decentralized networks of devices or systems while keeping all training data localized. Figure 14.12 illustrates this process: each participating device leverages its local data to calculate model updates, which are then aggregated to build an improved global model. However, the raw training data is never directly shared, transferred, or compiled. This privacy-preserving approach allows for the joint development of ML models without centralizing the potentially sensitive training data in one place.

    +

    FL trains machine learning models across decentralized networks of devices or systems while keeping all training data localized. Figure 14.12 illustrates this process: each participating device leverages its local data to calculate model updates, which are then aggregated to build an improved global model. However, the raw training data is never directly shared, transferred, or compiled. This privacy-preserving approach allows for the joint development of ML models without centralizing the potentially sensitive training data in one place.

    @@ -1806,7 +1806,7 @@

    Core Idea

    Case Study

    -

    Some researchers demonstrate a real-life example of machine unlearning approaches applied to SOTA machine learning models through training an LLM, LLaMA2-7b, to unlearn any references to Harry Potter (Eldan and Russinovich 2023). Though this model took 184K GPU hours to pre-train, it only took 1 GPU hour of fine-tuning to erase the model’s ability to generate or recall Harry Potter-related content without noticeably compromising the accuracy of generating content unrelated to Harry Potter. Figure 14.13 demonstrates how the model output changes before (Llama-7b-chat-hf column) and after (Finetuned Llama-b column) unlearning has occurred.

    +

    Some researchers have demonstrated a real-life example of machine unlearning approaches applied to SOTA machine learning models through training an LLM, LLaMA2-7b, to unlearn any references to Harry Potter (Eldan and Russinovich 2023). Though this model took 184K GPU hours to pre-train, it only took 1 GPU hour of fine-tuning to erase the model’s ability to generate or recall Harry Potter-related content without noticeably compromising the accuracy of generating content unrelated to Harry Potter. Figure 14.13 demonstrates how the model output changes before (Llama-7b-chat-hf column) and after (Finetuned Llama-b column) unlearning has occurred.

    @@ -1824,7 +1824,7 @@

    Case Study

    Other Uses

    Removing adversarial data
    -

    Deep learning models have previously been shown to be vulnerable to adversarial attacks, in which the attacker generates adversarial data similar to the original training data, where a human cannot tell the difference between the real and fabricated data. The adversarial data results in the model outputting incorrect predictions, which could have detrimental consequences in various applications, including healthcare diagnosis predictions. Machine unlearning has been used to unlearn the influence of adversarial data to prevent these incorrect predictions from occurring and causing any harm

    +

    Deep learning models have previously been shown to be vulnerable to adversarial attacks, in which the attacker generates adversarial data similar to the original training data, where a human cannot tell the difference between the real and fabricated data. The adversarial data results in the model outputting incorrect predictions, which could have detrimental consequences in various applications, including healthcare diagnosis predictions. Machine unlearning has been used to unlearn the influence of adversarial data to prevent these incorrect predictions from occurring and causing any harm.

    @@ -1851,13 +1851,13 @@

    Mechanics

  • Result Encryption: The result \(E(xy)\) remains encrypted and can only be decrypted by someone with the corresponding private key to reveal the actual product \(xy\).

    • Only authorized parties with the private key can decrypt the final outputs, protecting the intermediate state. However, noise accumulates with each operation, preventing further computation without decryption.

    -

    Beyond healthcare, homomorphic encryption enables confidential computing for applications like financial fraud detection, insurance analytics, genetics research, and more. It offers an alternative to techniques like multipartymultiparty computation and TEEs. Ongoing research aims to improve the efficiency and capabilities.

    +

    Beyond healthcare, homomorphic encryption enables confidential computing for applications like financial fraud detection, insurance analytics, genetics research, and more. It offers an alternative to techniques like multiparty computation and TEEs. Ongoing research improves the efficiency and capabilities.

    Tools like HElib, SEAL, and TensorFlow HE provide libraries for exploring implementing homomorphic encryption in real-world machine learning pipelines.

    Tradeoffs

    For many real-time and embedded applications, fully homomorphic encryption remains impractical for the following reasons.

    -

    Computational Overhead: Homomorphic encryption imposes very high computational overheads, often resulting in slowdowns of over 100x for real-world ML applications. This makes it impractical for many time-sensitive or resource-constrained uses. Optimized hardware and parallelization can help but not eliminate this issue.

    +

    Computational Overhead: Homomorphic encryption imposes very high computational overheads, often resulting in slowdowns of over 100x for real-world ML applications. This makes it impractical for many time-sensitive or resource-constrained uses. Optimized hardware and parallelization can alleviate but not eliminate this issue.

    Complexity of Implementation The sophisticated algorithms require deep expertise in cryptography to be implemented correctly. Nuances like format compatibility with floating point ML models and scalable key management pose hurdles. This complexity hinders widespread practical adoption.

    Algorithmic Limitations: Current schemes restrict the functions and depth of computations supported, limiting the models and data volumes that can be processed. Ongoing research is pushing these boundaries, but restrictions remain.

    Hardware Acceleration: Homomorphic encryption requires specialized hardware, such as secure processors or coprocessors with TEEs, which adds design and infrastructure costs.

    @@ -1885,7 +1885,7 @@

    Tradeoffs

    14.8.5 Secure Multiparty Communication

    Core Idea

    -

    The overarching goal of MPC is to enable different parties to jointly compute a function over their inputs while keeping those inputs private. For example, two organizations may want to collaborate on training a machine learning model by combining their respective data sets. Still, they cannot directly reveal that data due to Privacy or confidentiality constraints. MPC aims to provide protocols and techniques that allow them to achieve the benefits of pooled data for model accuracy without compromising the privacy of each organization’s sensitive data.

    +

    The overarching goal of Multi-Party Communication (MPC) is to enable different parties to jointly compute a function over their inputs while keeping those inputs private. For example, two organizations may want to collaborate on training a machine learning model by combining their respective data sets. Still, they cannot directly reveal that data due to Privacy or confidentiality constraints. MPC provides protocols and techniques that allow them to achieve the benefits of pooled data for model accuracy without compromising the privacy of each organization’s sensitive data.

    At a high level, MPC works by carefully splitting the computation into parts that each party can execute independently using their private input. The results are then combined to reveal only the final output of the function and nothing about the intermediate values. Cryptographic techniques are used to guarantee that the partial results remain private provably.

    Let’s take a simple example of an MPC protocol. One of the most basic MPC protocols is the secure addition of two numbers. Each party splits its input into random shares that are secretly distributed. They exchange the shares and locally compute the sum of the shares, which reconstructs the final sum without revealing the individual inputs. For example, if Alice has input x and Bob has input y:

      @@ -1895,7 +1895,7 @@

      Core Idea

    1. Alice computes \(x_2 + y_1 = s_1\), Bob computes \(x_1 + y_2 = s_2\)

    2. \(s_1 + s_2 = x + y\) is the final sum, without revealing \(x\) or \(y\).

    -

    Alice’s and Bob’s individual inputs (\(x\) and \(y\)) remain private, and each party only reveals one number associated with their original inputs. The random spits ensure no information about the original numbers disclosed

    +

    Alice’s and Bob’s individual inputs (\(x\) and \(y\)) remain private, and each party only reveals one number associated with their original inputs. The random outputs ensure that no information about the original numbers disclosed.

    Secure Comparison: Another basic operation is a secure comparison of two numbers, determining which is greater than the other. This can be done using techniques like Yao’s Garbled Circuits, where the comparison circuit is encrypted to allow joint evaluation of the inputs without leaking them.

    Secure Matrix Multiplication: Matrix operations like multiplication are essential for machine learning. MPC techniques like additive secret sharing can be used to split matrices into random shares, compute products on the shares, and then reconstruct the result.

    Secure Model Training: Distributed machine learning training algorithms like federated averaging can be made secure using MPC. Model updates computed on partitioned data at each node are secretly shared between nodes and aggregated to train the global model without exposing individual updates.

    @@ -1918,7 +1918,7 @@

    Tradeoffs

  • MPC systems require extensive communication and interaction between parties to compute on shares/ciphertexts jointly.

    • As a result, MPC protocols can slow down computations by 3-4 orders of magnitude compared to plain implementations. This becomes prohibitively expensive for large datasets and models. Therefore, training machine learning models on encrypted data using MPC remains infeasible today for realistic dataset sizes due to the overhead. Clever optimizations and approximations are needed to make MPC practical.

    -

    Ongoing MPC research aims to close this efficiency gap through cryptographic advances, new algorithms, trusted hardware like SGX enclaves, and leveraging accelerators like GPUs/TPUs. However, in the foreseeable future, some degree of approximation and performance tradeoff is needed to scale MPC to meet the demands of real-world machine learning systems.

    +

    Ongoing MPC research closes this efficiency gap through cryptographic advances, new algorithms, trusted hardware like SGX enclaves, and leveraging accelerators like GPUs/TPUs. However, in the foreseeable future, some degree of approximation and performance tradeoff is needed to scale MPC to meet the demands of real-world machine learning systems.

    @@ -1962,10 +1962,10 @@

    Benefits

    Tradeoffs

    -

    While synthetic data aims to remove any evidence of the original dataset, privacy leakage is still a risk since the synthetic data mimics the original data. The statistical information and distribution are similar, if not the same, between the original and synthetic data. By resampling from the distribution, adversaries may still be able to recover the original training samples. Due to their inherent learning processes and complexities, neural networks might accidentally reveal sensitive information about the original training data.

    +

    While synthetic data tries to remove any evidence of the original dataset, privacy leakage is still a risk since the synthetic data mimics the original data. The statistical information and distribution are similar, if not the same, between the original and synthetic data. By resampling from the distribution, adversaries may still be able to recover the original training samples. Due to their inherent learning processes and complexities, neural networks might accidentally reveal sensitive information about the original training data.

    A core challenge with synthetic data is the potential gap between synthetic and real-world data distributions. Despite advancements in generative modeling techniques, synthetic data may only partially capture real data’s complexity, diversity, and nuanced patterns. This can limit the utility of synthetic data for robustly training machine learning models. Rigorously evaluating synthetic data quality through adversary methods and comparing model performance to real data benchmarks helps assess and improve fidelity. However, inherently, synthetic data remains an approximation.

    Another critical concern is the privacy risks of synthetic data. Generative models may leak identifiable information about individuals in the training data, which could enable reconstruction of private information. Emerging adversarial attacks demonstrate the challenges in preventing identity leakage from synthetic data generation pipelines. Techniques like differential Privacy can help safeguard Privacy but come with tradeoffs in data utility. There is an inherent tension between producing useful synthetic data and fully protecting sensitive training data, which must be balanced.

    -

    Additional pitfalls of synthetic data include amplified biases, labeling difficulties, the computational overhead of training generative models, storage costs, and failure to account for out-of-distribution novel data. While these are secondary to the core synthetic-real gap and privacy risks, they remain important considerations when evaluating the suitability of synthetic data for particular machine-learning tasks. As with any technique, the advantages of synthetic data come with inherent tradeoffs and limitations that require thoughtful mitigation strategies.

    +

    Additional pitfalls of synthetic data include amplified biases, mislabeling, the computational overhead of training generative models, storage costs, and failure to account for out-of-distribution novel data. While these are secondary to the core synthetic-real gap and privacy risks, they remain important considerations when evaluating the suitability of synthetic data for particular machine-learning tasks. As with any technique, the advantages of synthetic data come with inherent tradeoffs and limitations that require thoughtful mitigation strategies.

    diff --git a/contents/responsible_ai/responsible_ai.html b/contents/responsible_ai/responsible_ai.html index 7f4ddaa7..dad695c9 100644 --- a/contents/responsible_ai/responsible_ai.html +++ b/contents/responsible_ai/responsible_ai.html @@ -913,7 +913,7 @@

    15.4.2 Explainability

    For cloud-based machine learning, explainability techniques can leverage significant compute resources, enabling complex methods like SHAP values or sampling-based approaches to interpret model behaviors. For example, Microsoft’s InterpretML toolkit provides explainability techniques tailored for cloud environments.

    However, edge ML operates on resource-constrained devices, requiring more lightweight explainability methods that can run locally without excessive latency. Techniques like LIME (Ribeiro, Singh, and Guestrin 2016) approximate model explanations using linear models or decision trees to avoid expensive computations, which makes them ideal for resource-constrained devices. However, LIME requires training hundreds to even thousands of models to generate good explanations, which is often infeasible given edge computing constraints. In contrast, saliency-based methods are often much faster in practice, only requiring a single forward pass through the network to estimate feature importance. This greater efficiency makes such methods better suited to edge devices with limited compute resources where low-latency explanations are critical.

    -

    Given tiny hardware capabilities, embedded systems pose the most significant challenges for explainability. More compact models and limited data make inherent model transparency easier. Explaining decisions may not be feasible on high-size and power-optimized microcontrollers. DARPA’s Transparent Computing program aims to develop extremely low overhead explainability, especially for TinyML devices like sensors and wearables.

    +

    Given tiny hardware capabilities, embedded systems pose the most significant challenges for explainability. More compact models and limited data make inherent model transparency easier. Explaining decisions may not be feasible on high-size and power-optimized microcontrollers. DARPA’s Transparent Computing program tries to develop extremely low overhead explainability, especially for TinyML devices like sensors and wearables.

    15.4.3 Fairness

    diff --git a/contents/robust_ai/robust_ai.html b/contents/robust_ai/robust_ai.html index 53242247..fc13583b 100644 --- a/contents/robust_ai/robust_ai.html +++ b/contents/robust_ai/robust_ai.html @@ -1368,7 +1368,7 @@

    Mechanis

    One prominent category of adversarial attacks is gradient-based attacks. These attacks leverage the gradients of the ML model’s loss function to craft adversarial examples. The Fast Gradient Sign Method (FGSM) is a well-known technique in this category. FGSM perturbs the input data by adding small noise in the gradient direction, aiming to maximize the model’s prediction error. FGSM can quickly generate adversarial examples, as shown in Figure 17.19, by taking a single step in the gradient direction.

    Another variant, the Projected Gradient Descent (PGD) attack, extends FGSM by iteratively applying the gradient update step, allowing for more refined and powerful adversarial examples. The Jacobian-based Saliency Map Attack (JSMA) is another gradient-based approach that identifies the most influential input features and perturbs them to create adversarial examples.

    Optimization-based Attacks

    -

    These attacks formulate the generation of adversarial examples as an optimization problem. The Carlini and Wagner (C&W) attack is a prominent example in this category. It aims to find the smallest perturbation that can cause misclassification while maintaining the perceptual similarity to the original input. The C&W attack employs an iterative optimization process to minimize the perturbation while maximizing the model’s prediction error.

    +

    These attacks formulate the generation of adversarial examples as an optimization problem. The Carlini and Wagner (C&W) attack is a prominent example in this category. It finds the smallest perturbation that can cause misclassification while maintaining the perceptual similarity to the original input. The C&W attack employs an iterative optimization process to minimize the perturbation while maximizing the model’s prediction error.

    Another optimization-based approach is the Elastic Net Attack to DNNs (EAD), which incorporates elastic net regularization to generate adversarial examples with sparse perturbations.

    Transfer-based Attacks

    Transfer-based attacks exploit the transferability property of adversarial examples. Transferability refers to the phenomenon where adversarial examples crafted for one ML model can often fool other models, even if they have different architectures or were trained on different datasets. This enables attackers to generate adversarial examples using a surrogate model and then transfer them to the target model without requiring direct access to its parameters or gradients. Transfer-based attacks highlight the generalization of adversarial vulnerabilities across different models and the potential for black-box attacks.

    @@ -1549,9 +1549,9 @@

    Mechanisms of -

    Modifying training data labels: One of the most straightforward mechanisms of data poisoning is modifying the training data labels. In this approach, the attacker selectively changes the labels of a subset of the training samples to mislead the model’s learning process as shown in Figure 17.23. For example, in a binary classification task, the attacker might flip the labels of some positive samples to negative, or vice versa. By introducing such label noise, the attacker aims to degrade the model’s performance or cause it to make incorrect predictions for specific target instances.

    +

    Modifying training data labels: One of the most straightforward mechanisms of data poisoning is modifying the training data labels. In this approach, the attacker selectively changes the labels of a subset of the training samples to mislead the model’s learning process as shown in Figure 17.23. For example, in a binary classification task, the attacker might flip the labels of some positive samples to negative, or vice versa. By introducing such label noise, the attacker degrades the model’s performance or cause it to make incorrect predictions for specific target instances.

    Altering feature values in training data: Another mechanism of data poisoning involves altering the feature values of the training samples without modifying the labels. The attacker carefully crafts the feature values to introduce specific biases or vulnerabilities into the model. For instance, in an image classification task, the attacker might add imperceptible perturbations to a subset of images, causing the model to learn a particular pattern or association. This type of poisoning can create backdoors or trojans in the trained model, which specific input patterns can trigger.

    -

    Injecting carefully crafted malicious samples: In this mechanism, the attacker creates malicious samples designed to poison the model. These samples are crafted to have a specific impact on the model’s behavior while blending in with the legitimate training data. The attacker might use techniques such as adversarial perturbations or data synthesis to generate poisoned samples that are difficult to detect. The attacker aims to manipulate the model’s decision boundaries by injecting these malicious samples into the training data or introducing targeted misclassifications.

    +

    Injecting carefully crafted malicious samples: In this mechanism, the attacker creates malicious samples designed to poison the model. These samples are crafted to have a specific impact on the model’s behavior while blending in with the legitimate training data. The attacker might use techniques such as adversarial perturbations or data synthesis to generate poisoned samples that are difficult to detect. The attacker manipulates the model’s decision boundaries by injecting these malicious samples into the training data or introducing targeted misclassifications.

    Exploiting data collection and preprocessing vulnerabilities: Data poisoning attacks can also exploit the data collection and preprocessing pipeline vulnerabilities. If the data collection process is not secure or there are weaknesses in the data preprocessing steps, an attacker can manipulate the data before it reaches the training phase. For example, if data is collected from untrusted sources or issues in data cleaning or aggregation, an attacker can introduce poisoned samples or manipulate the data to their advantage.

    Manipulating data at the source (e.g., sensor data): In some cases, attackers can manipulate the data at its source, such as sensor data or input devices. By tampering with the sensors or manipulating the environment in which data is collected, attackers can introduce poisoned samples or bias the data distribution. For instance, in a self-driving car scenario, an attacker might manipulate the sensors or the environment to feed misleading information into the training data, compromising the model’s ability to make safe and reliable decisions.

    @@ -2152,7 +2152,7 @@

    16.10.3 Further Improvements

    While Google has made measurable progress in restraining the carbon footprint of its AI operations, the company recognizes further efficiency gains will be vital for responsible innovation given the technology’s ongoing expansion.

    One area of focus is showing how advances are often incorrectly viewed as increasing unsustainable computing—like neural architecture search (NAS) to find optimized models— spur downstream savings, outweighing their upfront costs. Despite expending more energy on model discovery rather than hand-engineering, NAS cuts lifetime emissions by producing efficient designs callable across countless applications.

    -

    Additionally, the analysis reveals that focusing sustainability efforts on data center and server-side optimization makes sense, given the dominant energy draw versus consumer devices. Though Google aims to shrink inference impacts across processors like mobile phones, priority rests on improving training cycles and data center renewables procurement for maximal effect.

    +

    Additionally, the analysis reveals that focusing sustainability efforts on data center and server-side optimization makes sense, given the dominant energy draw versus consumer devices. Though Google shrinks inference impacts across processors like mobile phones, priority rests on improving training cycles and data center renewables procurement for maximal effect.

    To that end, Google’s progress in pooling computing inefficiently designed cloud facilities highlights the value of scale and centralization. As more workloads shift away from inefficient on-premise servers, internet giants’ prioritization of renewable energy—with Google and Facebook matched 100% by renewables since 2017 and 2020, respectively—unlocks compounding emissions cuts.

    Together, these efforts emphasize that while no resting on laurels is possible, Google’s multipronged approach shows that AI efficiency improvements are only accelerating. Cross-domain initiatives around lifecycle assessment, carbon-conscious development patterns, transparency, and matching rising AI demand with clean electricity supply pave a path toward bending the curve further as adoption grows. The company’s results compel the broader field towards replicating these integrated sustainability pursuits.

    @@ -1428,7 +1428,7 @@

    16.12.2 Restriction Mechanisms

    @@ -1509,7 +1509,7 @@

    16.14.2 Challenges

    -

    Despite these promising directions, several challenges need to be addressed. One of the major challenges is the need for consistent standards and methodologies for measuring and reporting the environmental impact of AI. These methods must capture the complexity of the life cycles of AI models and system hardware. Next, efficient and environmentally sustainable AI infrastructure and system hardware are needed. This consists of three components. It aims to maximize the utilization of accelerator and system resources, prolong the lifetime of AI infrastructure, and design systems hardware with environmental impact in mind.

    +

    Despite these promising directions, several challenges need to be addressed. One of the major challenges is the need for consistent standards and methodologies for measuring and reporting the environmental impact of AI. These methods must capture the complexity of the life cycles of AI models and system hardware. Next, efficient and environmentally sustainable AI infrastructure and system hardware are needed. This consists of three components. It maximizes the utilization of accelerator and system resources, prolong the lifetime of AI infrastructure, and design systems hardware with environmental impact in mind.

    On the software side, we should trade off experimentation and the subsequent training cost. Techniques such as neural architecture search and hyperparameter optimization can be used for design space exploration. However, these are often very resource-intensive. Efficient experimentation can significantly reduce the environmental footprint overhead. Next, methods to reduce wasted training efforts should be explored.

    To improve model quality, we often scale the dataset. However, the increased system resources required for data storage and ingestion caused by this scaling have a significant environmental impact (Wu et al. 2022). A thorough understanding of the rate at which data loses its predictive value and devising data sampling strategies is important.

    diff --git a/contents/training/training.html b/contents/training/training.html index 3484dbf6..9cf62b42 100644 --- a/contents/training/training.html +++ b/contents/training/training.html @@ -837,7 +837,7 @@

    <

    7.1 Introduction

    -

    Training is critical for developing accurate and useful AI systems using machine learning. The training aims to create a machine learning model that can generalize to new, unseen data rather than memorizing the training examples. This is done by feeding training data into algorithms that learn patterns from these examples by adjusting internal parameters.

    +

    Training is critical for developing accurate and useful AI systems using machine learning. The training creates a machine learning model that can generalize to new, unseen data rather than memorizing the training examples. This is done by feeding training data into algorithms that learn patterns from these examples by adjusting internal parameters.

    The algorithms minimize a loss function, which compares their predictions on the training data to the known labels or solutions, guiding the learning. Effective training often requires high-quality, representative data sets large enough to capture variability in real-world use cases.

    It also requires choosing an algorithm suited to the task, whether a neural network for computer vision, a reinforcement learning algorithm for robotic control, or a tree-based method for categorical prediction. Careful tuning is needed for the model structure, such as neural network depth and width, and learning parameters like step size and regularization strength.

    Techniques to prevent overfitting like regularization penalties and validation with held-out data, are also important. Overfitting can occur when a model fits the training data too closely, failing to generalize to new data. This can happen if the model is too complex or trained too long.

    @@ -1564,10 +1564,10 @@

    BigML

    Several commercial auto-tuning platforms are available to address this problem. One solution is Google’s Vertex AI Cloud, which has extensive integrated support for state-of-the-art tuning techniques.

    -

    One of the most salient capabilities of Google’s Vertex AI-managed machine learning platform is efficient, integrated hyperparameter tuning for model development. Successfully training performant ML models requires identifying optimal configurations for a set of external hyperparameters that dictate model behavior, posing a challenging high-dimensional search problem. Vertex AI aims to simplify this through Automated Machine Learning (AutoML) tooling.

    +

    One of the most salient capabilities of Google’s Vertex AI-managed machine learning platform is efficient, integrated hyperparameter tuning for model development. Successfully training performant ML models requires identifying optimal configurations for a set of external hyperparameters that dictate model behavior, posing a challenging high-dimensional search problem. Vertex AI simplifies this through Automated Machine Learning (AutoML) tooling.

    Specifically, data scientists can leverage Vertex AI’s hyperparameter tuning engines by providing a labeled dataset and choosing a model type such as a Neural Network or Random Forest classifier. Vertex launches a Hyperparameter Search job transparently on the backend, fully handling resource provisioning, model training, metric tracking, and result analysis automatically using advanced optimization algorithms.

    Under the hood, Vertex AutoML employs various search strategies to intelligently explore the most promising hyperparameter configurations based on previous evaluation results. Among these, Bayesian Optimization is offered as it provides superior sample efficiency, requiring fewer training iterations to achieve optimized model quality compared to standard Grid Search or Random Search methods. For more complex neural architecture search spaces, Vertex AutoML utilizes Population-Based Training, which simultaneously trains multiple models and dynamically adjusts their hyperparameters by leveraging the performance of other models in the population, analogous to natural selection principles.

    -

    Vertex AI aims to democratize state-of-the-art hyperparameter search techniques at the cloud scale for all ML developers, abstracting away the underlying orchestration and execution complexity. Users focus solely on their dataset, model requirements, and accuracy goals, while Vertex manages the tuning cycle, resource allocation, model training, accuracy tracking, and artifact storage under the hood. The result is getting deployment-ready, optimized ML models faster for the target problem.

    +

    Vertex AI democratizes state-of-the-art hyperparameter search techniques at the cloud scale for all ML developers, abstracting away the underlying orchestration and execution complexity. Users focus solely on their dataset, model requirements, and accuracy goals, while Vertex manages the tuning cycle, resource allocation, model training, accuracy tracking, and artifact storage under the hood. The result is getting deployment-ready, optimized ML models faster for the target problem.

    TinyML

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_0x38ec4d=_0x2946c4(_0x38ec4d),_0x4edbe2[_0x240ddf(0x2b73)]&&(_0x343e47[_0x240ddf(0x3903)]=_0x240ddf(0x2f35)),_0x343e47[_0x240ddf(0x12ab)]=new RegExp(_0x38ec4d),_0x343e47;if(!0x0===_0x2002e2[_0x240ddf(0x34c)](_0x38ec4d)&&(_0x38ec4d=_0x38ec4d['replace'](_0x2002e2,(_0x461d2e,_0x3829ae,_0x6ec2f)=>(void 0x0===_0x6ec2f?(_0x343e47['min']=Number(_0x3829ae),_0x343e47[_0x240ddf(0x28c)]=Number(_0x3829ae)):(_0x6ec2f=_0x6ec2f[_0x240ddf(0x1db7)](/, */,''),void 0x0===_0x3829ae?(_0x343e47['min']=0x0,_0x343e47[_0x240ddf(0x28c)]=Number(_0x6ec2f)):(_0x343e47[_0x240ddf(0x12f9)]=Number(_0x3829ae),_0x343e47['max']=Number(_0x6ec2f||0x3e7))),_0x343e47['greedy']=!0x0,_0x343e47[_0x240ddf(0x12f9)]||(_0x343e47[_0x240ddf(0x180e)]=!0x0),''))),'('===_0x1f4a92(_0x38ec4d)&&')'===_0x622ced(_0x38ec4d)){_0x4f0984[_0x240ddf(0x34c)](_0x38ec4d)?(_0x343e47['choices']=_0x38ec4d[_0x240ddf(0x29d0)](_0x4f0984),_0x343e47[_0x240ddf(0x5d3)]=_0x240ddf(0x43ea)):(_0x343e47[_0x240ddf(0x2607)]=_0x38ec4d[_0x240ddf(0x29d0)]('|'),_0x343e47[_0x240ddf(0x5d3)]='or'),_0x343e47[_0x240ddf(0x2607)][0x0]=_0x22a720(_0x343e47[_0x240ddf(0x2607)][0x0]);let _0x72aecf=_0x343e47[_0x240ddf(0x2607)]['length']-0x1;_0x343e47['choices'][_0x72aecf]=_0x543e07(_0x343e47[_0x240ddf(0x2607)][_0x72aecf]),_0x343e47['choices']=_0x343e47[_0x240ddf(0x2607)][_0x240ddf(0x2c94)](_0x1f44db=>_0x1f44db[_0x240ddf(0x1d0e)]()),_0x343e47['choices']=_0x343e47[_0x240ddf(0x2607)][_0x240ddf(0x295e)](_0x26e622=>_0x26e622),_0x343e47[_0x240ddf(0x2607)]=_0x343e47[_0x240ddf(0x2607)][_0x240ddf(0x2c94)](_0x509492=>_0x509492['split'](/ /g)[_0x240ddf(0x2c94)](_0x4ee4d4=>_0x7e6f8c(_0x4ee4d4,_0x4edbe2))),_0x38ec4d='';}if('{'===_0x1f4a92(_0x38ec4d)&&'}'===_0x622ced(_0x38ec4d)){if(_0x38ec4d=_0x2946c4(_0x38ec4d),_0x343e47[_0x240ddf(0xdf8)]=_0x38ec4d,/\//[_0x240ddf(0x34c)](_0x38ec4d)){let _0x48eac8=_0x343e47[_0x240ddf(0xdf8)][_0x240ddf(0x29d0)](/\//);_0x343e47[_0x240ddf(0xdf8)]=_0x48eac8[0x0],_0x343e47[_0x240ddf(0x44ff)]=_0x48eac8[0x1],_0x240ddf(0xa49)===_0x343e47[_0x240ddf(0x44ff)]&&(_0x343e47[_0x240ddf(0x44ff)]=_0x240ddf(0x19b5)),_0x343e47[_0x240ddf(0x44ff)]=_0x343e47[_0x240ddf(0x44ff)][_0x240ddf(0x32fe)](0x0)[_0x240ddf(0x4447)]()+_0x343e47[_0x240ddf(0x44ff)][_0x240ddf(0x32a4)](0x1)[_0x240ddf(0x1a57)](),void 0x0!==_0x48eac8[0x2]&&(_0x343e47[_0x240ddf(0x14f6)]=_0x48eac8[0x2]);}return _0x343e47;}if('<'===_0x1f4a92(_0x38ec4d)&&'>'===_0x622ced(_0x38ec4d))return _0x38ec4d=_0x2946c4(_0x38ec4d),_0x343e47[_0x240ddf(0x1637)]=_0x81f271(_0x38ec4d),_0x343e47[_0x240ddf(0x188a)]=!0x0,_0x343e47;if('%'===_0x1f4a92(_0x38ec4d)&&'%'===_0x622ced(_0x38ec4d))return _0x38ec4d=_0x2946c4(_0x38ec4d),_0x343e47[_0x240ddf(0x68)]=_0x38ec4d,_0x343e47;}return'#'===_0x1f4a92(_0x38ec4d)?(_0x343e47[_0x240ddf(0x17a2)]=_0x22a720(_0x38ec4d),_0x343e47[_0x240ddf(0x17a2)]=_0x81f271(_0x343e47[_0x240ddf(0x17a2)]),_0x343e47):'@'===_0x1f4a92(_0x38ec4d)?(_0x343e47[_0x240ddf(0xd7c)]=_0x22a720(_0x38ec4d),_0x343e47):'.'===_0x38ec4d?(_0x343e47[_0x240ddf(0x42e3)]=!0x0,_0x343e47):'*'===_0x38ec4d?(_0x343e47[_0x240ddf(0x42e3)]=!0x0,_0x343e47[_0x240ddf(0x188a)]=!0x0,_0x343e47[_0x240ddf(0x180e)]=!0x0,_0x343e47):(_0x38ec4d&&(_0x38ec4d=(_0x38ec4d=_0x38ec4d['replace']('\x5c*','*'))[_0x240ddf(0x1db7)]('\x5c.','.'),_0x4edbe2[_0x240ddf(0x2b73)]?_0x343e47['use']='text':_0x38ec4d=_0x38ec4d[_0x240ddf(0x1a57)](),_0x343e47[_0x240ddf(0x2013)]=_0x38ec4d),_0x343e47);},_0x1c7e56=_0x7e6f8c,_0x5e46c9=/[a-z0-9][-–—][a-z]/i,_0x214e70=function(_0x255400,_0x249e6f){const _0x3bdc3b=_0x235866;let _0x40adce=_0x249e6f['model']['one']['prefixes'];for(let _0x232cd6=_0x255400[_0x3bdc3b(0x205b)]-0x1;_0x232cd6>=0x0;_0x232cd6-=0x1){let _0x3cab9a=_0x255400[_0x232cd6];if(_0x3cab9a[_0x3bdc3b(0x2013)]&&_0x5e46c9[_0x3bdc3b(0x34c)](_0x3cab9a['word'])){let _0x4e7cb4=_0x3cab9a[_0x3bdc3b(0x2013)][_0x3bdc3b(0x29d0)](/[-–—]/g);if(_0x40adce['hasOwnProperty'](_0x4e7cb4[0x0]))continue;_0x4e7cb4=_0x4e7cb4[_0x3bdc3b(0x295e)](_0x3d7bd2=>_0x3d7bd2)[_0x3bdc3b(0x2a4c)](),_0x255400[_0x3bdc3b(0x506f)](_0x232cd6,0x1),_0x4e7cb4['forEach'](_0x457591=>{const _0x50e08c=_0x3bdc3b;let _0x599895=Object[_0x50e08c(0x11e8)]({},_0x3cab9a);_0x599895['word']=_0x457591,_0x255400[_0x50e08c(0x506f)](_0x232cd6,0x0,_0x599895);});}}return _0x255400;},_0x7e246=function(_0x476d69,_0x4eb1e1){const _0x576461=_0x235866;let {all:_0x48d445}=_0x4eb1e1[_0x576461(0x8f)][_0x576461(0x141f)][_0x576461(0x560)][_0x576461(0x91)]||{},_0x3a927f=_0x476d69[_0x576461(0xdf8)];return _0x48d445?_0x48d445(_0x3a927f,_0x4eb1e1[_0x576461(0x2c3e)]):[];},_0x2d5a00=function(_0x1084c5,_0x2b770d){const _0x594828=_0x235866;let {all:_0x5d25c3}=_0x2b770d[_0x594828(0x8f)][_0x594828(0x141f)][_0x594828(0x560)][_0x594828(0x3c45)]||{};return _0x5d25c3?_0x5d25c3(_0x1084c5[_0x594828(0xdf8)],_0x2b770d[_0x594828(0x2c3e)]):[_0x1084c5[_0x594828(0xdf8)]];},_0xdcf779=function(_0x587188,_0x5790a7){const _0x430688=_0x235866;let {all:_0x159648}=_0x5790a7[_0x430688(0x8f)][_0x430688(0x141f)][_0x430688(0x560)][_0x430688(0x2c03)]||{};return _0x159648?_0x159648(_0x587188[_0x430688(0xdf8)],_0x5790a7[_0x430688(0x2c3e)]):[_0x587188[_0x430688(0xdf8)]];},_0x9ecca8=function(_0x5e2ae9,_0x3490c6){const _0x397b6e=_0x235866;return _0x5e2ae9=_0x5e2ae9[_0x397b6e(0x2c94)](_0x4ae61c=>{const _0x4fe8ce=_0x397b6e;if(_0x4ae61c[_0x4fe8ce(0xdf8)]){if(_0x3490c6[_0x4fe8ce(0x8f)][_0x4fe8ce(0x141f)]&&_0x3490c6[_0x4fe8ce(0x8f)][_0x4fe8ce(0x141f)]['transform']){let _0x521e2a=[];_0x4ae61c['pos']?_0x4fe8ce(0x41f2)===_0x4ae61c[_0x4fe8ce(0x44ff)]?_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0x7e246(_0x4ae61c,_0x3490c6)):_0x4fe8ce(0x1ce5)===_0x4ae61c['pos']?_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0x2d5a00(_0x4ae61c,_0x3490c6)):'Adjective'===_0x4ae61c[_0x4fe8ce(0x44ff)]&&(_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0xdcf779(_0x4ae61c,_0x3490c6))):(_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0x7e246(_0x4ae61c,_0x3490c6)),_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0x2d5a00(_0x4ae61c,_0x3490c6)),_0x521e2a=_0x521e2a[_0x4fe8ce(0x41bf)](_0xdcf779(_0x4ae61c,_0x3490c6))),_0x521e2a=_0x521e2a['filter'](_0x413dd7=>_0x413dd7),_0x521e2a[_0x4fe8ce(0x205b)]>0x0&&(_0x4ae61c['operator']='or',_0x4ae61c['fastOr']=new Set(_0x521e2a));}else _0x4ae61c[_0x4fe8ce(0x194)]=_0x4ae61c['root'],delete _0x4ae61c['id'],delete _0x4ae61c['root'];}return _0x4ae61c;}),_0x5e2ae9;},_0x3165cb=function(_0x4e87c1){const _0x37c813=_0x235866;return _0x4e87c1=function(_0x28e6e1){const _0x1ef7e3=a0_0x329b;let _0x4972e5=0x0,_0x31807c=null;for(let _0x5a417d=0x0;_0x5a417d<_0x28e6e1[_0x1ef7e3(0x205b)];_0x5a417d++){const _0x1bd644=_0x28e6e1[_0x5a417d];!0x0===_0x1bd644[_0x1ef7e3(0x4dc3)]&&(_0x31807c=_0x1bd644[_0x1ef7e3(0x4941)],null===_0x31807c&&(_0x31807c=String(_0x4972e5),_0x4972e5+=0x1)),null!==_0x31807c&&(_0x1bd644['group']=_0x31807c),!0x0===_0x1bd644['groupEnd']&&(_0x31807c=null);}return _0x28e6e1;}(_0x4e87c1),_0x4e87c1=_0x4e87c1[_0x37c813(0x2c94)](_0x16d5a9=>{const _0x10bb99=_0x37c813;if(void 0x0!==_0x16d5a9[_0x10bb99(0x2607)]){if('or'!==_0x16d5a9['operator'])return _0x16d5a9;if(!0x0===_0x16d5a9[_0x10bb99(0x4bcb)])return _0x16d5a9;let _0x1c3fc0=_0x16d5a9['choices']['every'](_0x416e9f=>{const _0x48bc22=_0x10bb99;if(0x1!==_0x416e9f['length'])return!0x1;let _0x2d8142=_0x416e9f[0x0];return!0x0!==_0x2d8142[_0x48bc22(0x4bcb)]&&!_0x2d8142['start']&&!_0x2d8142['end']&&void 0x0!==_0x2d8142[_0x48bc22(0x2013)]&&!0x0!==_0x2d8142['negative']&&!0x0!==_0x2d8142[_0x48bc22(0x180e)]&&!0x0!==_0x2d8142[_0x48bc22(0xd7c)];});!0x0===_0x1c3fc0&&(_0x16d5a9[_0x10bb99(0x4eec)]=new Set(),_0x16d5a9[_0x10bb99(0x2607)][_0x10bb99(0x4854)](_0x4718e9=>{const _0x38706d=_0x10bb99;_0x16d5a9[_0x38706d(0x4eec)][_0x38706d(0x2fd8)](_0x4718e9[0x0][_0x38706d(0x2013)]);}),delete _0x16d5a9[_0x10bb99(0x2607)]);}return _0x16d5a9;}),_0x4e87c1=function(_0x3f7aa6){const _0x30a748=_0x37c813;return _0x3f7aa6[_0x30a748(0x2c94)](_0xb57b6d=>(_0xb57b6d[_0x30a748(0x4bcb)]&&_0xb57b6d[_0x30a748(0x2607)]&&_0xb57b6d[_0x30a748(0x2607)][_0x30a748(0x4854)](_0x20c16a=>{const _0x497de9=_0x30a748;0x1===_0x20c16a[_0x497de9(0x205b)]&&_0x20c16a[0x0][_0x497de9(0x2013)]&&(_0x20c16a[0x0][_0x497de9(0x4bcb)]=!0x0,_0x20c16a[0x0][_0x497de9(0x12f9)]=_0xb57b6d[_0x497de9(0x12f9)]);}),_0xb57b6d));}(_0x4e87c1),_0x4e87c1;},_0x996130=function(_0x5d9313,_0xfa3c7,_0x29f533){const _0x11729a=_0x235866;if(null==_0x5d9313||''===_0x5d9313)return[];_0xfa3c7=_0xfa3c7||{},_0x11729a(0x1ed7)==typeof _0x5d9313&&(_0x5d9313=String(_0x5d9313));let _0x58c33b=_0x5edb39(_0x5d9313);return _0x58c33b=_0x58c33b['map'](_0x2f9621=>_0x1c7e56(_0x2f9621,_0xfa3c7)),_0x58c33b=_0x214e70(_0x58c33b,_0x29f533),_0x58c33b=_0x9ecca8(_0x58c33b,_0x29f533),_0x58c33b=_0x3165cb(_0x58c33b,_0xfa3c7),_0x58c33b;},_0x21e24c=function(_0x162b1a,_0x365d2d){for(let _0x483d6e of _0x365d2d)if(_0x162b1a['has'](_0x483d6e))return!0x0;return!0x1;},_0x8c4700=function(_0x58e914,_0x375365){const _0x4ef50b=_0x235866;for(let _0x242032=0x0;_0x242032<_0x58e914[_0x4ef50b(0x205b)];_0x242032+=0x1){let _0x647d6c=_0x58e914[_0x242032];if(!0x0!==_0x647d6c[_0x4ef50b(0x180e)]&&!0x0!==_0x647d6c[_0x4ef50b(0x3fbe)]&&!0x0!==_0x647d6c['fuzzy']){if(void 0x0!==_0x647d6c[_0x4ef50b(0x2013)]&&!0x1===_0x375365[_0x4ef50b(0x5ec)](_0x647d6c['word']))return!0x0;if(void 0x0!==_0x647d6c['tag']&&!0x1===_0x375365[_0x4ef50b(0x5ec)]('#'+_0x647d6c['tag']))return!0x0;if(_0x647d6c[_0x4ef50b(0x4eec)]&&!0x1===_0x21e24c(_0x647d6c[_0x4ef50b(0x4eec)],_0x375365))return!0x1;}}return!0x1;},_0x526d59=function(_0x59938e,_0x2a9c8b,_0x365c33=0x3){const _0x4908a6=_0x235866;if(_0x59938e===_0x2a9c8b)return 0x1;if(_0x59938e['length']<_0x365c33||_0x2a9c8b[_0x4908a6(0x205b)]<_0x365c33)return 0x0;const _0x6d86bc=function(_0x2c5e36,_0x1b005a){const _0x2e57a8=_0x4908a6;let _0x56523a=_0x2c5e36[_0x2e57a8(0x205b)],_0x191b43=_0x1b005a['length'];if(0x0===_0x56523a)return _0x191b43;if(0x0===_0x191b43)return _0x56523a;let _0x23b288=(_0x191b43>_0x56523a?_0x191b43:_0x56523a)+0x1;if(Math[_0x2e57a8(0x444c)](_0x56523a-_0x191b43)>(_0x23b288||0x64))return _0x23b288||0x64;let _0x197aa3,_0x36ddc0,_0x565247,_0x3f2c1c,_0x176da6,_0x1a04e7,_0x2c6aad=[];for(let _0x4fb37b=0x0;_0x4fb37b<_0x23b288;_0x4fb37b++)_0x2c6aad[_0x4fb37b]=[_0x4fb37b],_0x2c6aad[_0x4fb37b][_0x2e57a8(0x205b)]=_0x23b288;for(let _0x674263=0x0;_0x674263<_0x23b288;_0x674263++)_0x2c6aad[0x0][_0x674263]=_0x674263;for(let _0x3e9ad1=0x1;_0x3e9ad1<=_0x56523a;++_0x3e9ad1)for(_0x36ddc0=_0x2c5e36[_0x3e9ad1-0x1],_0x197aa3=0x1;_0x197aa3<=_0x191b43;++_0x197aa3){if(_0x3e9ad1===_0x197aa3&&_0x2c6aad[_0x3e9ad1][_0x197aa3]>0x4)return _0x56523a;_0x565247=_0x1b005a[_0x197aa3-0x1],_0x3f2c1c=_0x36ddc0===_0x565247?0x0:0x1,_0x176da6=_0x2c6aad[_0x3e9ad1-0x1][_0x197aa3]+0x1,(_0x1a04e7=_0x2c6aad[_0x3e9ad1][_0x197aa3-0x1]+0x1)<_0x176da6&&(_0x176da6=_0x1a04e7),(_0x1a04e7=_0x2c6aad[_0x3e9ad1-0x1][_0x197aa3-0x1]+_0x3f2c1c)<_0x176da6&&(_0x176da6=_0x1a04e7);let _0x47887f=_0x3e9ad1>0x1&&_0x197aa3>0x1&&_0x36ddc0===_0x1b005a[_0x197aa3-0x2]&&_0x2c5e36[_0x3e9ad1-0x2]===_0x565247&&(_0x1a04e7=_0x2c6aad[_0x3e9ad1-0x2][_0x197aa3-0x2]+_0x3f2c1c)<_0x176da6;_0x2c6aad[_0x3e9ad1][_0x197aa3]=_0x47887f?_0x1a04e7:_0x176da6;}return _0x2c6aad[_0x56523a][_0x191b43];}(_0x59938e,_0x2a9c8b);let 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/,_0x3db438=(_0x35eac7,_0x2fcffc)=>-0x1!==_0x35eac7['post'][_0x235866(0x3458)](_0x2fcffc),_0x346944={'hasQuote':_0x132974=>_0xd885b8[_0x235866(0x34c)](_0x132974[_0x235866(0x1553)])||_0x189141[_0x235866(0x34c)](_0x132974[_0x235866(0x9ce)]),'hasComma':_0x7036c7=>_0x3db438(_0x7036c7,','),'hasPeriod':_0x570311=>!0x0===_0x3db438(_0x570311,'.')&&!0x1===_0x3db438(_0x570311,_0x235866(0x23a)),'hasExclamation':_0x2a1dc6=>_0x3db438(_0x2a1dc6,'!'),'hasQuestionMark':_0x512720=>_0x3db438(_0x512720,'?')||_0x3db438(_0x512720,'¿'),'hasEllipses':_0x17a866=>_0x3db438(_0x17a866,'..')||_0x3db438(_0x17a866,'…'),'hasSemicolon':_0x1cfea7=>_0x3db438(_0x1cfea7,';'),'hasColon':_0x33cc36=>_0x3db438(_0x33cc36,':'),'hasSlash':_0x36118f=>/\//['test'](_0x36118f['text']),'hasHyphen':_0x4bd4d8=>_0xaaf537[_0x235866(0x34c)](_0x4bd4d8['post'])||_0xaaf537[_0x235866(0x34c)](_0x4bd4d8[_0x235866(0x1553)]),'hasDash':_0x4b59ad=>_0x1ef0b9[_0x235866(0x34c)](_0x4b59ad['post'])||_0x1ef0b9[_0x235866(0x34c)](_0x4b59ad[_0x235866(0x1553)]),'hasContraction':_0x3bd054=>Boolean(_0x3bd054[_0x235866(0x15c0)]),'isAcronym':_0x5a9d0b=>_0x5a9d0b[_0x235866(0x23d1)][_0x235866(0x5ec)](_0x235866(0x265b)),'isKnown':_0x170608=>_0x170608[_0x235866(0x23d1)][_0x235866(0x3b53)]>0x0,'isTitleCase':_0x57a08d=>/^\p{Lu}[a-z'\u00C0-\u00FF]/u[_0x235866(0x34c)](_0x57a08d[_0x235866(0x2f35)]),'isUpperCase':_0x206cd2=>/^\p{Lu}+$/u['test'](_0x206cd2[_0x235866(0x2f35)])};_0x346944['hasQuotation']=_0x346944[_0x235866(0x85e)];const _0x16bdae=_0x346944;let _0x20ab4c=function(){};_0x20ab4c=function(_0x10ce74,_0x4344aa,_0x586f23,_0xc39d0f){const _0x507e12=_0x235866;let _0x30f2b8=function(_0x39b4d2,_0x388b26,_0x8f2edd,_0x1bb78d){const _0x288688=a0_0x329b;if(!0x0===_0x388b26['anything'])return!0x0;if(!0x0===_0x388b26[_0x288688(0x1698)]&&0x0!==_0x8f2edd)return!0x1;if(!0x0===_0x388b26[_0x288688(0x721)]&&_0x8f2edd!==_0x1bb78d-0x1)return!0x1;if(void 0x0!==_0x388b26['id']&&_0x388b26['id']===_0x39b4d2['id'])return!0x0;if(void 0x0!==_0x388b26[_0x288688(0x2013)]){if(_0x388b26[_0x288688(0x3903)])return _0x388b26['word']===_0x39b4d2[_0x388b26[_0x288688(0x3903)]];if(null!==_0x39b4d2[_0x288688(0x194)]&&_0x39b4d2[_0x288688(0x194)]===_0x388b26[_0x288688(0x2013)])return!0x0;if(void 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_0x26aa40=_0x235866;if(!0x0===_0xbbbd3e[_0x26aa40(0x721)]&&!0x0===_0xbbbd3e[_0x26aa40(0x188a)]&&_0x1b4ed8[_0x26aa40(0x3ef4)]+_0x1b4ed8['t']<_0x1b4ed8['phrase_length']-0x1){let _0x539687=Object['assign']({},_0xbbbd3e,{'end':!0x1});if(!0x0===_0x4dea73(_0x1b4ed8['terms'][_0x1b4ed8['t']],_0x539687,_0x1b4ed8['start_i']+_0x1b4ed8['t'],_0x1b4ed8[_0x26aa40(0xe13)]))return!0x0;}return!0x1;},_0x42da34=function(_0x3a6c77,_0x5770fb){const _0x3a2969=_0x235866;return _0x3a6c77[_0x3a2969(0xbbd)][_0x3a6c77[_0x3a2969(0xa7a)]]||(_0x3a6c77['groups'][_0x3a6c77[_0x3a2969(0xa7a)]]={'start':_0x5770fb,'length':0x0}),_0x3a6c77[_0x3a2969(0xbbd)][_0x3a6c77[_0x3a2969(0xa7a)]];},_0x11edc3=function(_0x2f4c9a){const _0x380003=_0x235866;let {regs:_0x3e4add}=_0x2f4c9a,_0x4e4880=_0x3e4add[_0x2f4c9a['r']],_0x1c9bcd=function(_0x49abf0,_0x4e765d){const _0xa6d118=a0_0x329b;let _0x476823=_0x49abf0['t'];if(!_0x4e765d)return 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_0x5ccc40=0x0;_0x5ccc40<_0x5ba639[_0x590931(0x2607)][_0x590931(0x205b)];_0x5ccc40+=0x1){let _0x20aa45=_0x5ba639[_0x590931(0x2607)][_0x5ccc40];if(_0x5284cf=_0x20aa45,_0x590931(0x4fba)!==Object['prototype']['toString'][_0x590931(0x4c9d)](_0x5284cf))return!0x1;if(_0xe533df=_0x20aa45[_0x590931(0x464d)]((_0x50e3c6,_0x136729)=>{const _0x179c2c=_0x590931;let _0x2033a4=0x0,_0x24dfa5=_0x563328['t']+_0x136729+_0x1c9697+_0x2033a4;if(void 0x0===_0x563328[_0x179c2c(0x18a5)][_0x24dfa5])return!0x1;let _0x338c73=_0x4dea73(_0x563328[_0x179c2c(0x18a5)][_0x24dfa5],_0x50e3c6,_0x24dfa5+_0x563328['start_i'],_0x563328[_0x179c2c(0xe13)]);if(!0x0===_0x338c73&&!0x0===_0x50e3c6[_0x179c2c(0x188a)])for(let _0x3c91d8=0x1;_0x3c91d8<_0x563328[_0x179c2c(0x18a5)][_0x179c2c(0x205b)];_0x3c91d8+=0x1){let _0xecffa5=_0x563328[_0x179c2c(0x18a5)][_0x24dfa5+_0x3c91d8];if(_0xecffa5){if(!0x0!==_0x4dea73(_0xecffa5,_0x50e3c6,_0x563328[_0x179c2c(0x3ef4)]+_0x3c91d8,_0x563328[_0x179c2c(0xe13)]))break;_0x2033a4+=0x1;}}return _0x1c9697+=_0x2033a4,_0x338c73;}),_0xe533df){_0x1c9697+=_0x20aa45[_0x590931(0x205b)];break;}}var _0x5284cf;return _0xe533df&&!0x0===_0x5ba639[_0x590931(0x188a)]?_0x26a92d(_0x563328,_0x1c9697):_0x1c9697;},_0x1f2512=function(_0x4af8f7){const _0x303760=_0x235866,{regs:_0x1a3019}=_0x4af8f7;let _0x292864=_0x1a3019[_0x4af8f7['r']],_0x3be85d=_0x26a92d(_0x4af8f7);if(_0x3be85d){if(!0x0===_0x292864['negative'])return null;!0x0===_0x4af8f7[_0x303760(0x1d68)]&&(_0x42da34(_0x4af8f7,_0x4af8f7['t'])[_0x303760(0x205b)]+=_0x3be85d);if(!0x0===_0x292864['end']){let _0x11f5c0=_0x4af8f7[_0x303760(0xe13)];if(_0x4af8f7['t']+_0x4af8f7[_0x303760(0x3ef4)]+_0x3be85d!==_0x11f5c0)return null;}return _0x4af8f7['t']+=_0x3be85d,!0x0;}return!!_0x292864[_0x303760(0x180e)]||null;},_0x200da2=function(_0x5b69ca){const _0x429ada=_0x235866,{regs:_0x53fb19}=_0x5b69ca;let _0x7ba0a0=_0x53fb19[_0x5b69ca['r']],_0x2169c6=function(_0x435c8f){const _0x48b0b1=a0_0x329b;let _0x37dee5=0x0,_0x152d94=_0x435c8f[_0x48b0b1(0x4176)][_0x435c8f['r']]['choices'][_0x48b0b1(0x464d)](_0x1d96f9=>{const _0x16489b=_0x48b0b1;let _0x35eabd=_0x1d96f9[_0x16489b(0x464d)]((_0x2ff9a0,_0x39e727)=>{const _0x3c726c=_0x16489b;let _0x46fedf=_0x435c8f['t']+_0x39e727;return void 0x0!==_0x435c8f[_0x3c726c(0x18a5)][_0x46fedf]&&_0x4dea73(_0x435c8f[_0x3c726c(0x18a5)][_0x46fedf],_0x2ff9a0,_0x46fedf,_0x435c8f[_0x3c726c(0xe13)]);});return!0x0===_0x35eabd&&_0x1d96f9[_0x16489b(0x205b)]>_0x37dee5&&(_0x37dee5=_0x1d96f9[_0x16489b(0x205b)]),_0x35eabd;});return!0x0===_0x152d94&&_0x37dee5;}(_0x5b69ca);if(_0x2169c6){if(!0x0===_0x7ba0a0[_0x429ada(0x3fbe)])return null;!0x0===_0x5b69ca[_0x429ada(0x1d68)]&&(_0x42da34(_0x5b69ca,_0x5b69ca['t'])[_0x429ada(0x205b)]+=_0x2169c6);if(!0x0===_0x7ba0a0['end']){let _0x58d06d=_0x5b69ca[_0x429ada(0xe13)]-0x1;if(_0x5b69ca['t']+_0x5b69ca['start_i']!==_0x58d06d)return null;}return _0x5b69ca['t']+=_0x2169c6,!0x0;}return!!_0x7ba0a0[_0x429ada(0x180e)]||null;},_0x30270a=function(_0x7f1dab,_0x3d61c4,_0x20305f){const _0x58f455=_0x235866;let _0x46edb9=0x0;for(let _0x40f678=_0x7f1dab['t'];_0x40f678<_0x7f1dab[_0x58f455(0x18a5)][_0x58f455(0x205b)];_0x40f678+=0x1){let _0x5eac96=_0x4dea73(_0x7f1dab['terms'][_0x40f678],_0x3d61c4,_0x7f1dab[_0x58f455(0x3ef4)]+_0x7f1dab['t'],_0x7f1dab[_0x58f455(0xe13)]);if(_0x5eac96)break;if(_0x20305f&&(_0x5eac96=_0x4dea73(_0x7f1dab['terms'][_0x40f678],_0x20305f,_0x7f1dab['start_i']+_0x7f1dab['t'],_0x7f1dab[_0x58f455(0xe13)]),_0x5eac96))break;if(_0x46edb9+=0x1,void 0x0!==_0x3d61c4['max']&&_0x46edb9===_0x3d61c4[_0x58f455(0x28c)])break;}return 0x0!==_0x46edb9&&(!(_0x3d61c4['min']&&_0x3d61c4[_0x58f455(0x12f9)]>_0x46edb9)&&(_0x7f1dab['t']+=_0x46edb9,!0x0));},_0x4190c2=function(_0x1645d5){const _0x26a81c=_0x235866,{regs:_0x332123}=_0x1645d5;let _0x15a855=_0x332123[_0x1645d5['r']],_0x41aa65=Object[_0x26a81c(0x11e8)]({},_0x15a855);if(_0x41aa65[_0x26a81c(0x3fbe)]=!0x1,_0x4dea73(_0x1645d5[_0x26a81c(0x18a5)][_0x1645d5['t']],_0x41aa65,_0x1645d5['start_i']+_0x1645d5['t'],_0x1645d5[_0x26a81c(0xe13)]))return!0x1;if(_0x15a855[_0x26a81c(0x180e)]){let _0x477fcb=_0x332123[_0x1645d5['r']+0x1];if(_0x477fcb){if(_0x4dea73(_0x1645d5[_0x26a81c(0x18a5)][_0x1645d5['t']],_0x477fcb,_0x1645d5[_0x26a81c(0x3ef4)]+_0x1645d5['t'],_0x1645d5[_0x26a81c(0xe13)]))_0x1645d5['r']+=0x1;else _0x477fcb[_0x26a81c(0x180e)]&&_0x332123[_0x1645d5['r']+0x2]&&(_0x4dea73(_0x1645d5['terms'][_0x1645d5['t']],_0x332123[_0x1645d5['r']+0x2],_0x1645d5[_0x26a81c(0x3ef4)]+_0x1645d5['t'],_0x1645d5[_0x26a81c(0xe13)])&&(_0x1645d5['r']+=0x2));}}return _0x15a855[_0x26a81c(0x188a)]?_0x30270a(_0x1645d5,_0x41aa65,_0x332123[_0x1645d5['r']+0x1]):(_0x1645d5['t']+=0x1,!0x0);},_0x534e50=function(_0x5144ca){const _0x1cedbc=_0x235866,{regs:_0x134cc7}=_0x5144ca;let _0x37579d=_0x134cc7[_0x5144ca['r']],_0x5ac269=_0x5144ca[_0x1cedbc(0x18a5)][_0x5144ca['t']],_0x502ca6=_0x4dea73(_0x5ac269,_0x134cc7[_0x5144ca['r']+0x1],_0x5144ca[_0x1cedbc(0x3ef4)]+_0x5144ca['t'],_0x5144ca[_0x1cedbc(0xe13)]);if(_0x37579d['negative']||_0x502ca6){let _0x196e71=_0x5144ca[_0x1cedbc(0x18a5)][_0x5144ca['t']+0x1];_0x196e71&&_0x4dea73(_0x196e71,_0x134cc7[_0x5144ca['r']+0x1],_0x5144ca[_0x1cedbc(0x3ef4)]+_0x5144ca['t'],_0x5144ca[_0x1cedbc(0xe13)])||(_0x5144ca['r']+=0x1);}},_0x3ec437=function(_0x49adc5){const _0x1af436=_0x235866,{regs:_0x5a5be7,phrase_length:_0x43db79}=_0x49adc5;let _0x25ba1e=_0x5a5be7[_0x49adc5['r']];return _0x49adc5['t']=function(_0x4434ea,_0x149317){const _0x8bbc26=a0_0x329b;let _0x25dbfc=Object['assign']({},_0x4434ea[_0x8bbc26(0x4176)][_0x4434ea['r']],{'start':!0x1,'end':!0x1}),_0x8c82b2=_0x4434ea['t'];for(;_0x4434ea['t']<_0x4434ea['terms'][_0x8bbc26(0x205b)];_0x4434ea['t']+=0x1){if(_0x149317&&_0x4dea73(_0x4434ea['terms'][_0x4434ea['t']],_0x149317,_0x4434ea[_0x8bbc26(0x3ef4)]+_0x4434ea['t'],_0x4434ea[_0x8bbc26(0xe13)]))return _0x4434ea['t'];let _0x5d0cc5=_0x4434ea['t']-_0x8c82b2+0x1;if(void 0x0!==_0x25dbfc[_0x8bbc26(0x28c)]&&_0x5d0cc5===_0x25dbfc['max'])return _0x4434ea['t'];if(!0x1===_0x4dea73(_0x4434ea[_0x8bbc26(0x18a5)][_0x4434ea['t']],_0x25dbfc,_0x4434ea['start_i']+_0x4434ea['t'],_0x4434ea['phrase_length']))return void 0x0!==_0x25dbfc[_0x8bbc26(0x12f9)]&&_0x5d0cc5<_0x25dbfc['min']?null:_0x4434ea['t'];}return _0x4434ea['t'];}(_0x49adc5,_0x5a5be7[_0x49adc5['r']+0x1]),null===_0x49adc5['t']||_0x25ba1e['min']&&_0x25ba1e['min']>_0x49adc5['t']?null:!0x0!==_0x25ba1e['end']||_0x49adc5[_0x1af436(0x3ef4)]+_0x49adc5['t']===_0x43db79||null;},_0x3b1d6a=function(_0xfe2a14){const _0x12be92=_0x235866;let _0x44643f=_0xfe2a14[_0x12be92(0x18a5)][_0xfe2a14['t']],_0x156b18=_0xfe2a14[_0x12be92(0x4176)][_0xfe2a14['r']];if(_0x44643f['implicit']&&_0xfe2a14['terms'][_0xfe2a14['t']+0x1]){if(!_0xfe2a14[_0x12be92(0x18a5)][_0xfe2a14['t']+0x1]['implicit'])return;_0x156b18[_0x12be92(0x2013)]===_0x44643f[_0x12be92(0x4cb)]&&(_0xfe2a14['t']+=0x1),_0x12be92(0x2043)===_0x156b18[_0x12be92(0xd7c)]&&(_0xfe2a14['t']+=0x1);}},_0x22354c=function(_0x4f7729){const _0x591b38=_0x235866,{regs:_0x506a2d}=_0x4f7729;let _0xa7a396=_0x506a2d[_0x4f7729['r']],_0x4180d1=_0x4f7729[_0x591b38(0x18a5)][_0x4f7729['t']],_0x57a2b5=_0x4f7729['t'];if(_0xa7a396[_0x591b38(0x180e)]&&_0x506a2d[_0x4f7729['r']+0x1]&&_0xa7a396[_0x591b38(0x3fbe)])return!0x0;if(_0xa7a396[_0x591b38(0x180e)]&&_0x506a2d[_0x4f7729['r']+0x1]&&_0x534e50(_0x4f7729),_0x4180d1[_0x591b38(0x15c0)]&&_0x4f7729[_0x591b38(0x18a5)][_0x4f7729['t']+0x1]&&_0x3b1d6a(_0x4f7729),_0x4f7729['t']+=0x1,!0x0===_0xa7a396[_0x591b38(0x721)]&&_0x4f7729['t']!==_0x4f7729[_0x591b38(0x18a5)][_0x591b38(0x205b)]&&!0x0!==_0xa7a396[_0x591b38(0x188a)])return null;if(!0x0===_0xa7a396[_0x591b38(0x188a)]){if(!_0x3ec437(_0x4f7729))return null;}return!0x0===_0x4f7729[_0x591b38(0x1d68)]&&function(_0x5398ac,_0x746461){const _0x56ba7f=_0x591b38;let _0x4d5f2d=_0x5398ac[_0x56ba7f(0x4176)][_0x5398ac['r']];const _0xde3103=_0x42da34(_0x5398ac,_0x746461);_0x5398ac['t']>0x1&&_0x4d5f2d[_0x56ba7f(0x188a)]?_0xde3103[_0x56ba7f(0x205b)]+=_0x5398ac['t']-_0x746461:_0xde3103[_0x56ba7f(0x205b)]++;}(_0x4f7729,_0x57a2b5),!0x0;},_0x109d1d=function(_0x17d862,_0x11b634,_0x5a2671,_0x2f0aea){const _0x38bd24=_0x235866;if(0x0===_0x17d862[_0x38bd24(0x205b)]||0x0===_0x11b634[_0x38bd24(0x205b)])return null;let _0x46e3da={'t':0x0,'terms':_0x17d862,'r':0x0,'regs':_0x11b634,'groups':{},'start_i':_0x5a2671,'phrase_length':_0x2f0aea,'inGroup':null};for(;_0x46e3da['r']<_0x11b634[_0x38bd24(0x205b)];_0x46e3da['r']+=0x1){let _0x3a2485=_0x11b634[_0x46e3da['r']];if(_0x46e3da[_0x38bd24(0x1d68)]=Boolean(_0x3a2485[_0x38bd24(0x4941)]),!0x0===_0x46e3da[_0x38bd24(0x1d68)]?_0x46e3da[_0x38bd24(0xa7a)]=_0x3a2485[_0x38bd24(0x4941)]:_0x46e3da[_0x38bd24(0xa7a)]=null,!_0x46e3da[_0x38bd24(0x18a5)][_0x46e3da['t']]){if(!0x1===_0x11b634[_0x38bd24(0x428e)](_0x46e3da['r'])['some'](_0x23fdc8=>!_0x23fdc8[_0x38bd24(0x180e)]))break;return null;}if(!0x0!==_0x3a2485[_0x38bd24(0x42e3)]||!0x0!==_0x3a2485[_0x38bd24(0x188a)]){if(void 0x0===_0x3a2485[_0x38bd24(0x2607)]||'or'!==_0x3a2485[_0x38bd24(0x5d3)]){if(void 0x0===_0x3a2485[_0x38bd24(0x2607)]||_0x38bd24(0x43ea)!==_0x3a2485[_0x38bd24(0x5d3)]){if(!0x0!==_0x3a2485[_0x38bd24(0x42e3)]){if(!0x0!==_0x1836a6(_0x3a2485,_0x46e3da)){if(_0x3a2485[_0x38bd24(0x3fbe)]){if(!_0x4190c2(_0x46e3da))return null;}else{if(!0x0!==_0x4dea73(_0x46e3da['terms'][_0x46e3da['t']],_0x3a2485,_0x46e3da[_0x38bd24(0x3ef4)]+_0x46e3da['t'],_0x46e3da[_0x38bd24(0xe13)])){if(!0x0!==_0x3a2485['optional'])return null;}else{if(!_0x22354c(_0x46e3da))return null;}}}else{if(!_0x22354c(_0x46e3da))return null;}}else{if(_0x3a2485[_0x38bd24(0x3fbe)]&&_0x3a2485[_0x38bd24(0x42e3)])return null;if(!_0x22354c(_0x46e3da))return null;}}else{if(!_0x200da2(_0x46e3da))return null;}}else{if(!_0x1f2512(_0x46e3da))return null;}}else{if(!_0x11edc3(_0x46e3da))return null;}}let 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_0x37ae4a[_0x2bd5de(0x4116)][_0x2bd5de(0x1d56)][_0x3c8bac]:_0x37ae4a[_0x2bd5de(0x4116)][_0x2bd5de(0x1d56)][_0x3c8bac]=_0x25766a[_0x3c8bac];}),this;}},_0x646b0f={'model':{'one':{'typeahead':{}}},'api':_0x317e3f,'lib':_0x2a531f,'compute':_0x5a6ce2,'hooks':[_0x235866(0x1d56)]};_0x5d20a7['extend'](_0x40ef9a),_0x5d20a7[_0x235866(0x389d)](_0x1e9079),_0x5d20a7['extend'](_0x5c7b74),_0x5d20a7[_0x235866(0x389d)](_0x282ee5),_0x5d20a7[_0x235866(0x389d)](_0x557f42),_0x5d20a7[_0x235866(0x4c30)](_0x1876cc),_0x5d20a7[_0x235866(0x389d)](_0x4fccbd),_0x5d20a7[_0x235866(0x389d)](_0x4cb09b),_0x5d20a7[_0x235866(0x4c30)](_0x30866c),_0x5d20a7[_0x235866(0x389d)](_0x2fa8bb),_0x5d20a7[_0x235866(0x389d)](_0x646b0f),_0x5d20a7[_0x235866(0x389d)](_0x1ec109),_0x5d20a7[_0x235866(0x389d)](_0x4539be);const _0x3a5c82=_0x5d20a7,_0x49642c={'addendum':_0x235866(0x4030),'corpus':'corpora','criterion':_0x235866(0x1651),'curriculum':'curricula','genus':_0x235866(0x40ae),'memorandum':'memoranda','opus':'opera','ovum':_0x235866(0x48ea),'phenomenon':_0x235866(0x978),'referendum':'referenda','alga':_0x235866(0xc16),'alumna':'alumnae','antenna':_0x235866(0x4e03),'formula':_0x235866(0x235f),'larva':'larvae','nebula':'nebulae','vertebra':_0x235866(0x4d35),'analysis':_0x235866(0x490c),'axis':_0x235866(0x187d),'diagnosis':_0x235866(0x286e),'parenthesis':'parentheses','prognosis':_0x235866(0x2f28),'synopsis':'synopses','thesis':_0x235866(0x613),'neurosis':_0x235866(0xf8b),'appendix':_0x235866(0x3cf0),'index':'indices','matrix':_0x235866(0x17ef),'ox':_0x235866(0x1f78),'sex':_0x235866(0x2e7),'alumnus':_0x235866(0x2ee8),'bacillus':_0x235866(0x467e),'cactus':_0x235866(0x4c48),'fungus':_0x235866(0x38dc),'hippopotamus':_0x235866(0x2796),'libretto':'libretti','modulus':_0x235866(0x2b19),'nucleus':_0x235866(0x2009),'octopus':'octopi','radius':'radii','stimulus':'stimuli','syllabus':_0x235866(0x2158),'cookie':_0x235866(0x699),'calorie':_0x235866(0x5119),'auntie':_0x235866(0x28e0),'movie':_0x235866(0x1b90),'pie':_0x235866(0x129e),'rookie':_0x235866(0x4e67),'tie':'ties','zombie':_0x235866(0x9c8),'leaf':_0x235866(0x2d40),'loaf':_0x235866(0x3dd2),'thief':_0x235866(0x375f),'foot':_0x235866(0x6b7),'goose':'geese','tooth':_0x235866(0x3545),'beau':_0x235866(0x38e2),'chateau':'chateaux','tableau':_0x235866(0x39a7),'bus':_0x235866(0x2d62),'gas':_0x235866(0xc3c),'circus':_0x235866(0x2813),'crisis':_0x235866(0x3fcc),'virus':_0x235866(0x34cb),'database':_0x235866(0x46a3),'excuse':'excuses','abuse':_0x235866(0x2469),'avocado':'avocados','barracks':_0x235866(0x1a83),'child':_0x235866(0x4538),'clothes':'clothes','echo':'echoes','embargo':_0x235866(0x2341),'epoch':_0x235866(0x2150),'deer':_0x235866(0x3a03),'halo':_0x235866(0x329),'man':_0x235866(0x356c),'woman':_0x235866(0x177e),'mosquito':_0x235866(0x5006),'mouse':_0x235866(0x22f9),'person':'people','quiz':'quizzes','rodeo':_0x235866(0x3875),'shoe':_0x235866(0x24f),'sombrero':_0x235866(0x3515),'stomach':'stomachs','tornado':_0x235866(0x2587),'tuxedo':_0x235866(0x19c4),'volcano':_0x235866(0x1d62)},_0x21316d={'Comparative':_0x235866(0x3b3b),'Superlative':_0x235866(0xb5f),'PresentTense':'true¦bests,sounds','Condition':_0x235866(0xae4),'PastTense':_0x235866(0x43fe),'Participle':_0x235866(0x1f41),'Gerund':'true¦accord0be0doin,go0result0stain0;ing','Expression':_0x235866(0x4f4f),'Negative':'true¦n0;ever,o0;n,t','QuestionWord':_0x235866(0x2a8a),'Reflexive':_0x235866(0x4a83),'Plural':_0x235866(0x4ddd),'Unit|Noun':'true¦cEfDgChBinchAk9lb,m6newt5oz,p4qt,t1y0;ardEd;able1b0ea1sp;!l,sp;spo1;a,t,x;on9;!b,g,i1l,m,p0;h,s;!les;!b,elvin,g,m;!es;g,z;al,b;eet,oot,t;m,up0;!s','Value':'true¦a\x20few','Imperative':_0x235866(0x118),'Plural|Verb':_0x235866(0x5c1),'Demonym':'true¦0:15;1:12;a0Vb0Oc0Dd0Ce08f07g04h02iYjVkTlPmLnIomHpEqatari,rCs7t5u4v3welAz2;am0Gimbabwe0;enezuel0ietnam0I;gAkrai1;aiwTex0hai,rinida0Ju2;ni0Prkmen;a5cotti4e3ingapoOlovak,oma0Spaniard,udRw2y0W;ede,iss;negal0Cr09;sh;mo0uT;o5us0Jw2;and0;a2eru0Fhilippi0Nortugu07uerto\x20r0S;kist3lesti1na2raguay0;ma1;ani;ami00i2orweP;caragu0geri2;an,en;a3ex0Lo2;ngo0Drocc0;cedo1la2;gasy,y07;a4eb9i2;b2thua1;e0Cy0;o,t01;azakh,eny0o2uwaiI;re0;a2orda1;ma0Ap2;anO;celandic,nd4r2sraeli,ta01vo05;a2iB;ni0qi;i0oneU;aiAin2ondur0unO;di;amEe2hanai0reek,uatemal0;or2rm0;gi0;ilipino,ren8;cuadoVgyp4mira3ngli2sto1thiopi0urope0;shm0;ti;ti0;aPominUut3;a9h6o4roat3ub0ze2;ch;!i0;lom2ngol5;bi0;a6i2;le0n2;ese;lifor1m2na3;bo2eroo1;di0;angladeshi,el6o4r3ul2;gaE;azi9it;li2s1;vi0;aru2gi0;si0;fAl7merBngol0r5si0us2;sie,tr2;a2i0;li0;genti2me1;ne;ba1ge2;ri0;ni0;gh0r2;ic0;an','Organization':_0x235866(0x4d44),'Possessive':'true¦its,my,our0thy;!s','Noun|Verb':'true¦0:9W;1:AA;2:96;3:A3;4:9R;5:A2;6:9K;7:8N;8:7L;9:A8;A:93;B:8D;C:8X;a9Ob8Qc7Id6Re6Gf5Sg5Hh55i4Xj4Uk4Rl4Em40n3Vo3Sp2Squ2Rr21s0Jt02u00vVwGyFzD;ip,oD;ne,om;awn,e6Fie68;aOeMhJiHoErD;ap,e9Oink2;nd0rDuC;kDry,sh5Hth;!shop;ck,nDpe,re,sh;!d,g;e86iD;p,sD;k,p0t2;aDed,lco8W;r,th0;it,lk,rEsDt4ve,x;h,te;!ehou1ra9;aGen5FiFoD;iDmAte,w;ce,d;be,ew,sA;cuum,l4B;pDr7;da5gra6Elo6A;aReQhrPiOoMrGuEwiDy5Z;n,st;nDrn;e,n7O;aGeFiEoDu6;t,ub2;bu5ck4Jgg0m,p;at,k,nd;ck,de,in,nsDp,v7J;f0i8R;ll,ne,p,r4Yss,t94uD;ch,r;ck,de,e,le,me,p,re;e5Wow,u6;ar,e,ll,mp0st,xt;g,lDng2rg7Ps5x;k,ly;a0Sc0Ne0Kh0Fi0Dk0Cl0Am08n06o05pXquaBtKuFwD;ea88iD;ng,pe,t4;bGit,m,ppErD;fa3ge,pri1v2U;lDo6S;e6Py;!je8;aMeLiKoHrEuDy2;dy,ff,mb2;a85eEiDo5Pugg2;ke,ng;am,ss,t4;ckEop,p,rD;e,m;ing,pi2;ck,nk,t4;er,m,p;ck,ff,ge,in,ke,lEmp,nd,p2rDte,y;!e,t;k,l;aJeIiHlGoFrDur,y;ay,e56inDu3;g,k2;ns8Bt;a5Qit;ll,n,r87te;ed,ll;m,n,rk;b,uC;aDee1Tow;ke,p;a5Je4FiDo53;le,rk;eep,iDou4;ce,p,t;ateboa7Ii;de,gnDl2Vnk,p,ze;!al;aGeFiEoDuff2;ck,p,re,w;ft,p,v0;d,i3Ylt0;ck,de,pe,re,ve;aEed,nDrv1It;se,t2N;l,r4t;aGhedu2oBrD;aEeDibb2o3Z;en,w;pe,t4;le,n,r2M;cDfegua72il,mp2;k,rifi3;aZeHhy6LiGoEuD;b,in,le,n,s5X;a6ck,ll,oDpe,u5;f,t;de,ng,ot,p,s1W;aTcSdo,el,fQ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0x0,'as':'Adjective'})},_0x438ab5={'Copula':_0x235866(0x1e19),'PastTense':'Gerund','PresentTense':_0x235866(0x1e19),'Infinitive':'Gerund'},_0x370d31={'Value':_0x235866(0x1e19)},_0x639b83={'are':'Gerund','were':'Gerund','be':'Gerund','no':'Gerund','without':_0x235866(0x1e19),'you':_0x235866(0x1e19),'we':_0x235866(0x1e19),'they':'Gerund','he':_0x235866(0x1e19),'she':_0x235866(0x1e19),'us':'Gerund','them':'Gerund'},_0x2554f3={'the':'Gerund','this':_0x235866(0x1e19),'that':_0x235866(0x1e19),'me':'Gerund','us':_0x235866(0x1e19),'them':_0x235866(0x1e19)},_0x372b7e={'beforeTags':Object[_0x235866(0x11e8)]({},_0x97a0c4['beforeTags'],_0x86b88f[_0x235866(0x4142)],_0x438ab5),'afterTags':Object[_0x235866(0x11e8)]({},_0x97a0c4[_0x235866(0x3916)],_0x86b88f[_0x235866(0x3916)],_0x370d31),'beforeWords':Object['assign']({},_0x97a0c4[_0x235866(0x4af9)],_0x86b88f['beforeWords'],_0x639b83),'afterWords':Object[_0x235866(0x11e8)]({},_0x97a0c4[_0x235866(0x298f)],_0x86b88f['afterWords'],_0x2554f3)},_0x2ea2b6=_0x235866(0x3faa),_0x195c45=_0x235866(0x24ba),_0x4c7ecd={'beforeTags':Object[_0x235866(0x11e8)]({},_0x12f830[_0x235866(0x4142)],_0x86b88f[_0x235866(0x4142)],{'Adjective':_0x2ea2b6,'Particle':_0x2ea2b6}),'afterTags':Object[_0x235866(0x11e8)]({},_0x12f830['afterTags'],_0x86b88f[_0x235866(0x3916)],{'ProperNoun':_0x195c45,'Gerund':_0x195c45,'Adjective':_0x195c45,'Copula':_0x2ea2b6}),'beforeWords':Object[_0x235866(0x11e8)]({},_0x12f830[_0x235866(0x4af9)],_0x86b88f['beforeWords'],{'is':_0x2ea2b6,'was':_0x2ea2b6,'of':_0x2ea2b6,'have':null}),'afterWords':Object[_0x235866(0x11e8)]({},_0x12f830[_0x235866(0x298f)],_0x86b88f[_0x235866(0x298f)],{'instead':_0x195c45,'about':_0x195c45,'his':_0x195c45,'her':_0x195c45,'to':null,'by':null,'in':null})},_0x1512dc=_0x235866(0x29bf),_0x39fec2={'beforeTags':{'Honorific':_0x1512dc,'Person':_0x1512dc},'afterTags':{'Person':_0x1512dc,'ProperNoun':_0x1512dc,'Verb':_0x1512dc},'ownTags':{'ProperNoun':_0x1512dc},'beforeWords':{'hi':_0x1512dc,'hey':_0x1512dc,'yo':_0x1512dc,'dear':_0x1512dc,'hello':_0x1512dc},'afterWords':{'said':_0x1512dc,'says':_0x1512dc,'told':_0x1512dc,'tells':_0x1512dc,'feels':_0x1512dc,'felt':_0x1512dc,'seems':_0x1512dc,'thinks':_0x1512dc,'thought':_0x1512dc,'spends':_0x1512dc,'spendt':_0x1512dc,'plays':_0x1512dc,'played':_0x1512dc,'sing':_0x1512dc,'sang':_0x1512dc,'learn':_0x1512dc,'learned':_0x1512dc,'wants':_0x1512dc,'wanted':_0x1512dc}},_0x586b91='Month',_0x533d43={'beforeTags':{'Date':_0x586b91,'Value':_0x586b91},'afterTags':{'Date':_0x586b91,'Value':_0x586b91},'beforeWords':{'by':_0x586b91,'in':_0x586b91,'on':_0x586b91,'during':_0x586b91,'after':_0x586b91,'before':_0x586b91,'between':_0x586b91,'until':_0x586b91,'til':_0x586b91,'sometime':_0x586b91,'of':_0x586b91,'this':_0x586b91,'next':_0x586b91,'last':_0x586b91,'previous':_0x586b91,'following':_0x586b91,'with':_0x235866(0x29bf)},'afterWords':{'sometime':_0x586b91,'in':_0x586b91,'of':_0x586b91,'until':_0x586b91,'the':_0x586b91}},_0x40f3ee={'beforeTags':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x4142)],_0x533d43[_0x235866(0x4142)]),'afterTags':Object[_0x235866(0x11e8)]({},_0x39fec2['afterTags'],_0x533d43['afterTags']),'beforeWords':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x4af9)],_0x533d43[_0x235866(0x4af9)]),'afterWords':Object[_0x235866(0x11e8)]({},_0x39fec2['afterWords'],_0x533d43[_0x235866(0x298f)])},_0x18db53=_0x235866(0x3fae),_0x3ac8b4={'beforeTags':{'Place':_0x18db53},'afterTags':{'Place':_0x18db53,'Abbreviation':_0x18db53},'beforeWords':{'in':_0x18db53,'by':_0x18db53,'near':_0x18db53,'from':_0x18db53,'to':_0x18db53},'afterWords':{'in':_0x18db53,'by':_0x18db53,'near':_0x18db53,'from':_0x18db53,'to':_0x18db53,'government':_0x18db53,'council':_0x18db53,'region':_0x18db53,'city':_0x18db53}};let _0x2ffc35=_0x235866(0x283a);const _0x1011f9={'Actor|Verb':_0x18d6da,'Adj|Gerund':_0x16f949,'Adj|Noun':_0x1f6d91,'Adj|Past':_0x15973d,'Adj|Present':_0x3662a6,'Noun|Verb':_0x4c7ecd,'Noun|Gerund':_0x372b7e,'Person|Noun':{'beforeTags':Object['assign']({},_0x86b88f[_0x235866(0x4142)],_0x39fec2['beforeTags']),'afterTags':Object[_0x235866(0x11e8)]({},_0x86b88f[_0x235866(0x3916)],_0x39fec2[_0x235866(0x3916)]),'beforeWords':Object[_0x235866(0x11e8)]({},_0x86b88f[_0x235866(0x4af9)],_0x39fec2[_0x235866(0x4af9)],{'i':'Infinitive','we':_0x235866(0x24ba)}),'afterWords':Object[_0x235866(0x11e8)]({},_0x86b88f[_0x235866(0x298f)],_0x39fec2[_0x235866(0x298f)])},'Person|Date':_0x40f3ee,'Person|Verb':{'beforeTags':Object['assign']({},_0x86b88f[_0x235866(0x4142)],_0x39fec2[_0x235866(0x4142)],_0x12f830[_0x235866(0x4142)]),'afterTags':Object['assign']({},_0x86b88f[_0x235866(0x3916)],_0x39fec2[_0x235866(0x3916)],_0x12f830[_0x235866(0x3916)]),'beforeWords':Object[_0x235866(0x11e8)]({},_0x86b88f[_0x235866(0x4af9)],_0x39fec2[_0x235866(0x4af9)],_0x12f830[_0x235866(0x4af9)]),'afterWords':Object[_0x235866(0x11e8)]({},_0x86b88f[_0x235866(0x298f)],_0x39fec2[_0x235866(0x298f)],_0x12f830['afterWords'])},'Person|Place':{'beforeTags':Object[_0x235866(0x11e8)]({},_0x3ac8b4['beforeTags'],_0x39fec2[_0x235866(0x4142)]),'afterTags':Object[_0x235866(0x11e8)]({},_0x3ac8b4['afterTags'],_0x39fec2['afterTags']),'beforeWords':Object[_0x235866(0x11e8)]({},_0x3ac8b4['beforeWords'],_0x39fec2[_0x235866(0x4af9)]),'afterWords':Object['assign']({},_0x3ac8b4[_0x235866(0x298f)],_0x39fec2[_0x235866(0x298f)])},'Person|Adj':{'beforeTags':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x4142)],_0x13cfe4['beforeTags']),'afterTags':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x3916)],_0x13cfe4[_0x235866(0x3916)]),'beforeWords':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x4af9)],_0x13cfe4[_0x235866(0x4af9)]),'afterWords':Object[_0x235866(0x11e8)]({},_0x39fec2[_0x235866(0x298f)],_0x13cfe4['afterWords'])},'Unit|Noun':{'beforeTags':{'Value':_0x2ffc35},'afterTags':{},'beforeWords':{'per':_0x2ffc35,'every':_0x2ffc35,'each':_0x2ffc35,'square':_0x2ffc35,'cubic':_0x2ffc35,'sq':_0x2ffc35,'metric':_0x2ffc35},'afterWords':{'per':_0x2ffc35,'squared':_0x2ffc35,'cubed':_0x2ffc35,'long':_0x2ffc35}}},_0x10041f=(_0x1296dc,_0x5ac83b)=>{const 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_0x5d2d7c=_0x191012,_0x4f573b=function(_0x10b599){const _0x106192=_0x235866;let _0x1c05c1=_0x10b599[_0x106192(0x3934)](_0x10b599[_0x106192(0x205b)]-0x3);if(!0x0===_0x5d2d7c[_0x106192(0x32b5)](_0x1c05c1))return _0x5d2d7c[_0x1c05c1];let _0x22f1bc=_0x10b599['substring'](_0x10b599['length']-0x2);return!0x0===_0x5d2d7c[_0x106192(0x32b5)](_0x22f1bc)?_0x5d2d7c[_0x22f1bc]:'s'===_0x10b599[_0x106192(0x3934)](_0x10b599[_0x106192(0x205b)]-0x1)?_0x106192(0x17a6):null;},_0x3d1df3={'are':'be','were':'be','been':'be','is':'be','am':'be','was':'be','be':'be','being':'be'},_0x3286d2=function(_0x14aa26,_0x20b9ca,_0x5f140b){const _0x3b956a=_0x235866,{fromPast:_0x7c22ad,fromPresent:_0x28d279,fromGerund:_0x467e60,fromParticiple:_0x148250}=_0x20b9ca[_0x3b956a(0x141f)][_0x3b956a(0x1526)];let {prefix:_0x1663ef,verb:_0x45cff0,particle:_0x1cf2b9}=function(_0x4cd474,_0x2aa22c){const _0x5cd0bf=_0x3b956a;let _0x421171='',_0x4ffa48={};_0x2aa22c['one']&&_0x2aa22c[_0x5cd0bf(0x4116)][_0x5cd0bf(0x447a)]&&(_0x4ffa48=_0x2aa22c[_0x5cd0bf(0x4116)][_0x5cd0bf(0x447a)]);let [_0x576823,_0x5a2677]=_0x4cd474[_0x5cd0bf(0x29d0)](/ /);return _0x5a2677&&!0x0===_0x4ffa48[_0x576823]&&(_0x421171=_0x576823,_0x576823=_0x5a2677,_0x5a2677=''),{'prefix':_0x421171,'verb':_0x576823,'particle':_0x5a2677};}(_0x14aa26,_0x20b9ca),_0x33e59c='';if(_0x5f140b||(_0x5f140b=_0x4f573b(_0x14aa26)),_0x3d1df3['hasOwnProperty'](_0x14aa26))_0x33e59c=_0x3d1df3[_0x14aa26];else{if(_0x3b956a(0x184e)===_0x5f140b)_0x33e59c=_0x3ab3c0(_0x45cff0,_0x148250);else{if('PastTense'===_0x5f140b)_0x33e59c=_0x3ab3c0(_0x45cff0,_0x7c22ad);else{if(_0x3b956a(0x17a6)===_0x5f140b)_0x33e59c=_0x3ab3c0(_0x45cff0,_0x28d279);else{if('Gerund'!==_0x5f140b)return _0x14aa26;_0x33e59c=_0x3ab3c0(_0x45cff0,_0x467e60);}}}}return 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_0xb36875=_0x235866;if(_0x2624b1[_0xb36875(0x32b5)](_0x52ff63))return _0x2624b1[_0x52ff63];let _0x5da2b0=_0x4ead73(_0x52ff63,_0x38076c);return _0x5da2b0||(_0x5da2b0=_0x52ff63+'ly'),_0x5da2b0;},_0x2fb634={'toSuperlative':_0x5f405f,'toComparative':_0x23f0ee,'toAdverb':_0x5ea3ed,'toNoun':function(_0x3d0d4f,_0x198089){const _0x2bc3c5=_0x235866,_0x2aaef3=_0x198089['two'][_0x2bc3c5(0x1526)]['adjToNoun'];return _0x3ab3c0(_0x3d0d4f,_0x2aaef3);},'fromAdverb':function(_0x3868db){const _0x7489a2=_0x235866;return _0x3868db[_0x7489a2(0xe37)]('ly')?_0x94cd99[_0x7489a2(0x5ec)](_0x3868db)?_0x3868db[_0x7489a2(0x1db7)](/ically/,_0x7489a2(0xa96)):_0x5eadc4[_0x7489a2(0x5ec)](_0x3868db)?null:_0x36c786[_0x7489a2(0x32b5)](_0x3868db)?_0x36c786[_0x3868db]:_0x4ead73(_0x3868db,_0x17e73b)||_0x3868db:null;},'fromSuperlative':function(_0x89989a,_0x14bd11){const _0xa9fe55=_0x235866,_0x1b9157=_0x14bd11[_0xa9fe55(0x141f)][_0xa9fe55(0x1526)]['fromSuperlative'];return _0x3ab3c0(_0x89989a,_0x1b9157);},'fromComparative':function(_0x332c1b,_0x34b890){const _0x2e636b=_0x235866,_0x4be40d=_0x34b890[_0x2e636b(0x141f)][_0x2e636b(0x1526)]['fromComparative'];return _0x3ab3c0(_0x332c1b,_0x4be40d);},'all':function(_0x49d072,_0x51cad2){const _0x59997e=_0x235866;let _0x535fd9=[_0x49d072];return _0x535fd9['push'](_0x5f405f(_0x49d072,_0x51cad2)),_0x535fd9[_0x59997e(0x4131)](_0x23f0ee(_0x49d072,_0x51cad2)),_0x535fd9['push'](_0x5ea3ed(_0x49d072)),_0x535fd9=_0x535fd9[_0x59997e(0x295e)](_0x1c4f88=>_0x1c4f88),_0x535fd9=new Set(_0x535fd9),Array[_0x59997e(0xc81)](_0x535fd9);}},_0x2b5576={'noun':_0x260fab,'verb':_0xaf65af,'adjective':_0x2fb634},_0x524c3a={'Singular':(_0x39b3d2,_0x4a95c2,_0x4b9602,_0x5e08ee)=>{const _0x2bc8ca=_0x235866;let _0x3bb817=_0x5e08ee['one'][_0x2bc8ca(0x4ab0)],_0x3e95cd=_0x4b9602[_0x2bc8ca(0x141f)]['transform'][_0x2bc8ca(0x3c45)]['toPlural'](_0x39b3d2,_0x5e08ee);_0x3bb817[_0x3e95cd]||(_0x4a95c2[_0x3e95cd]=_0x4a95c2[_0x3e95cd]||'Plural');},'Actor':(_0x32f2d8,_0x47f0ee,_0x4ef521,_0x289398)=>{const _0x31ddbf=_0x235866;let _0x20d99c=_0x289398[_0x31ddbf(0x4116)]['lexicon'],_0x53d8ae=_0x4ef521[_0x31ddbf(0x141f)][_0x31ddbf(0x560)][_0x31ddbf(0x3c45)]['toPlural'](_0x32f2d8,_0x289398);_0x20d99c[_0x53d8ae]||(_0x47f0ee[_0x53d8ae]=_0x47f0ee[_0x53d8ae]||[_0x31ddbf(0x27b3),'Actor']);},'Comparable':(_0x32f30a,_0x4ef281,_0x338bb0,_0x391fec)=>{const _0x1dd894=_0x235866;let _0x26a968=_0x391fec[_0x1dd894(0x4116)][_0x1dd894(0x4ab0)],{toSuperlative:_0x19fda3,toComparative:_0x1c8ca9}=_0x338bb0[_0x1dd894(0x141f)][_0x1dd894(0x560)][_0x1dd894(0x2c03)],_0x4eee94=_0x19fda3(_0x32f30a,_0x391fec);_0x26a968[_0x4eee94]||(_0x4ef281[_0x4eee94]=_0x4ef281[_0x4eee94]||'Superlative');let _0x3dbb94=_0x1c8ca9(_0x32f30a,_0x391fec);_0x26a968[_0x3dbb94]||(_0x4ef281[_0x3dbb94]=_0x4ef281[_0x3dbb94]||_0x1dd894(0x1ab0)),_0x4ef281[_0x32f30a]=_0x1dd894(0x19b5);},'Demonym':(_0xb6f413,_0x153c50,_0x27dcf7,_0x190aaa)=>{const _0x1ac720=_0x235866;let _0x4de29a=_0x27dcf7[_0x1ac720(0x141f)][_0x1ac720(0x560)][_0x1ac720(0x3c45)]['toPlural'](_0xb6f413,_0x190aaa);_0x153c50[_0x4de29a]=_0x153c50[_0x4de29a]||['Demonym',_0x1ac720(0x27b3)];},'Infinitive':(_0x80acdf,_0x2369f0,_0x16cee1,_0xe13084)=>{const _0x4d3eee=_0x235866;let _0x530f42=_0xe13084[_0x4d3eee(0x4116)]['lexicon'],_0x3a8b9a=_0x16cee1[_0x4d3eee(0x141f)][_0x4d3eee(0x560)][_0x4d3eee(0x91)]['conjugate'](_0x80acdf,_0xe13084);Object[_0x4d3eee(0x5088)](_0x3a8b9a)[_0x4d3eee(0x4854)](_0x5522a3=>{_0x530f42[_0x5522a3[0x1]]||_0x2369f0[_0x5522a3[0x1]]||'FutureTense'===_0x5522a3[0x0]||(_0x2369f0[_0x5522a3[0x1]]=_0x5522a3[0x0]);});},'PhrasalVerb':(_0xa1dc3d,_0x1d1dfe,_0x726e80,_0x1e16be)=>{const _0x1b3df6=_0x235866;let _0x117400=_0x1e16be['one']['lexicon'];_0x1d1dfe[_0xa1dc3d]=['PhrasalVerb',_0x1b3df6(0x24ba)];let _0x15f4a9=_0x1e16be[_0x1b3df6(0x4116)]['_multiCache'],[_0x559c10,_0x16f0ba]=_0xa1dc3d[_0x1b3df6(0x29d0)]('\x20');_0x117400[_0x559c10]||(_0x1d1dfe[_0x559c10]=_0x1d1dfe[_0x559c10]||_0x1b3df6(0x24ba));let _0x3ec043=_0x726e80[_0x1b3df6(0x141f)][_0x1b3df6(0x560)]['verb'][_0x1b3df6(0x431b)](_0x559c10,_0x1e16be);delete _0x3ec043['FutureTense'],Object[_0x1b3df6(0x5088)](_0x3ec043)[_0x1b3df6(0x4854)](_0x5eeb77=>{const _0x327175=_0x1b3df6;if(_0x327175(0x432f)===_0x5eeb77[0x0]||''===_0x5eeb77[0x1])return;_0x1d1dfe[_0x5eeb77[0x1]]||_0x117400[_0x5eeb77[0x1]]||(_0x1d1dfe[_0x5eeb77[0x1]]=_0x5eeb77[0x0]),_0x15f4a9[_0x5eeb77[0x1]]=0x2;let _0x121f78=_0x5eeb77[0x1]+'\x20'+_0x16f0ba;_0x1d1dfe[_0x121f78]=_0x1d1dfe[_0x121f78]||[_0x5eeb77[0x0],_0x327175(0x4634)];});},'Multiple':(_0x392f31,_0x188b2c)=>{const _0x3a59b5=_0x235866;_0x188b2c[_0x392f31]=[_0x3a59b5(0x4408),_0x3a59b5(0x2346)],_0x188b2c[_0x392f31+'th']=[_0x3a59b5(0x4408),_0x3a59b5(0x529)],_0x188b2c[_0x392f31+_0x3a59b5(0x376f)]=[_0x3a59b5(0x4408),_0x3a59b5(0x3793)];},'Cardinal':(_0xdf6afe,_0x13495d)=>{const _0x1a7cf1=_0x235866;_0x13495d[_0xdf6afe]=[_0x1a7cf1(0x20ed),_0x1a7cf1(0x2346)];},'Ordinal':(_0x173173,_0x2431da)=>{const _0x1c26f6=_0x235866;_0x2431da[_0x173173]=[_0x1c26f6(0x20ed),_0x1c26f6(0x529)],_0x2431da[_0x173173+'s']=['TextValue',_0x1c26f6(0x3793)];},'Place':(_0x5c85c4,_0x45831d)=>{const _0x356399=_0x235866;_0x45831d[_0x5c85c4]=[_0x356399(0x3fae),'ProperNoun'];},'Region':(_0x6bf997,_0x348802)=>{const _0x592577=_0x235866;_0x348802[_0x6bf997]=[_0x592577(0x4bf7),_0x592577(0x374d)];}},_0x4db76e=function(_0x3d83c5,_0x2fe61e){const _0x364c60=_0x235866,{methods:_0x254c3f,model:_0x227ad3}=_0x2fe61e;let _0x267ab8={},_0x333e3d={};return Object[_0x364c60(0x2f75)](_0x3d83c5)[_0x364c60(0x4854)](_0x5bb8d4=>{const _0x87daf6=_0x364c60;let _0x478b96=_0x3d83c5[_0x5bb8d4],_0x38227b=(_0x5bb8d4=(_0x5bb8d4=_0x5bb8d4['toLowerCase']()['trim']())[_0x87daf6(0x1db7)](/'s\b/,''))['split'](/ /);_0x38227b[_0x87daf6(0x205b)]>0x1&&(void 0x0===_0x333e3d[_0x38227b[0x0]]||_0x38227b[_0x87daf6(0x205b)]>_0x333e3d[_0x38227b[0x0]])&&(_0x333e3d[_0x38227b[0x0]]=_0x38227b[_0x87daf6(0x205b)]),!0x0===_0x524c3a[_0x87daf6(0x32b5)](_0x478b96)&&_0x524c3a[_0x478b96](_0x5bb8d4,_0x267ab8,_0x254c3f,_0x227ad3),_0x267ab8[_0x5bb8d4]=_0x267ab8[_0x5bb8d4]||_0x478b96;}),delete _0x267ab8[''],delete _0x267ab8['null'],delete _0x267ab8['\x20'],{'lex':_0x267ab8,'_multi':_0x333e3d};},_0x2454b6=function(_0x4dffb8){const _0x4305b0=_0x235866,_0x414b11=/[,:;]/;let _0x5f46c4=[];return _0x4dffb8[_0x4305b0(0x4854)](_0x5a83de=>{const _0x2a642e=_0x4305b0;let _0xbf71b=0x0;_0x5a83de[_0x2a642e(0x4854)]((_0x2a1b96,_0x3c787d)=>{const _0x4ab31e=_0x2a642e;_0x414b11[_0x4ab31e(0x34c)](_0x2a1b96[_0x4ab31e(0x9ce)])&&function(_0x99b8ba,_0xfd6fed){const _0x713297=_0x4ab31e,_0x5dd87b=/^[0-9]+$/;let _0x58fd66=_0x99b8ba[_0xfd6fed];if(!_0x58fd66)return!0x1;const _0x11790a=new Set(['may',_0x713297(0x3562),_0x713297(0x3943),_0x713297(0x40a4)]);if(_0x713297(0x1fb0)===_0x58fd66[_0x713297(0x4cb)]||_0x11790a[_0x713297(0x5ec)](_0x58fd66[_0x713297(0x4cb)]))return!0x1;if(_0x58fd66[_0x713297(0x23d1)]['has'](_0x713297(0x3fae))||_0x58fd66['tags'][_0x713297(0x5ec)]('Date'))return!0x1;if(_0x99b8ba[_0xfd6fed-0x1]){let _0x3d79bd=_0x99b8ba[_0xfd6fed-0x1];if(_0x3d79bd[_0x713297(0x23d1)][_0x713297(0x5ec)]('Date')||_0x11790a[_0x713297(0x5ec)](_0x3d79bd[_0x713297(0x4cb)]))return!0x1;if(_0x3d79bd['tags']['has']('Adjective')||_0x58fd66[_0x713297(0x23d1)][_0x713297(0x5ec)]('Adjective'))return!0x1;}let _0x2b5b02=_0x58fd66['normal'];return 0x1!==_0x2b5b02['length']&&0x2!==_0x2b5b02[_0x713297(0x205b)]&&0x4!==_0x2b5b02[_0x713297(0x205b)]||!_0x5dd87b[_0x713297(0x34c)](_0x2b5b02);}(_0x5a83de,_0x3c787d+0x1)&&(_0x5f46c4[_0x4ab31e(0x4131)](_0x5a83de[_0x4ab31e(0x428e)](_0xbf71b,_0x3c787d+0x1)),_0xbf71b=_0x3c787d+0x1);}),_0xbf71b<_0x5a83de[_0x2a642e(0x205b)]&&_0x5f46c4[_0x2a642e(0x4131)](_0x5a83de[_0x2a642e(0x428e)](_0xbf71b,_0x5a83de['length']));}),_0x5f46c4;},_0x22c2e5={'e':[_0x235866(0x22f9),_0x235866(0x2e75),'antennae',_0x235866(0x235f),'nebulae',_0x235866(0x4d35),_0x235866(0x3972)],'i':['tia','octopi',_0x235866(0x3b2e),_0x235866(0x1436),'nuclei',_0x235866(0x38dc),'cacti','stimuli'],'n':['men'],'t':[_0x235866(0x6b7)]},_0x26b4ca=new Set([_0x235866(0x1054),'menus',_0x235866(0x368)]),_0x4c29ed=[_0x235866(0x35e8),_0x235866(0x3156),'was',_0x235866(0x1bfc),_0x235866(0x4fe1),_0x235866(0x167d),_0x235866(0x4740),_0x235866(0x2135),_0x235866(0xb0a),'ois',_0x235866(0x1d3c),_0x235866(0x3d10),'tis',_0x235866(0x159),_0x235866(0x36b8),_0x235866(0x1d24),_0x235866(0x1127),_0x235866(0x3fc9),_0x235866(0x241e),_0x235866(0x25ae),'lus','nus',_0x235866(0x2726),_0x235866(0x41c5),_0x235866(0x4333),_0x235866(0x494d),_0x235866(0x2dbb),_0x235866(0x4e44),'xus','aos',_0x235866(0x354a),_0x235866(0x18e5),'ogos','\x27s','ss'],_0xdc5701=function(_0x3881ee){const _0x4903c9=_0x235866;if(!_0x3881ee||_0x3881ee[_0x4903c9(0x205b)]<=0x3)return!0x1;if(_0x26b4ca['has'](_0x3881ee))return!0x0;let _0x5e5264=_0x3881ee[_0x3881ee[_0x4903c9(0x205b)]-0x1];return _0x22c2e5[_0x4903c9(0x32b5)](_0x5e5264)?_0x22c2e5[_0x5e5264]['find'](_0x3dbb8c=>_0x3881ee[_0x4903c9(0xe37)](_0x3dbb8c)):'s'===_0x5e5264&&!_0x4c29ed[_0x4903c9(0x12b9)](_0x4ded79=>_0x3881ee['endsWith'](_0x4ded79));},_0x474f59={'two':{'quickSplit':_0x2454b6,'expandLexicon':_0x4db76e,'transform':_0x2b5576,'looksPlural':_0xdc5701}},_0x4eb4c2=function(_0x3e42c2){const _0x251d17=_0x235866,{irregularPlurals:_0xf14be2}=_0x3e42c2['two'],{lexicon:_0x5c9f02}=_0x3e42c2[_0x251d17(0x4116)];return Object[_0x251d17(0x5088)](_0xf14be2)['forEach'](_0x406245=>{const _0x4d0cc3=_0x251d17;_0x5c9f02[_0x406245[0x0]]=_0x5c9f02[_0x406245[0x0]]||_0x4d0cc3(0x3faa),_0x5c9f02[_0x406245[0x1]]=_0x5c9f02[_0x406245[0x1]]||'Plural';}),_0x3e42c2;};let _0x18c1f0={'one':{'lexicon':{}},'two':{'models':_0x4be2c6}};const _0x5813d8={'Actor|Verb':_0x235866(0x432f),'Adj|Gerund':_0x235866(0x19b5),'Adj|Noun':_0x235866(0x19b5),'Adj|Past':_0x235866(0x19b5),'Adj|Present':_0x235866(0x19b5),'Noun|Verb':_0x235866(0x3faa),'Noun|Gerund':'Gerund','Person|Noun':_0x235866(0x1ce5),'Person|Date':'Month','Person|Verb':_0x235866(0xcbd),'Person|Place':_0x235866(0x29bf),'Person|Adj':_0x235866(0x1ab0),'Plural|Verb':'Plural','Unit|Noun':_0x235866(0x1ce5)},_0x35ffb3=function(_0x55f625,_0x11d4b1){const _0x5c0615=_0x235866,_0x28faf0={'model':_0x11d4b1,'methods':_0x474f59};let {lex:_0x41bbdc,_multi:_0x44d740}=_0x474f59[_0x5c0615(0x141f)]['expandLexicon'](_0x55f625,_0x28faf0);return Object['assign'](_0x11d4b1[_0x5c0615(0x4116)][_0x5c0615(0x4ab0)],_0x41bbdc),Object[_0x5c0615(0x11e8)](_0x11d4b1[_0x5c0615(0x4116)][_0x5c0615(0x1398)],_0x44d740),_0x11d4b1;},_0x8bca8b=function(_0x1533bc,_0x5d7c94,_0x536b78){const _0x56e0be=_0x235866;let _0x1d20c6=_0x78c4dc(_0x1533bc,_0x18c1f0);_0x5d7c94[_0x1d20c6[_0x56e0be(0x2192)]]=_0x5d7c94[_0x1d20c6[_0x56e0be(0x2192)]]||_0x56e0be(0x2192),_0x5d7c94[_0x1d20c6[_0x56e0be(0x1e19)]]=_0x5d7c94[_0x1d20c6['Gerund']]||_0x56e0be(0x1e19),!0x0===_0x536b78&&(_0x5d7c94[_0x1d20c6['PresentTense']]=_0x5d7c94[_0x1d20c6[_0x56e0be(0x17a6)]]||_0x56e0be(0x17a6));},_0x51ce06=function(_0xe7b714,_0x46eb27,_0x38eb22){const _0x410d93=_0x235866;let _0x57e2f4=_0x5f405f(_0xe7b714,_0x38eb22);_0x46eb27[_0x57e2f4]=_0x46eb27[_0x57e2f4]||_0x410d93(0x2d21);let _0x220e1a=_0x23f0ee(_0xe7b714,_0x38eb22);_0x46eb27[_0x220e1a]=_0x46eb27[_0x220e1a]||_0x410d93(0x1ab0);},_0x2d35d9=function(_0x3763f1,_0x518c3b){const _0x3bf033=_0x235866;let _0x4395b7={};const _0x46f1e9=_0x518c3b['one'][_0x3bf033(0x4ab0)];return Object['keys'](_0x3763f1)[_0x3bf033(0x4854)](_0x133eec=>{const 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/,'');},_0x563b4c=function(){const _0x9a2bad=_0x235866;let _0x883ba1=this[_0x9a2bad(0x4163)](_0x9a2bad(0x40b1)),_0x5843ac=_0x883ba1['match'](_0x9a2bad(0x20e));return 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Set([_0x235866(0x2427),'how',_0x235866(0x489c),'if','too']);let _0x59a2d1=new Set([_0x235866(0x1074),_0x235866(0x1ebc),_0x235866(0x4a49)]);const _0x592ffc=function(_0xa610e8,_0x574f47){const _0xf036b4=_0x235866;let _0xe903f0=_0xa610e8[_0x574f47][_0xf036b4(0x4cb)][_0xf036b4(0x29d0)](_0x2bfc4b)[0x0];if(_0xf036b4(0x4647)===_0xe903f0)return[_0xe903f0,'us'];if(_0xf036b4(0x2105)===_0xe903f0){let _0x45b9ab=_0xa610e8[_0x574f47+0x1];if(_0x45b9ab&&_0x45b9ab['tags'][_0xf036b4(0x5ec)](_0xf036b4(0x27b3)))return[_0xe903f0,'are'];}return _0xf036b4(0x5ec)===((_0x1538a4,_0x131d3b)=>{const _0x135527=_0xf036b4;for(let _0x98fde1=_0x131d3b+0x1;_0x98fde1<_0x1538a4[_0x135527(0x205b)];_0x98fde1+=0x1){let 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Set([_0x235866(0x44fa),_0x235866(0x48dc),_0x235866(0x19f2),'it','had']),_0x574f6a=new Set(['have','be']),_0x1212e2=function(_0x949ef1,_0x284013){const _0x5c965e=_0x235866;let _0x20d405=_0x949ef1[_0x284013]['normal'][_0x5c965e(0x29d0)](_0x1a9d99)[0x0];return _0x5c965e(0x3626)===_0x20d405||_0x5c965e(0x2427)===_0x20d405?[_0x20d405,'did']:_0x5c965e(0x455a)===((_0x3705c2,_0x52396d)=>{const _0x4b0482=_0x5c965e;for(let _0x3e18f1=_0x52396d+0x1;_0x3e18f1<_0x3705c2[_0x4b0482(0x205b)];_0x3e18f1+=0x1){let _0x45b1a9=_0x3705c2[_0x3e18f1];if(_0x563a96[_0x4b0482(0x5ec)](_0x45b1a9[_0x4b0482(0x4cb)]))return'had';if(_0x574f6a['has'](_0x45b1a9['normal']))return'would';if(_0x45b1a9['tags'][_0x4b0482(0x5ec)](_0x4b0482(0x2192))||'Adj|Past'===_0x45b1a9[_0x4b0482(0x68)])return _0x4b0482(0x455a);if(_0x45b1a9[_0x4b0482(0x23d1)][_0x4b0482(0x5ec)](_0x4b0482(0x17a6))||_0x45b1a9[_0x4b0482(0x23d1)][_0x4b0482(0x5ec)](_0x4b0482(0x24ba)))return _0x4b0482(0x416a);if(_0x45b1a9['tags'][_0x4b0482(0x5ec)]('#Determiner'))return _0x4b0482(0x455a);if(_0x45b1a9[_0x4b0482(0x23d1)]['has'](_0x4b0482(0x19b5)))return _0x4b0482(0x416a);}return!0x1;})(_0x949ef1,_0x284013)?[_0x20d405,_0x5c965e(0x455a)]:[_0x20d405,'would'];},_0x1038d3=function(_0x1d8415,_0xb4baff){const _0x8739fb=_0x235866;if(_0x8739fb(0x2e85)===_0x1d8415[_0xb4baff][_0x8739fb(0x4cb)]||_0x8739fb(0x284f)===_0x1d8415[_0xb4baff][_0x8739fb(0x4cb)]){if(_0x1d8415[_0xb4baff+0x1]&&_0x8739fb(0x4b8c)===_0x1d8415[_0xb4baff+0x1][_0x8739fb(0x4cb)])return[_0x8739fb(0xb66)];let _0x3f96f7=function(_0x363ffc,_0x5695b3){const _0x14b4d7=_0x8739fb;for(let _0x231f76=_0x5695b3-0x1;_0x231f76>=0x0;_0x231f76-=0x1)if(_0x363ffc[_0x231f76]['tags']['has'](_0x14b4d7(0x1ce5))||_0x363ffc[_0x231f76][_0x14b4d7(0x23d1)]['has']('Pronoun')||_0x363ffc[_0x231f76][_0x14b4d7(0x23d1)][_0x14b4d7(0x5ec)](_0x14b4d7(0x27b3))||_0x363ffc[_0x231f76][_0x14b4d7(0x23d1)]['has'](_0x14b4d7(0x3faa)))return 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_0x68c62a=_0x53f933[_0x1bbe03];if(_0x182b54[_0x1c445f(0x32b5)](_0x68c62a[_0x1c445f(0x194)]||_0x68c62a[_0x1c445f(0x4cb)]))return!0x1;if(_0x68c62a['tags'][_0x1c445f(0x5ec)](_0x1c445f(0x1e24)))return!0x0;if(_0x68c62a[_0x1c445f(0x23d1)][_0x1c445f(0x5ec)](_0x1c445f(0x12d4)))return!0x1;if('he\x27s'===_0x68c62a[_0x1c445f(0x4cb)]||_0x1c445f(0x3c75)===_0x68c62a[_0x1c445f(0x4cb)])return!0x1;let _0x2f001f=_0x53f933[_0x1bbe03+0x1];if(!_0x2f001f)return!0x0;if(_0x1c445f(0x49b1)===_0x68c62a['normal'])return!!_0x2f001f[_0x1c445f(0x23d1)][_0x1c445f(0x5ec)]('#Noun');if(_0x1c445f(0x3a55)==_0x2f001f[_0x1c445f(0x68)]){let _0x19dabd=_0x53f933[_0x1bbe03+0x2];return 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_0x2d4762=_0x11111e[_0x399ce0(0x3e8f)](_0x45433a[_0x399ce0(0x379c)]('\x20'));return _0x2d4762[_0x399ce0(0x210d)]('id'),_0x2d4762[_0x399ce0(0x3f72)][0x0];},_0xb83c5f={'contractionTwo':_0x8d615c=>{const _0xcf0f5e=_0x235866;let {world:_0x5bfdeb,document:_0x2e3cf8}=_0x8d615c;_0x2e3cf8[_0xcf0f5e(0x4854)]((_0x697862,_0x592b75)=>{const _0x347b61=_0xcf0f5e;for(let _0x6ccdc4=_0x697862['length']-0x1;_0x6ccdc4>=0x0;_0x6ccdc4-=0x1){if(_0x697862[_0x6ccdc4][_0x347b61(0x15c0)])return;let _0x51e37c=null;!0x0===_0x1a86ab[_0x347b61(0x34c)](_0x697862[_0x6ccdc4][_0x347b61(0x4cb)])&&(_0x51e37c=_0x697862[_0x6ccdc4][_0x347b61(0x4cb)][_0x347b61(0x29d0)](_0x1a86ab)[0x1]);let 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_0x461ea4=_0x77b020+_0x4a4cee(0xe15)+_0x47745c+'\x20\x0a\x0a\x20QUESTION:\x20'+_0x4575c8+_0x4a4cee(0xd8e)+_0x379feb,_0x2f8de1={'prompt':_0x461ea4,'messages':_0x3de7d2};_0x2f8de1[_0x4a4cee(0x2c3e)]=_0x58daab,_0x4dd22a+=0x1,_0x501c91(_0x403bd6,_0x4dd22a);let _0xc2225c=0x0;_0x4dd22a+=0x1,_0x501c91(_0x403bd6,_0x4dd22a);try{for await(let _0x3acd9e of _0x6b503a(_0x2f8de1,_0x4586ac,!0x0,!0x0))0x0===_0xc2225c&&(_0x4dd22a+=0x1,_0x501c91(_0x403bd6,_0x4dd22a)),_0xc2225c+=0x1,_0x324c51+=_0x3acd9e,_0x60383d(_0x324c51,_0x487553);}catch(_0x3a7f6a){_0x26f7f9(_0x2c6959,_0x3a7f6a+_0x4a4cee(0x593),_0x4a4cee(0x4b47));}_0x37bbfd(_0x36a8f0||0x1,_0x4a4cee(0x6cd),_0x324c51,_0x4586ac),_0x4c99f2=_0x324c51+_0x4a4cee(0x3b9a);const _0xbf5f40=_0x574c15(_0x2c6959,0x0,_0x19cc1a);_0x101050(_0x2c6959,_0x487553,_0xbf5f40),await _0x4f50a1(_0x487553,'progress'),_0x184d80(_0x487553),_0x4c0aad(_0x2c6959),_0x6eaaba(_0x4a4cee(0x203b),_0x2c6959),await 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fetch(_0x72c272,{'method':'POST','headers':{'Content-Type':_0x2cb6f1(0x198d),'Authorization':_0x2cb6f1(0x2485)+_0x576f5c},'body':JSON[_0x2cb6f1(0xf0b)](_0x13ac50)});if(!_0x28af3a['ok'])throw new Error(_0x2cb6f1(0x1a58)+_0x28af3a[_0x2cb6f1(0x33de)]);return await _0x28af3a[_0x2cb6f1(0x25b9)]();}catch(_0x13fb96){}}(_0x4a581a,_0x4586ac),_0x291eba(_0x5bd6f8),_0x133944+=0x1,_0x501c91(_0x4885fb,_0x133944),Array[_0x15a455(0x477f)](_0x38da71))_0x457895(_0x5bd6f8,_0x38da71);else{if(null!==_0x38da71&&_0x15a455(0x2b58)==typeof _0x38da71){const _0x4adbab=Object[_0x15a455(0x2f75)](_0x38da71);if(_0x4adbab[_0x15a455(0x205b)]>0x0&&_0x38da71[_0x4adbab[0x0]][_0x15a455(0x205b)]>0x0){const _0x33d57c=_0x4adbab[0x0];_0x457895(_0x5bd6f8,_0x38da71[_0x33d57c]);}else _0x38da71='0',_0x457895(_0x5bd6f8,_0x38da71),_0x26f7f9(_0x2c6959,_0x15a455(0xd62),'Error');}else 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STORY_CREATION_USER_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_CREATION_USER_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_CREATION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DATABASE_USER_DELETION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_COMP_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_GROUP_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_DELETION_USER_FROM_GROUP_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_FILTERER_RESTRICTION_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_PRIVILEGE_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_GRANTING_RIGHTS_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_IS_MAIN_SERVER_CHANGED_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_IS_PUBLIC_CHANGED_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_FILTERER_RESTRICTION_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_PRIVILEGE_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_REMOVING_RIGHTS_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_CREATION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_SERVER_LOGIN_DELETION_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_CATEGORY_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_COMP_TITLE_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_FULL_NAME_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_GROUP_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_PARENT_GROUP_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_AUTH_TYPE_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_LOGIN_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION\x20SYSRES_CONST_ADMINISTRATION_HISTORY_UPDATING_USER_STATUS_ACTION_CODE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE\x20SYSRES_CONST_ADMINISTRATION_HISTORY_USER_PASSWORD_CHANGE_ACTION\x20SYSRES_CONST_ALL_ACCEPT_CONDITION_RUS\x20SYSRES_CONST_ALL_USERS_GROUP\x20SYSRES_CONST_ALL_USERS_GROUP_NAME\x20SYSRES_CONST_ALL_USERS_SERVER_GROUP_NAME\x20SYSRES_CONST_ALLOWED_ACCESS_TYPE_CODE\x20SYSRES_CONST_ALLOWED_ACCESS_TYPE_NAME\x20SYSRES_CONST_APP_VIEWER_TYPE_REQUISITE_CODE\x20SYSRES_CONST_APPROVING_SIGNATURE_NAME\x20SYSRES_CONST_APPROVING_SIGNATURE_REQUISITE_CODE\x20SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE\x20SYSRES_CONST_ASSISTANT_SUBSTITUE_TYPE_CODE\x20SYSRES_CONST_ATTACH_TYPE_COMPONENT_TOKEN\x20SYSRES_CONST_ATTACH_TYPE_DOC\x20SYSRES_CONST_ATTACH_TYPE_EDOC\x20SYSRES_CONST_ATTACH_TYPE_FOLDER\x20SYSRES_CONST_ATTACH_TYPE_JOB\x20SYSRES_CONST_ATTACH_TYPE_REFERENCE\x20SYSRES_CONST_ATTACH_TYPE_TASK\x20SYSRES_CONST_AUTH_ENCODED_PASSWORD\x20SYSRES_CONST_AUTH_ENCODED_PASSWORD_CODE\x20SYSRES_CONST_AUTH_NOVELL\x20SYSRES_CONST_AUTH_PASSWORD\x20SYSRES_CONST_AUTH_PASSWORD_CODE\x20SYSRES_CONST_AUTH_WINDOWS\x20SYSRES_CONST_AUTHENTICATING_SIGNATURE_NAME\x20SYSRES_CONST_AUTHENTICATING_SIGNATURE_REQUISITE_CODE\x20SYSRES_CONST_AUTO_ENUM_METHOD_FLAG\x20SYSRES_CONST_AUTO_NUMERATION_CODE\x20SYSRES_CONST_AUTO_STRONG_ENUM_METHOD_FLAG\x20SYSRES_CONST_AUTOTEXT_NAME_REQUISITE_CODE\x20SYSRES_CONST_AUTOTEXT_TEXT_REQUISITE_CODE\x20SYSRES_CONST_AUTOTEXT_USAGE_ALL\x20SYSRES_CONST_AUTOTEXT_USAGE_ALL_CODE\x20SYSRES_CONST_AUTOTEXT_USAGE_SIGN\x20SYSRES_CONST_AUTOTEXT_USAGE_SIGN_CODE\x20SYSRES_CONST_AUTOTEXT_USAGE_WORK\x20SYSRES_CONST_AUTOTEXT_USAGE_WORK_CODE\x20SYSRES_CONST_AUTOTEXT_USE_ANYWHERE_CODE\x20SYSRES_CONST_AUTOTEXT_USE_ON_SIGNING_CODE\x20SYSRES_CONST_AUTOTEXT_USE_ON_WORK_CODE\x20SYSRES_CONST_BEGIN_DATE_REQUISITE_CODE\x20SYSRES_CONST_BLACK_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_BLUE_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_BTN_PART\x20SYSRES_CONST_CALCULATED_ROLE_TYPE_CODE\x20SYSRES_CONST_CALL_TYPE_VARIABLE_BUTTON_VALUE\x20SYSRES_CONST_CALL_TYPE_VARIABLE_PROGRAM_VALUE\x20SYSRES_CONST_CANCEL_MESSAGE_FUNCTION_RESULT\x20SYSRES_CONST_CARD_PART\x20SYSRES_CONST_CARD_REFERENCE_MODE_NAME\x20SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_ENCRYPT_VALUE\x20SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_AND_ENCRYPT_VALUE\x20SYSRES_CONST_CERTIFICATE_TYPE_REQUISITE_SIGN_VALUE\x20SYSRES_CONST_CHECK_PARAM_VALUE_DATE_PARAM_TYPE\x20SYSRES_CONST_CHECK_PARAM_VALUE_FLOAT_PARAM_TYPE\x20SYSRES_CONST_CHECK_PARAM_VALUE_INTEGER_PARAM_TYPE\x20SYSRES_CONST_CHECK_PARAM_VALUE_PICK_PARAM_TYPE\x20SYSRES_CONST_CHECK_PARAM_VALUE_REEFRENCE_PARAM_TYPE\x20SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_FEMININE\x20SYSRES_CONST_CLOSED_RECORD_FLAG_VALUE_MASCULINE\x20SYSRES_CONST_CODE_COMPONENT_TYPE_ADMIN\x20SYSRES_CONST_CODE_COMPONENT_TYPE_DEVELOPER\x20SYSRES_CONST_CODE_COMPONENT_TYPE_DOCS\x20SYSRES_CONST_CODE_COMPONENT_TYPE_EDOC_CARDS\x20SYSRES_CONST_CODE_COMPONENT_TYPE_EXTERNAL_EXECUTABLE\x20SYSRES_CONST_CODE_COMPONENT_TYPE_OTHER\x20SYSRES_CONST_CODE_COMPONENT_TYPE_REFERENCE\x20SYSRES_CONST_CODE_COMPONENT_TYPE_REPORT\x20SYSRES_CONST_CODE_COMPONENT_TYPE_SCRIPT\x20SYSRES_CONST_CODE_COMPONENT_TYPE_URL\x20SYSRES_CONST_CODE_REQUISITE_ACCESS\x20SYSRES_CONST_CODE_REQUISITE_CODE\x20SYSRES_CONST_CODE_REQUISITE_COMPONENT\x20SYSRES_CONST_CODE_REQUISITE_DESCRIPTION\x20SYSRES_CONST_CODE_REQUISITE_EXCLUDE_COMPONENT\x20SYSRES_CONST_CODE_REQUISITE_RECORD\x20SYSRES_CONST_COMMENT_REQ_CODE\x20SYSRES_CONST_COMMON_SETTINGS_REQUISITE_CODE\x20SYSRES_CONST_COMP_CODE_GRD\x20SYSRES_CONST_COMPONENT_GROUP_TYPE_REQUISITE_CODE\x20SYSRES_CONST_COMPONENT_TYPE_ADMIN_COMPONENTS\x20SYSRES_CONST_COMPONENT_TYPE_DEVELOPER_COMPONENTS\x20SYSRES_CONST_COMPONENT_TYPE_DOCS\x20SYSRES_CONST_COMPONENT_TYPE_EDOC_CARDS\x20SYSRES_CONST_COMPONENT_TYPE_EDOCS\x20SYSRES_CONST_COMPONENT_TYPE_EXTERNAL_EXECUTABLE\x20SYSRES_CONST_COMPONENT_TYPE_OTHER\x20SYSRES_CONST_COMPONENT_TYPE_REFERENCE_TYPES\x20SYSRES_CONST_COMPONENT_TYPE_REFERENCES\x20SYSRES_CONST_COMPONENT_TYPE_REPORTS\x20SYSRES_CONST_COMPONENT_TYPE_SCRIPTS\x20SYSRES_CONST_COMPONENT_TYPE_URL\x20SYSRES_CONST_COMPONENTS_REMOTE_SERVERS_VIEW_CODE\x20SYSRES_CONST_CONDITION_BLOCK_DESCRIPTION\x20SYSRES_CONST_CONST_FIRM_STATUS_COMMON\x20SYSRES_CONST_CONST_FIRM_STATUS_INDIVIDUAL\x20SYSRES_CONST_CONST_NEGATIVE_VALUE\x20SYSRES_CONST_CONST_POSITIVE_VALUE\x20SYSRES_CONST_CONST_SERVER_STATUS_DONT_REPLICATE\x20SYSRES_CONST_CONST_SERVER_STATUS_REPLICATE\x20SYSRES_CONST_CONTENTS_REQUISITE_CODE\x20SYSRES_CONST_DATA_TYPE_BOOLEAN\x20SYSRES_CONST_DATA_TYPE_DATE\x20SYSRES_CONST_DATA_TYPE_FLOAT\x20SYSRES_CONST_DATA_TYPE_INTEGER\x20SYSRES_CONST_DATA_TYPE_PICK\x20SYSRES_CONST_DATA_TYPE_REFERENCE\x20SYSRES_CONST_DATA_TYPE_STRING\x20SYSRES_CONST_DATA_TYPE_TEXT\x20SYSRES_CONST_DATA_TYPE_VARIANT\x20SYSRES_CONST_DATE_CLOSE_REQ_CODE\x20SYSRES_CONST_DATE_FORMAT_DATE_ONLY_CHAR\x20SYSRES_CONST_DATE_OPEN_REQ_CODE\x20SYSRES_CONST_DATE_REQUISITE\x20SYSRES_CONST_DATE_REQUISITE_CODE\x20SYSRES_CONST_DATE_REQUISITE_NAME\x20SYSRES_CONST_DATE_REQUISITE_TYPE\x20SYSRES_CONST_DATE_TYPE_CHAR\x20SYSRES_CONST_DATETIME_FORMAT_VALUE\x20SYSRES_CONST_DEA_ACCESS_RIGHTS_ACTION_CODE\x20SYSRES_CONST_DESCRIPTION_LOCALIZE_ID_REQUISITE_CODE\x20SYSRES_CONST_DESCRIPTION_REQUISITE_CODE\x20SYSRES_CONST_DET1_PART\x20SYSRES_CONST_DET2_PART\x20SYSRES_CONST_DET3_PART\x20SYSRES_CONST_DET4_PART\x20SYSRES_CONST_DET5_PART\x20SYSRES_CONST_DET6_PART\x20SYSRES_CONST_DETAIL_DATASET_KEY_REQUISITE_CODE\x20SYSRES_CONST_DETAIL_PICK_REQUISITE_CODE\x20SYSRES_CONST_DETAIL_REQ_CODE\x20SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_CODE\x20SYSRES_CONST_DO_NOT_USE_ACCESS_TYPE_NAME\x20SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_CODE\x20SYSRES_CONST_DO_NOT_USE_ON_VIEW_ACCESS_TYPE_NAME\x20SYSRES_CONST_DOCUMENT_STORAGES_CODE\x20SYSRES_CONST_DOCUMENT_TEMPLATES_TYPE_NAME\x20SYSRES_CONST_DOUBLE_REQUISITE_CODE\x20SYSRES_CONST_EDITOR_CLOSE_FILE_OBSERV_TYPE_CODE\x20SYSRES_CONST_EDITOR_CLOSE_PROCESS_OBSERV_TYPE_CODE\x20SYSRES_CONST_EDITOR_TYPE_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_APPLICATION_NAME_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_CREATE_SEVERAL_PROCESSES_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_EXTENSION_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_OBSERVER_BY_PROCESS_TYPE\x20SYSRES_CONST_EDITORS_REFERENCE_CODE\x20SYSRES_CONST_EDITORS_REPLACE_SPEC_CHARS_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_USE_PLUGINS_REQUISITE_CODE\x20SYSRES_CONST_EDITORS_VIEW_DOCUMENT_OPENED_TO_EDIT_CODE\x20SYSRES_CONST_EDOC_CARD_TYPE_REQUISITE_CODE\x20SYSRES_CONST_EDOC_CARD_TYPES_LINK_REQUISITE_CODE\x20SYSRES_CONST_EDOC_CERTIFICATE_AND_PASSWORD_ENCODE_CODE\x20SYSRES_CONST_EDOC_CERTIFICATE_ENCODE_CODE\x20SYSRES_CONST_EDOC_DATE_REQUISITE_CODE\x20SYSRES_CONST_EDOC_KIND_REFERENCE_CODE\x20SYSRES_CONST_EDOC_KINDS_BY_TEMPLATE_ACTION_CODE\x20SYSRES_CONST_EDOC_MANAGE_ACCESS_CODE\x20SYSRES_CONST_EDOC_NONE_ENCODE_CODE\x20SYSRES_CONST_EDOC_NUMBER_REQUISITE_CODE\x20SYSRES_CONST_EDOC_PASSWORD_ENCODE_CODE\x20SYSRES_CONST_EDOC_READONLY_ACCESS_CODE\x20SYSRES_CONST_EDOC_SHELL_LIFE_TYPE_VIEW_VALUE\x20SYSRES_CONST_EDOC_SIZE_RESTRICTION_PRIORITY_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_CHECK_ACCESS_RIGHTS_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_COMPUTER_NAME_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_DATABASE_NAME_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_EDIT_IN_STORAGE_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_LOCAL_PATH_REQUISITE_CODE\x20SYSRES_CONST_EDOC_STORAGE_SHARED_SOURCE_NAME_REQUISITE_CODE\x20SYSRES_CONST_EDOC_TEMPLATE_REQUISITE_CODE\x20SYSRES_CONST_EDOC_TYPES_REFERENCE_CODE\x20SYSRES_CONST_EDOC_VERSION_ACTIVE_STAGE_CODE\x20SYSRES_CONST_EDOC_VERSION_DESIGN_STAGE_CODE\x20SYSRES_CONST_EDOC_VERSION_OBSOLETE_STAGE_CODE\x20SYSRES_CONST_EDOC_WRITE_ACCES_CODE\x20SYSRES_CONST_EDOCUMENT_CARD_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE\x20SYSRES_CONST_ENCODE_CERTIFICATE_TYPE_CODE\x20SYSRES_CONST_END_DATE_REQUISITE_CODE\x20SYSRES_CONST_ENUMERATION_TYPE_REQUISITE_CODE\x20SYSRES_CONST_EXECUTE_ACCESS_RIGHTS_TYPE_CODE\x20SYSRES_CONST_EXECUTIVE_FILE_STORAGE_TYPE\x20SYSRES_CONST_EXIST_CONST\x20SYSRES_CONST_EXIST_VALUE\x20SYSRES_CONST_EXPORT_LOCK_TYPE_ASK\x20SYSRES_CONST_EXPORT_LOCK_TYPE_WITH_LOCK\x20SYSRES_CONST_EXPORT_LOCK_TYPE_WITHOUT_LOCK\x20SYSRES_CONST_EXPORT_VERSION_TYPE_ASK\x20SYSRES_CONST_EXPORT_VERSION_TYPE_LAST\x20SYSRES_CONST_EXPORT_VERSION_TYPE_LAST_ACTIVE\x20SYSRES_CONST_EXTENSION_REQUISITE_CODE\x20SYSRES_CONST_FILTER_NAME_REQUISITE_CODE\x20SYSRES_CONST_FILTER_REQUISITE_CODE\x20SYSRES_CONST_FILTER_TYPE_COMMON_CODE\x20SYSRES_CONST_FILTER_TYPE_COMMON_NAME\x20SYSRES_CONST_FILTER_TYPE_USER_CODE\x20SYSRES_CONST_FILTER_TYPE_USER_NAME\x20SYSRES_CONST_FILTER_VALUE_REQUISITE_NAME\x20SYSRES_CONST_FLOAT_NUMBER_FORMAT_CHAR\x20SYSRES_CONST_FLOAT_REQUISITE_TYPE\x20SYSRES_CONST_FOLDER_AUTHOR_VALUE\x20SYSRES_CONST_FOLDER_KIND_ANY_OBJECTS\x20SYSRES_CONST_FOLDER_KIND_COMPONENTS\x20SYSRES_CONST_FOLDER_KIND_EDOCS\x20SYSRES_CONST_FOLDER_KIND_JOBS\x20SYSRES_CONST_FOLDER_KIND_TASKS\x20SYSRES_CONST_FOLDER_TYPE_COMMON\x20SYSRES_CONST_FOLDER_TYPE_COMPONENT\x20SYSRES_CONST_FOLDER_TYPE_FAVORITES\x20SYSRES_CONST_FOLDER_TYPE_INBOX\x20SYSRES_CONST_FOLDER_TYPE_OUTBOX\x20SYSRES_CONST_FOLDER_TYPE_QUICK_LAUNCH\x20SYSRES_CONST_FOLDER_TYPE_SEARCH\x20SYSRES_CONST_FOLDER_TYPE_SHORTCUTS\x20SYSRES_CONST_FOLDER_TYPE_USER\x20SYSRES_CONST_FROM_DICTIONARY_ENUM_METHOD_FLAG\x20SYSRES_CONST_FULL_SUBSTITUTE_TYPE\x20SYSRES_CONST_FULL_SUBSTITUTE_TYPE_CODE\x20SYSRES_CONST_FUNCTION_CANCEL_RESULT\x20SYSRES_CONST_FUNCTION_CATEGORY_SYSTEM\x20SYSRES_CONST_FUNCTION_CATEGORY_USER\x20SYSRES_CONST_FUNCTION_FAILURE_RESULT\x20SYSRES_CONST_FUNCTION_SAVE_RESULT\x20SYSRES_CONST_GENERATED_REQUISITE\x20SYSRES_CONST_GREEN_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_GROUP_ACCOUNT_TYPE_VALUE_CODE\x20SYSRES_CONST_GROUP_CATEGORY_NORMAL_CODE\x20SYSRES_CONST_GROUP_CATEGORY_NORMAL_NAME\x20SYSRES_CONST_GROUP_CATEGORY_SERVICE_CODE\x20SYSRES_CONST_GROUP_CATEGORY_SERVICE_NAME\x20SYSRES_CONST_GROUP_COMMON_CATEGORY_FIELD_VALUE\x20SYSRES_CONST_GROUP_FULL_NAME_REQUISITE_CODE\x20SYSRES_CONST_GROUP_NAME_REQUISITE_CODE\x20SYSRES_CONST_GROUP_RIGHTS_T_REQUISITE_CODE\x20SYSRES_CONST_GROUP_SERVER_CODES_REQUISITE_CODE\x20SYSRES_CONST_GROUP_SERVER_NAME_REQUISITE_CODE\x20SYSRES_CONST_GROUP_SERVICE_CATEGORY_FIELD_VALUE\x20SYSRES_CONST_GROUP_USER_REQUISITE_CODE\x20SYSRES_CONST_GROUPS_REFERENCE_CODE\x20SYSRES_CONST_GROUPS_REQUISITE_CODE\x20SYSRES_CONST_HIDDEN_MODE_NAME\x20SYSRES_CONST_HIGH_LVL_REQUISITE_CODE\x20SYSRES_CONST_HISTORY_ACTION_CREATE_CODE\x20SYSRES_CONST_HISTORY_ACTION_DELETE_CODE\x20SYSRES_CONST_HISTORY_ACTION_EDIT_CODE\x20SYSRES_CONST_HOUR_CHAR\x20SYSRES_CONST_ID_REQUISITE_CODE\x20SYSRES_CONST_IDSPS_REQUISITE_CODE\x20SYSRES_CONST_IMAGE_MODE_COLOR\x20SYSRES_CONST_IMAGE_MODE_GREYSCALE\x20SYSRES_CONST_IMAGE_MODE_MONOCHROME\x20SYSRES_CONST_IMPORTANCE_HIGH\x20SYSRES_CONST_IMPORTANCE_LOW\x20SYSRES_CONST_IMPORTANCE_NORMAL\x20SYSRES_CONST_IN_DESIGN_VERSION_STATE_PICK_VALUE\x20SYSRES_CONST_INCOMING_WORK_RULE_TYPE_CODE\x20SYSRES_CONST_INT_REQUISITE\x20SYSRES_CONST_INT_REQUISITE_TYPE\x20SYSRES_CONST_INTEGER_NUMBER_FORMAT_CHAR\x20SYSRES_CONST_INTEGER_TYPE_CHAR\x20SYSRES_CONST_IS_GENERATED_REQUISITE_NEGATIVE_VALUE\x20SYSRES_CONST_IS_PUBLIC_ROLE_REQUISITE_CODE\x20SYSRES_CONST_IS_REMOTE_USER_NEGATIVE_VALUE\x20SYSRES_CONST_IS_REMOTE_USER_POSITIVE_VALUE\x20SYSRES_CONST_IS_STORED_REQUISITE_NEGATIVE_VALUE\x20SYSRES_CONST_IS_STORED_REQUISITE_STORED_VALUE\x20SYSRES_CONST_ITALIC_LIFE_CYCLE_STAGE_DRAW_STYLE\x20SYSRES_CONST_JOB_BLOCK_DESCRIPTION\x20SYSRES_CONST_JOB_KIND_CONTROL_JOB\x20SYSRES_CONST_JOB_KIND_JOB\x20SYSRES_CONST_JOB_KIND_NOTICE\x20SYSRES_CONST_JOB_STATE_ABORTED\x20SYSRES_CONST_JOB_STATE_COMPLETE\x20SYSRES_CONST_JOB_STATE_WORKING\x20SYSRES_CONST_KIND_REQUISITE_CODE\x20SYSRES_CONST_KIND_REQUISITE_NAME\x20SYSRES_CONST_KINDS_CREATE_SHADOW_COPIES_REQUISITE_CODE\x20SYSRES_CONST_KINDS_DEFAULT_EDOC_LIFE_STAGE_REQUISITE_CODE\x20SYSRES_CONST_KINDS_EDOC_ALL_TEPLATES_ALLOWED_REQUISITE_CODE\x20SYSRES_CONST_KINDS_EDOC_ALLOW_LIFE_CYCLE_STAGE_CHANGING_REQUISITE_CODE\x20SYSRES_CONST_KINDS_EDOC_ALLOW_MULTIPLE_ACTIVE_VERSIONS_REQUISITE_CODE\x20SYSRES_CONST_KINDS_EDOC_SHARE_ACCES_RIGHTS_BY_DEFAULT_CODE\x20SYSRES_CONST_KINDS_EDOC_TEMPLATE_REQUISITE_CODE\x20SYSRES_CONST_KINDS_EDOC_TYPE_REQUISITE_CODE\x20SYSRES_CONST_KINDS_SIGNERS_REQUISITES_CODE\x20SYSRES_CONST_KOD_INPUT_TYPE\x20SYSRES_CONST_LAST_UPDATE_DATE_REQUISITE_CODE\x20SYSRES_CONST_LIFE_CYCLE_START_STAGE_REQUISITE_CODE\x20SYSRES_CONST_LILAC_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_LINK_OBJECT_KIND_COMPONENT\x20SYSRES_CONST_LINK_OBJECT_KIND_DOCUMENT\x20SYSRES_CONST_LINK_OBJECT_KIND_EDOC\x20SYSRES_CONST_LINK_OBJECT_KIND_FOLDER\x20SYSRES_CONST_LINK_OBJECT_KIND_JOB\x20SYSRES_CONST_LINK_OBJECT_KIND_REFERENCE\x20SYSRES_CONST_LINK_OBJECT_KIND_TASK\x20SYSRES_CONST_LINK_REF_TYPE_REQUISITE_CODE\x20SYSRES_CONST_LIST_REFERENCE_MODE_NAME\x20SYSRES_CONST_LOCALIZATION_DICTIONARY_MAIN_VIEW_CODE\x20SYSRES_CONST_MAIN_VIEW_CODE\x20SYSRES_CONST_MANUAL_ENUM_METHOD_FLAG\x20SYSRES_CONST_MASTER_COMP_TYPE_REQUISITE_CODE\x20SYSRES_CONST_MASTER_TABLE_REC_ID_REQUISITE_CODE\x20SYSRES_CONST_MAXIMIZED_MODE_NAME\x20SYSRES_CONST_ME_VALUE\x20SYSRES_CONST_MESSAGE_ATTENTION_CAPTION\x20SYSRES_CONST_MESSAGE_CONFIRMATION_CAPTION\x20SYSRES_CONST_MESSAGE_ERROR_CAPTION\x20SYSRES_CONST_MESSAGE_INFORMATION_CAPTION\x20SYSRES_CONST_MINIMIZED_MODE_NAME\x20SYSRES_CONST_MINUTE_CHAR\x20SYSRES_CONST_MODULE_REQUISITE_CODE\x20SYSRES_CONST_MONITORING_BLOCK_DESCRIPTION\x20SYSRES_CONST_MONTH_FORMAT_VALUE\x20SYSRES_CONST_NAME_LOCALIZE_ID_REQUISITE_CODE\x20SYSRES_CONST_NAME_REQUISITE_CODE\x20SYSRES_CONST_NAME_SINGULAR_REQUISITE_CODE\x20SYSRES_CONST_NAMEAN_INPUT_TYPE\x20SYSRES_CONST_NEGATIVE_PICK_VALUE\x20SYSRES_CONST_NEGATIVE_VALUE\x20SYSRES_CONST_NO\x20SYSRES_CONST_NO_PICK_VALUE\x20SYSRES_CONST_NO_SIGNATURE_REQUISITE_CODE\x20SYSRES_CONST_NO_VALUE\x20SYSRES_CONST_NONE_ACCESS_RIGHTS_TYPE_CODE\x20SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE\x20SYSRES_CONST_NONOPERATING_RECORD_FLAG_VALUE_MASCULINE\x20SYSRES_CONST_NORMAL_ACCESS_RIGHTS_TYPE_CODE\x20SYSRES_CONST_NORMAL_LIFE_CYCLE_STAGE_DRAW_STYLE\x20SYSRES_CONST_NORMAL_MODE_NAME\x20SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_CODE\x20SYSRES_CONST_NOT_ALLOWED_ACCESS_TYPE_NAME\x20SYSRES_CONST_NOTE_REQUISITE_CODE\x20SYSRES_CONST_NOTICE_BLOCK_DESCRIPTION\x20SYSRES_CONST_NUM_REQUISITE\x20SYSRES_CONST_NUM_STR_REQUISITE_CODE\x20SYSRES_CONST_NUMERATION_AUTO_NOT_STRONG\x20SYSRES_CONST_NUMERATION_AUTO_STRONG\x20SYSRES_CONST_NUMERATION_FROM_DICTONARY\x20SYSRES_CONST_NUMERATION_MANUAL\x20SYSRES_CONST_NUMERIC_TYPE_CHAR\x20SYSRES_CONST_NUMREQ_REQUISITE_CODE\x20SYSRES_CONST_OBSOLETE_VERSION_STATE_PICK_VALUE\x20SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE\x20SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_CODE\x20SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_FEMININE\x20SYSRES_CONST_OPERATING_RECORD_FLAG_VALUE_MASCULINE\x20SYSRES_CONST_OPTIONAL_FORM_COMP_REQCODE_PREFIX\x20SYSRES_CONST_ORANGE_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_ORIGINALREF_REQUISITE_CODE\x20SYSRES_CONST_OURFIRM_REF_CODE\x20SYSRES_CONST_OURFIRM_REQUISITE_CODE\x20SYSRES_CONST_OURFIRM_VAR\x20SYSRES_CONST_OUTGOING_WORK_RULE_TYPE_CODE\x20SYSRES_CONST_PICK_NEGATIVE_RESULT\x20SYSRES_CONST_PICK_POSITIVE_RESULT\x20SYSRES_CONST_PICK_REQUISITE\x20SYSRES_CONST_PICK_REQUISITE_TYPE\x20SYSRES_CONST_PICK_TYPE_CHAR\x20SYSRES_CONST_PLAN_STATUS_REQUISITE_CODE\x20SYSRES_CONST_PLATFORM_VERSION_COMMENT\x20SYSRES_CONST_PLUGINS_SETTINGS_DESCRIPTION_REQUISITE_CODE\x20SYSRES_CONST_POSITIVE_PICK_VALUE\x20SYSRES_CONST_POWER_TO_CREATE_ACTION_CODE\x20SYSRES_CONST_POWER_TO_SIGN_ACTION_CODE\x20SYSRES_CONST_PRIORITY_REQUISITE_CODE\x20SYSRES_CONST_QUALIFIED_TASK_TYPE\x20SYSRES_CONST_QUALIFIED_TASK_TYPE_CODE\x20SYSRES_CONST_RECSTAT_REQUISITE_CODE\x20SYSRES_CONST_RED_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_REF_ID_T_REF_TYPE_REQUISITE_CODE\x20SYSRES_CONST_REF_REQUISITE\x20SYSRES_CONST_REF_REQUISITE_TYPE\x20SYSRES_CONST_REF_REQUISITES_REFERENCE_CODE_SELECTED_REQUISITE\x20SYSRES_CONST_REFERENCE_RECORD_HISTORY_CREATE_ACTION_CODE\x20SYSRES_CONST_REFERENCE_RECORD_HISTORY_DELETE_ACTION_CODE\x20SYSRES_CONST_REFERENCE_RECORD_HISTORY_MODIFY_ACTION_CODE\x20SYSRES_CONST_REFERENCE_TYPE_CHAR\x20SYSRES_CONST_REFERENCE_TYPE_REQUISITE_NAME\x20SYSRES_CONST_REFERENCES_ADD_PARAMS_REQUISITE_CODE\x20SYSRES_CONST_REFERENCES_DISPLAY_REQUISITE_REQUISITE_CODE\x20SYSRES_CONST_REMOTE_SERVER_STATUS_WORKING\x20SYSRES_CONST_REMOTE_SERVER_TYPE_MAIN\x20SYSRES_CONST_REMOTE_SERVER_TYPE_SECONDARY\x20SYSRES_CONST_REMOTE_USER_FLAG_VALUE_CODE\x20SYSRES_CONST_REPORT_APP_EDITOR_INTERNAL\x20SYSRES_CONST_REPORT_BASE_REPORT_ID_REQUISITE_CODE\x20SYSRES_CONST_REPORT_BASE_REPORT_REQUISITE_CODE\x20SYSRES_CONST_REPORT_SCRIPT_REQUISITE_CODE\x20SYSRES_CONST_REPORT_TEMPLATE_REQUISITE_CODE\x20SYSRES_CONST_REPORT_VIEWER_CODE_REQUISITE_CODE\x20SYSRES_CONST_REQ_ALLOW_COMPONENT_DEFAULT_VALUE\x20SYSRES_CONST_REQ_ALLOW_RECORD_DEFAULT_VALUE\x20SYSRES_CONST_REQ_ALLOW_SERVER_COMPONENT_DEFAULT_VALUE\x20SYSRES_CONST_REQ_MODE_AVAILABLE_CODE\x20SYSRES_CONST_REQ_MODE_EDIT_CODE\x20SYSRES_CONST_REQ_MODE_HIDDEN_CODE\x20SYSRES_CONST_REQ_MODE_NOT_AVAILABLE_CODE\x20SYSRES_CONST_REQ_MODE_VIEW_CODE\x20SYSRES_CONST_REQ_NUMBER_REQUISITE_CODE\x20SYSRES_CONST_REQ_SECTION_VALUE\x20SYSRES_CONST_REQ_TYPE_VALUE\x20SYSRES_CONST_REQUISITE_FORMAT_BY_UNIT\x20SYSRES_CONST_REQUISITE_FORMAT_DATE_FULL\x20SYSRES_CONST_REQUISITE_FORMAT_DATE_TIME\x20SYSRES_CONST_REQUISITE_FORMAT_LEFT\x20SYSRES_CONST_REQUISITE_FORMAT_RIGHT\x20SYSRES_CONST_REQUISITE_FORMAT_WITHOUT_UNIT\x20SYSRES_CONST_REQUISITE_NUMBER_REQUISITE_CODE\x20SYSRES_CONST_REQUISITE_SECTION_ACTIONS\x20SYSRES_CONST_REQUISITE_SECTION_BUTTON\x20SYSRES_CONST_REQUISITE_SECTION_BUTTONS\x20SYSRES_CONST_REQUISITE_SECTION_CARD\x20SYSRES_CONST_REQUISITE_SECTION_TABLE\x20SYSRES_CONST_REQUISITE_SECTION_TABLE10\x20SYSRES_CONST_REQUISITE_SECTION_TABLE11\x20SYSRES_CONST_REQUISITE_SECTION_TABLE12\x20SYSRES_CONST_REQUISITE_SECTION_TABLE13\x20SYSRES_CONST_REQUISITE_SECTION_TABLE14\x20SYSRES_CONST_REQUISITE_SECTION_TABLE15\x20SYSRES_CONST_REQUISITE_SECTION_TABLE16\x20SYSRES_CONST_REQUISITE_SECTION_TABLE17\x20SYSRES_CONST_REQUISITE_SECTION_TABLE18\x20SYSRES_CONST_REQUISITE_SECTION_TABLE19\x20SYSRES_CONST_REQUISITE_SECTION_TABLE2\x20SYSRES_CONST_REQUISITE_SECTION_TABLE20\x20SYSRES_CONST_REQUISITE_SECTION_TABLE21\x20SYSRES_CONST_REQUISITE_SECTION_TABLE22\x20SYSRES_CONST_REQUISITE_SECTION_TABLE23\x20SYSRES_CONST_REQUISITE_SECTION_TABLE24\x20SYSRES_CONST_REQUISITE_SECTION_TABLE3\x20SYSRES_CONST_REQUISITE_SECTION_TABLE4\x20SYSRES_CONST_REQUISITE_SECTION_TABLE5\x20SYSRES_CONST_REQUISITE_SECTION_TABLE6\x20SYSRES_CONST_REQUISITE_SECTION_TABLE7\x20SYSRES_CONST_REQUISITE_SECTION_TABLE8\x20SYSRES_CONST_REQUISITE_SECTION_TABLE9\x20SYSRES_CONST_REQUISITES_PSEUDOREFERENCE_REQUISITE_NUMBER_REQUISITE_CODE\x20SYSRES_CONST_RIGHT_ALIGNMENT_CODE\x20SYSRES_CONST_ROLES_REFERENCE_CODE\x20SYSRES_CONST_ROUTE_STEP_AFTER_RUS\x20SYSRES_CONST_ROUTE_STEP_AND_CONDITION_RUS\x20SYSRES_CONST_ROUTE_STEP_OR_CONDITION_RUS\x20SYSRES_CONST_ROUTE_TYPE_COMPLEX\x20SYSRES_CONST_ROUTE_TYPE_PARALLEL\x20SYSRES_CONST_ROUTE_TYPE_SERIAL\x20SYSRES_CONST_SBDATASETDESC_NEGATIVE_VALUE\x20SYSRES_CONST_SBDATASETDESC_POSITIVE_VALUE\x20SYSRES_CONST_SBVIEWSDESC_POSITIVE_VALUE\x20SYSRES_CONST_SCRIPT_BLOCK_DESCRIPTION\x20SYSRES_CONST_SEARCH_BY_TEXT_REQUISITE_CODE\x20SYSRES_CONST_SEARCHES_COMPONENT_CONTENT\x20SYSRES_CONST_SEARCHES_CRITERIA_ACTION_NAME\x20SYSRES_CONST_SEARCHES_EDOC_CONTENT\x20SYSRES_CONST_SEARCHES_FOLDER_CONTENT\x20SYSRES_CONST_SEARCHES_JOB_CONTENT\x20SYSRES_CONST_SEARCHES_REFERENCE_CODE\x20SYSRES_CONST_SEARCHES_TASK_CONTENT\x20SYSRES_CONST_SECOND_CHAR\x20SYSRES_CONST_SECTION_REQUISITE_ACTIONS_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_CARD_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_CODE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_1_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_2_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_3_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_4_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_5_VALUE\x20SYSRES_CONST_SECTION_REQUISITE_DETAIL_6_VALUE\x20SYSRES_CONST_SELECT_REFERENCE_MODE_NAME\x20SYSRES_CONST_SELECT_TYPE_SELECTABLE\x20SYSRES_CONST_SELECT_TYPE_SELECTABLE_ONLY_CHILD\x20SYSRES_CONST_SELECT_TYPE_SELECTABLE_WITH_CHILD\x20SYSRES_CONST_SELECT_TYPE_UNSLECTABLE\x20SYSRES_CONST_SERVER_TYPE_MAIN\x20SYSRES_CONST_SERVICE_USER_CATEGORY_FIELD_VALUE\x20SYSRES_CONST_SETTINGS_USER_REQUISITE_CODE\x20SYSRES_CONST_SIGNATURE_AND_ENCODE_CERTIFICATE_TYPE_CODE\x20SYSRES_CONST_SIGNATURE_CERTIFICATE_TYPE_CODE\x20SYSRES_CONST_SINGULAR_TITLE_REQUISITE_CODE\x20SYSRES_CONST_SQL_SERVER_AUTHENTIFICATION_FLAG_VALUE_CODE\x20SYSRES_CONST_SQL_SERVER_ENCODE_AUTHENTIFICATION_FLAG_VALUE_CODE\x20SYSRES_CONST_STANDART_ROUTE_REFERENCE_CODE\x20SYSRES_CONST_STANDART_ROUTE_REFERENCE_COMMENT_REQUISITE_CODE\x20SYSRES_CONST_STANDART_ROUTES_GROUPS_REFERENCE_CODE\x20SYSRES_CONST_STATE_REQ_NAME\x20SYSRES_CONST_STATE_REQUISITE_ACTIVE_VALUE\x20SYSRES_CONST_STATE_REQUISITE_CLOSED_VALUE\x20SYSRES_CONST_STATE_REQUISITE_CODE\x20SYSRES_CONST_STATIC_ROLE_TYPE_CODE\x20SYSRES_CONST_STATUS_PLAN_DEFAULT_VALUE\x20SYSRES_CONST_STATUS_VALUE_AUTOCLEANING\x20SYSRES_CONST_STATUS_VALUE_BLUE_SQUARE\x20SYSRES_CONST_STATUS_VALUE_COMPLETE\x20SYSRES_CONST_STATUS_VALUE_GREEN_SQUARE\x20SYSRES_CONST_STATUS_VALUE_ORANGE_SQUARE\x20SYSRES_CONST_STATUS_VALUE_PURPLE_SQUARE\x20SYSRES_CONST_STATUS_VALUE_RED_SQUARE\x20SYSRES_CONST_STATUS_VALUE_SUSPEND\x20SYSRES_CONST_STATUS_VALUE_YELLOW_SQUARE\x20SYSRES_CONST_STDROUTE_SHOW_TO_USERS_REQUISITE_CODE\x20SYSRES_CONST_STORAGE_TYPE_FILE\x20SYSRES_CONST_STORAGE_TYPE_SQL_SERVER\x20SYSRES_CONST_STR_REQUISITE\x20SYSRES_CONST_STRIKEOUT_LIFE_CYCLE_STAGE_DRAW_STYLE\x20SYSRES_CONST_STRING_FORMAT_LEFT_ALIGN_CHAR\x20SYSRES_CONST_STRING_FORMAT_RIGHT_ALIGN_CHAR\x20SYSRES_CONST_STRING_REQUISITE_CODE\x20SYSRES_CONST_STRING_REQUISITE_TYPE\x20SYSRES_CONST_STRING_TYPE_CHAR\x20SYSRES_CONST_SUBSTITUTES_PSEUDOREFERENCE_CODE\x20SYSRES_CONST_SUBTASK_BLOCK_DESCRIPTION\x20SYSRES_CONST_SYSTEM_SETTING_CURRENT_USER_PARAM_VALUE\x20SYSRES_CONST_SYSTEM_SETTING_EMPTY_VALUE_PARAM_VALUE\x20SYSRES_CONST_SYSTEM_VERSION_COMMENT\x20SYSRES_CONST_TASK_ACCESS_TYPE_ALL\x20SYSRES_CONST_TASK_ACCESS_TYPE_ALL_MEMBERS\x20SYSRES_CONST_TASK_ACCESS_TYPE_MANUAL\x20SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION\x20SYSRES_CONST_TASK_ENCODE_TYPE_CERTIFICATION_AND_PASSWORD\x20SYSRES_CONST_TASK_ENCODE_TYPE_NONE\x20SYSRES_CONST_TASK_ENCODE_TYPE_PASSWORD\x20SYSRES_CONST_TASK_ROUTE_ALL_CONDITION\x20SYSRES_CONST_TASK_ROUTE_AND_CONDITION\x20SYSRES_CONST_TASK_ROUTE_OR_CONDITION\x20SYSRES_CONST_TASK_STATE_ABORTED\x20SYSRES_CONST_TASK_STATE_COMPLETE\x20SYSRES_CONST_TASK_STATE_CONTINUED\x20SYSRES_CONST_TASK_STATE_CONTROL\x20SYSRES_CONST_TASK_STATE_INIT\x20SYSRES_CONST_TASK_STATE_WORKING\x20SYSRES_CONST_TASK_TITLE\x20SYSRES_CONST_TASK_TYPES_GROUPS_REFERENCE_CODE\x20SYSRES_CONST_TASK_TYPES_REFERENCE_CODE\x20SYSRES_CONST_TEMPLATES_REFERENCE_CODE\x20SYSRES_CONST_TEST_DATE_REQUISITE_NAME\x20SYSRES_CONST_TEST_DEV_DATABASE_NAME\x20SYSRES_CONST_TEST_DEV_SYSTEM_CODE\x20SYSRES_CONST_TEST_EDMS_DATABASE_NAME\x20SYSRES_CONST_TEST_EDMS_MAIN_CODE\x20SYSRES_CONST_TEST_EDMS_MAIN_DB_NAME\x20SYSRES_CONST_TEST_EDMS_SECOND_CODE\x20SYSRES_CONST_TEST_EDMS_SECOND_DB_NAME\x20SYSRES_CONST_TEST_EDMS_SYSTEM_CODE\x20SYSRES_CONST_TEST_NUMERIC_REQUISITE_NAME\x20SYSRES_CONST_TEXT_REQUISITE\x20SYSRES_CONST_TEXT_REQUISITE_CODE\x20SYSRES_CONST_TEXT_REQUISITE_TYPE\x20SYSRES_CONST_TEXT_TYPE_CHAR\x20SYSRES_CONST_TYPE_CODE_REQUISITE_CODE\x20SYSRES_CONST_TYPE_REQUISITE_CODE\x20SYSRES_CONST_UNDEFINED_LIFE_CYCLE_STAGE_FONT_COLOR\x20SYSRES_CONST_UNITS_SECTION_ID_REQUISITE_CODE\x20SYSRES_CONST_UNITS_SECTION_REQUISITE_CODE\x20SYSRES_CONST_UNOPERATING_RECORD_FLAG_VALUE_CODE\x20SYSRES_CONST_UNSTORED_DATA_REQUISITE_CODE\x20SYSRES_CONST_UNSTORED_DATA_REQUISITE_NAME\x20SYSRES_CONST_USE_ACCESS_TYPE_CODE\x20SYSRES_CONST_USE_ACCESS_TYPE_NAME\x20SYSRES_CONST_USER_ACCOUNT_TYPE_VALUE_CODE\x20SYSRES_CONST_USER_ADDITIONAL_INFORMATION_REQUISITE_CODE\x20SYSRES_CONST_USER_AND_GROUP_ID_FROM_PSEUDOREFERENCE_REQUISITE_CODE\x20SYSRES_CONST_USER_CATEGORY_NORMAL\x20SYSRES_CONST_USER_CERTIFICATE_REQUISITE_CODE\x20SYSRES_CONST_USER_CERTIFICATE_STATE_REQUISITE_CODE\x20SYSRES_CONST_USER_CERTIFICATE_SUBJECT_NAME_REQUISITE_CODE\x20SYSRES_CONST_USER_CERTIFICATE_THUMBPRINT_REQUISITE_CODE\x20SYSRES_CONST_USER_COMMON_CATEGORY\x20SYSRES_CONST_USER_COMMON_CATEGORY_CODE\x20SYSRES_CONST_USER_FULL_NAME_REQUISITE_CODE\x20SYSRES_CONST_USER_GROUP_TYPE_REQUISITE_CODE\x20SYSRES_CONST_USER_LOGIN_REQUISITE_CODE\x20SYSRES_CONST_USER_REMOTE_CONTROLLER_REQUISITE_CODE\x20SYSRES_CONST_USER_REMOTE_SYSTEM_REQUISITE_CODE\x20SYSRES_CONST_USER_RIGHTS_T_REQUISITE_CODE\x20SYSRES_CONST_USER_SERVER_NAME_REQUISITE_CODE\x20SYSRES_CONST_USER_SERVICE_CATEGORY\x20SYSRES_CONST_USER_SERVICE_CATEGORY_CODE\x20SYSRES_CONST_USER_STATUS_ADMINISTRATOR_CODE\x20SYSRES_CONST_USER_STATUS_ADMINISTRATOR_NAME\x20SYSRES_CONST_USER_STATUS_DEVELOPER_CODE\x20SYSRES_CONST_USER_STATUS_DEVELOPER_NAME\x20SYSRES_CONST_USER_STATUS_DISABLED_CODE\x20SYSRES_CONST_USER_STATUS_DISABLED_NAME\x20SYSRES_CONST_USER_STATUS_SYSTEM_DEVELOPER_CODE\x20SYSRES_CONST_USER_STATUS_USER_CODE\x20SYSRES_CONST_USER_STATUS_USER_NAME\x20SYSRES_CONST_USER_STATUS_USER_NAME_DEPRECATED\x20SYSRES_CONST_USER_TYPE_FIELD_VALUE_USER\x20SYSRES_CONST_USER_TYPE_REQUISITE_CODE\x20SYSRES_CONST_USERS_CONTROLLER_REQUISITE_CODE\x20SYSRES_CONST_USERS_IS_MAIN_SERVER_REQUISITE_CODE\x20SYSRES_CONST_USERS_REFERENCE_CODE\x20SYSRES_CONST_USERS_REGISTRATION_CERTIFICATES_ACTION_NAME\x20SYSRES_CONST_USERS_REQUISITE_CODE\x20SYSRES_CONST_USERS_SYSTEM_REQUISITE_CODE\x20SYSRES_CONST_USERS_USER_ACCESS_RIGHTS_TYPR_REQUISITE_CODE\x20SYSRES_CONST_USERS_USER_AUTHENTICATION_REQUISITE_CODE\x20SYSRES_CONST_USERS_USER_COMPONENT_REQUISITE_CODE\x20SYSRES_CONST_USERS_USER_GROUP_REQUISITE_CODE\x20SYSRES_CONST_USERS_VIEW_CERTIFICATES_ACTION_NAME\x20SYSRES_CONST_VIEW_DEFAULT_CODE\x20SYSRES_CONST_VIEW_DEFAULT_NAME\x20SYSRES_CONST_VIEWER_REQUISITE_CODE\x20SYSRES_CONST_WAITING_BLOCK_DESCRIPTION\x20SYSRES_CONST_WIZARD_FORM_LABEL_TEST_STRING\x20\x20SYSRES_CONST_WIZARD_QUERY_PARAM_HEIGHT_ETALON_STRING\x20SYSRES_CONST_WIZARD_REFERENCE_COMMENT_REQUISITE_CODE\x20SYSRES_CONST_WORK_RULES_DESCRIPTION_REQUISITE_CODE\x20SYSRES_CONST_WORK_TIME_CALENDAR_REFERENCE_CODE\x20SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE\x20SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE\x20SYSRES_CONST_WORK_WORKFLOW_HARD_ROUTE_TYPE_VALUE_CODE_RUS\x20SYSRES_CONST_WORK_WORKFLOW_SOFT_ROUTE_TYPE_VALUE_CODE_RUS\x20SYSRES_CONST_WORKFLOW_ROUTE_TYPR_HARD\x20SYSRES_CONST_WORKFLOW_ROUTE_TYPR_SOFT\x20SYSRES_CONST_XML_ENCODING\x20SYSRES_CONST_XREC_STAT_REQUISITE_CODE\x20SYSRES_CONST_XRECID_FIELD_NAME\x20SYSRES_CONST_YES\x20SYSRES_CONST_YES_NO_2_REQUISITE_CODE\x20SYSRES_CONST_YES_NO_REQUISITE_CODE\x20SYSRES_CONST_YES_NO_T_REF_TYPE_REQUISITE_CODE\x20SYSRES_CONST_YES_PICK_VALUE\x20SYSRES_CONST_YES_VALUE\x20CR\x20FALSE\x20nil\x20NO_VALUE\x20NULL\x20TAB\x20TRUE\x20YES_VALUE\x20ADMINISTRATORS_GROUP_NAME\x20CUSTOMIZERS_GROUP_NAME\x20DEVELOPERS_GROUP_NAME\x20SERVICE_USERS_GROUP_NAME\x20DECISION_BLOCK_FIRST_OPERAND_PROPERTY\x20DECISION_BLOCK_NAME_PROPERTY\x20DECISION_BLOCK_OPERATION_PROPERTY\x20DECISION_BLOCK_RESULT_TYPE_PROPERTY\x20DECISION_BLOCK_SECOND_OPERAND_PROPERTY\x20ANY_FILE_EXTENTION\x20COMPRESSED_DOCUMENT_EXTENSION\x20EXTENDED_DOCUMENT_EXTENSION\x20SHORT_COMPRESSED_DOCUMENT_EXTENSION\x20SHORT_EXTENDED_DOCUMENT_EXTENSION\x20JOB_BLOCK_ABORT_DEADLINE_PROPERTY\x20JOB_BLOCK_AFTER_FINISH_EVENT\x20JOB_BLOCK_AFTER_QUERY_PARAMETERS_EVENT\x20JOB_BLOCK_ATTACHMENT_PROPERTY\x20JOB_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY\x20JOB_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY\x20JOB_BLOCK_BEFORE_QUERY_PARAMETERS_EVENT\x20JOB_BLOCK_BEFORE_START_EVENT\x20JOB_BLOCK_CREATED_JOBS_PROPERTY\x20JOB_BLOCK_DEADLINE_PROPERTY\x20JOB_BLOCK_EXECUTION_RESULTS_PROPERTY\x20JOB_BLOCK_IS_PARALLEL_PROPERTY\x20JOB_BLOCK_IS_RELATIVE_ABORT_DEADLINE_PROPERTY\x20JOB_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY\x20JOB_BLOCK_JOB_TEXT_PROPERTY\x20JOB_BLOCK_NAME_PROPERTY\x20JOB_BLOCK_NEED_SIGN_ON_PERFORM_PROPERTY\x20JOB_BLOCK_PERFORMER_PROPERTY\x20JOB_BLOCK_RELATIVE_ABORT_DEADLINE_TYPE_PROPERTY\x20JOB_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY\x20JOB_BLOCK_SUBJECT_PROPERTY\x20ENGLISH_LANGUAGE_CODE\x20RUSSIAN_LANGUAGE_CODE\x20smHidden\x20smMaximized\x20smMinimized\x20smNormal\x20wmNo\x20wmYes\x20COMPONENT_TOKEN_LINK_KIND\x20DOCUMENT_LINK_KIND\x20EDOCUMENT_LINK_KIND\x20FOLDER_LINK_KIND\x20JOB_LINK_KIND\x20REFERENCE_LINK_KIND\x20TASK_LINK_KIND\x20COMPONENT_TOKEN_LOCK_TYPE\x20EDOCUMENT_VERSION_LOCK_TYPE\x20MONITOR_BLOCK_AFTER_FINISH_EVENT\x20MONITOR_BLOCK_BEFORE_START_EVENT\x20MONITOR_BLOCK_DEADLINE_PROPERTY\x20MONITOR_BLOCK_INTERVAL_PROPERTY\x20MONITOR_BLOCK_INTERVAL_TYPE_PROPERTY\x20MONITOR_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY\x20MONITOR_BLOCK_NAME_PROPERTY\x20MONITOR_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY\x20MONITOR_BLOCK_SEARCH_SCRIPT_PROPERTY\x20NOTICE_BLOCK_AFTER_FINISH_EVENT\x20NOTICE_BLOCK_ATTACHMENT_PROPERTY\x20NOTICE_BLOCK_ATTACHMENTS_RIGHTS_GROUP_PROPERTY\x20NOTICE_BLOCK_ATTACHMENTS_RIGHTS_TYPE_PROPERTY\x20NOTICE_BLOCK_BEFORE_START_EVENT\x20NOTICE_BLOCK_CREATED_NOTICES_PROPERTY\x20NOTICE_BLOCK_DEADLINE_PROPERTY\x20NOTICE_BLOCK_IS_RELATIVE_DEADLINE_PROPERTY\x20NOTICE_BLOCK_NAME_PROPERTY\x20NOTICE_BLOCK_NOTICE_TEXT_PROPERTY\x20NOTICE_BLOCK_PERFORMER_PROPERTY\x20NOTICE_BLOCK_RELATIVE_DEADLINE_TYPE_PROPERTY\x20NOTICE_BLOCK_SUBJECT_PROPERTY\x20dseAfterCancel\x20dseAfterClose\x20dseAfterDelete\x20dseAfterDeleteOutOfTransaction\x20dseAfterInsert\x20dseAfterOpen\x20dseAfterScroll\x20dseAfterUpdate\x20dseAfterUpdateOutOfTransaction\x20dseBeforeCancel\x20dseBeforeClose\x20dseBeforeDelete\x20dseBeforeDetailUpdate\x20dseBeforeInsert\x20dseBeforeOpen\x20dseBeforeUpdate\x20dseOnAnyRequisiteChange\x20dseOnCloseRecord\x20dseOnDeleteError\x20dseOnOpenRecord\x20dseOnPrepareUpdate\x20dseOnUpdateError\x20dseOnUpdateRatifiedRecord\x20dseOnValidDelete\x20dseOnValidUpdate\x20reOnChange\x20reOnChangeValues\x20SELECTION_BEGIN_ROUTE_EVENT\x20SELECTION_END_ROUTE_EVENT\x20CURRENT_PERIOD_IS_REQUIRED\x20PREVIOUS_CARD_TYPE_NAME\x20SHOW_RECORD_PROPERTIES_FORM\x20ACCESS_RIGHTS_SETTING_DIALOG_CODE\x20ADMINISTRATOR_USER_CODE\x20ANALYTIC_REPORT_TYPE\x20asrtHideLocal\x20asrtHideRemote\x20CALCULATED_ROLE_TYPE_CODE\x20COMPONENTS_REFERENCE_DEVELOPER_VIEW_CODE\x20DCTS_TEST_PROTOCOLS_FOLDER_PATH\x20E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED\x20E_EDOC_VERSION_ALREADY_APPROVINGLY_SIGNED_BY_USER\x20E_EDOC_VERSION_ALREDY_SIGNED\x20E_EDOC_VERSION_ALREDY_SIGNED_BY_USER\x20EDOC_TYPES_CODE_REQUISITE_FIELD_NAME\x20EDOCUMENTS_ALIAS_NAME\x20FILES_FOLDER_PATH\x20FILTER_OPERANDS_DELIMITER\x20FILTER_OPERATIONS_DELIMITER\x20FORMCARD_NAME\x20FORMLIST_NAME\x20GET_EXTENDED_DOCUMENT_EXTENSION_CREATION_MODE\x20GET_EXTENDED_DOCUMENT_EXTENSION_IMPORT_MODE\x20INTEGRATED_REPORT_TYPE\x20IS_BUILDER_APPLICATION_ROLE\x20IS_BUILDER_APPLICATION_ROLE2\x20IS_BUILDER_USERS\x20ISBSYSDEV\x20LOG_FOLDER_PATH\x20mbCancel\x20mbNo\x20mbNoToAll\x20mbOK\x20mbYes\x20mbYesToAll\x20MEMORY_DATASET_DESRIPTIONS_FILENAME\x20mrNo\x20mrNoToAll\x20mrYes\x20mrYesToAll\x20MULTIPLE_SELECT_DIALOG_CODE\x20NONOPERATING_RECORD_FLAG_FEMININE\x20NONOPERATING_RECORD_FLAG_MASCULINE\x20OPERATING_RECORD_FLAG_FEMININE\x20OPERATING_RECORD_FLAG_MASCULINE\x20PROFILING_SETTINGS_COMMON_SETTINGS_CODE_VALUE\x20PROGRAM_INITIATED_LOOKUP_ACTION\x20ratDelete\x20ratEdit\x20ratInsert\x20REPORT_TYPE\x20REQUIRED_PICK_VALUES_VARIABLE\x20rmCard\x20rmList\x20SBRTE_PROGID_DEV\x20SBRTE_PROGID_RELEASE\x20STATIC_ROLE_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This includes not only those who have directly contributed through code and writing but also those who have helped by identifying issues, providing feedback, and offering suggestions. Below, you will find a list of all contributors. If you would like to contribute to this project, please visit our GitHub page for more information.\n\n\n\n\n\n\n\n\nVijay Janapa Reddi\n\n\nIkechukwu Uchendu\n\n\nNaeem Khoshnevis\n\n\nDouwe den Blanken\n\n\njasonjabbour\n\n\n\n\nMarcelo Rovai\n\n\nshanzehbatool\n\n\nMatthew Stewart\n\n\nElias Nuwara\n\n\nkai4avaya\n\n\n\n\nJared Ping\n\n\nItai Shapira\n\n\nMaximilian Lam\n\n\nJayson Lin\n\n\nAndrea\n\n\n\n\nJeffrey Ma\n\n\nSophia Cho\n\n\nAlex Rodriguez\n\n\nKorneel Van den Berghe\n\n\nColby Banbury\n\n\n\n\nZishen Wan\n\n\nSara Khosravi\n\n\nDivya Amirtharaj\n\n\nAbdulrahman Mahmoud\n\n\nSrivatsan Krishnan\n\n\n\n\nAghyad Deeb\n\n\nEmeka Ezike\n\n\narnaumarin\n\n\nAditi Raju\n\n\nELSuitorHarvard\n\n\n\n\nJared Ni\n\n\noishib\n\n\nHaoran Qiu\n\n\nMichael Schnebly\n\n\nEmil Njor\n\n\n\n\nHenry Bae\n\n\nMark Mazumder\n\n\nJae-Won Chung\n\n\nYu-Shun Hsiao\n\n\nMarco Zennaro\n\n\n\n\neurashin\n\n\nAndrew Bass\n\n\nPong Trairatvorakul\n\n\nJennifer Zhou\n\n\nShvetank Prakash\n\n\n\n\nAlex Oesterling\n\n\nAllen-Kuang\n\n\nBruno Scaglione\n\n\ngnodipac886\n\n\nGauri Jain\n\n\n\n\nFin Amin\n\n\nSercan Aygün\n\n\nBaldassarre Cesarano\n\n\nYang Zhou\n\n\nabigailswallow\n\n\n\n\nyanjingl\n\n\nJason Yik\n\n\nhappyappledog\n\n\nCurren Iyer\n\n\nEmmanuel Rassou\n\n\n\n\nJessica Quaye\n\n\nJason Yik\n\n\nShreya Johri\n\n\nJessica Quaye\n\n\nThe Random DIY\n\n\nSonia Murthy\n\n\nVijay Edupuganti\n\n\n\n\nCostin-Andrei Oncescu\n\n\nAnnie Laurie Cook\n\n\nVijay Edupuganti\n\n\nJothi Ramaswamy\n\n\n\n\nBatur Arslan\n\n\nCurren Iyer\n\n\nyanjingl\n\n\na-saraf\n\n\n\n\nsonghan\n\n\nZishen", + "text": "We extend our sincere thanks to the diverse group of individuals who have generously contributed their expertise, insights, time, and support to improve both the content and codebase of this project. This includes not only those who have directly contributed through code and writing but also those who have helped by identifying issues, providing feedback, and offering suggestions. Below, you will find a list of all contributors. If you would like to contribute to this project, please visit our GitHub page for more information.\n\n\n\n\n\n\n\n\nVijay Janapa Reddi\n\n\nIkechukwu Uchendu\n\n\nNaeem Khoshnevis\n\n\njasonjabbour\n\n\nDouwe den Blanken\n\n\n\n\nshanzehbatool\n\n\nMarcelo Rovai\n\n\nElias Nuwara\n\n\nkai4avaya\n\n\nJared Ping\n\n\n\n\nMatthew Stewart\n\n\nItai Shapira\n\n\nMaximilian Lam\n\n\nJayson Lin\n\n\nSophia Cho\n\n\n\n\nJeffrey Ma\n\n\nAndrea\n\n\nAlex Rodriguez\n\n\nKorneel Van den Berghe\n\n\nZishen Wan\n\n\n\n\nColby Banbury\n\n\nSara Khosravi\n\n\nDivya Amirtharaj\n\n\nAbdulrahman Mahmoud\n\n\nSrivatsan Krishnan\n\n\n\n\nmarin-llobet\n\n\nAghyad Deeb\n\n\noishib\n\n\nAditi Raju\n\n\nELSuitorHarvard\n\n\n\n\nJared Ni\n\n\nEmil Njor\n\n\nHaoran Qiu\n\n\nMichael Schnebly\n\n\nHenry Bae\n\n\n\n\nJae-Won Chung\n\n\nMark Mazumder\n\n\nYu-Shun Hsiao\n\n\nEmeka Ezike\n\n\neurashin\n\n\n\n\nJennifer Zhou\n\n\nMarco Zennaro\n\n\nShvetank Prakash\n\n\nAndrew Bass\n\n\nPong Trairatvorakul\n\n\n\n\nAllen-Kuang\n\n\nBruno Scaglione\n\n\nSercan Aygün\n\n\nGauri Jain\n\n\nFin Amin\n\n\n\n\ngnodipac886\n\n\nAlex Oesterling\n\n\nabigailswallow\n\n\nYang Zhou\n\n\nEmmanuel Rassou\n\n\n\n\nhappyappledog\n\n\nJessica Quaye\n\n\nJason Yik\n\n\nSonia Murthy\n\n\nShreya Johri\n\n\n\n\nThe Random DIY\n\n\nCostin-Andrei Oncescu\n\n\nBaldassarre Cesarano\n\n\nAnnie Laurie Cook\n\n\nVijay Edupuganti\n\n\n\n\nJothi Ramaswamy\n\n\nBatur Arslan\n\n\nCurren Iyer\n\n\nyanjingl\n\n\na-saraf\n\n\n\n\nsonghan\n\n\nZishen", "crumbs": [ "FRONT MATTER", "Contributors & Thanks" @@ -136,7 +136,7 @@ "href": "contents/introduction/introduction.html", "title": "1  Introduction", "section": "", - "text": "1.1 Overview\nIn the early 1990s, Mark Weiser, a pioneering computer scientist, introduced the world to a revolutionary concept that would forever change how we interact with technology. He envisioned a future where computing would be seamlessly integrated into our environments, becoming an invisible, integral part of daily life. This vision, which he termed “ubiquitous computing,” promised a world where technology would serve us without demanding our constant attention or interaction. Fast forward to today, and we find ourselves on the cusp of realizing Weiser’s vision, thanks to the advent and proliferation of machine learning systems.\nIn the vision of ubiquitous computing (Weiser 1991), the integration of processors into everyday objects is just one aspect of a larger paradigm shift. The true essence of this vision lies in creating an intelligent environment that can anticipate our needs and act on our behalf, enhancing our experiences without requiring explicit commands. To achieve this level of pervasive intelligence, it is crucial to develop and deploy machine learning systems that span the entire ecosystem, from the cloud to the edge and even to the tiniest IoT devices.\nBy distributing machine learning capabilities across the computing continuum, we can harness the strengths of each layer while mitigating their limitations. The cloud, with its vast computational resources and storage capacity, is ideal for training complex models on large datasets and performing resource-intensive tasks. Edge devices, such as gateways and smartphones, can process data locally, enabling faster response times, improved privacy, and reduced bandwidth requirements. Finally, the tiniest IoT devices, equipped with machine learning capabilities, can make quick decisions based on sensor data, enabling highly responsive and efficient systems.\nThis distributed intelligence is particularly crucial for applications that require real-time processing, such as autonomous vehicles, industrial automation, and smart healthcare. By processing data at the most appropriate layer of the computing continuum, we can ensure that decisions are made quickly and accurately, without relying on constant communication with a central server.\nThe migration of machine learning intelligence across the ecosystem also enables more personalized and context-aware experiences. By learning from user behavior and preferences at the edge, devices can adapt to individual needs without compromising privacy. This localized intelligence can then be aggregated and refined in the cloud, creating a feedback loop that continuously improves the overall system.\nHowever, deploying machine learning systems across the computing continuum presents several challenges. Ensuring the interoperability and seamless integration of these systems requires standardized protocols and interfaces. Security and privacy concerns must also be addressed, as the distribution of intelligence across multiple layers increases the attack surface and the potential for data breaches.\nFurthermore, the varying computational capabilities and energy constraints of devices at different layers of the computing continuum necessitate the development of efficient and adaptable machine learning models. Techniques such as model compression, federated learning, and transfer learning can help address these challenges, enabling the deployment of intelligence across a wide range of devices.\nAs we move towards the realization of Weiser’s vision of ubiquitous computing, the development and deployment of machine learning systems across the entire ecosystem will be critical. By leveraging the strengths of each layer of the computing continuum, we can create an intelligent environment that seamlessly integrates with our daily lives, anticipating our needs and enhancing our experiences in ways that were once unimaginable. As we continue to push the boundaries of what’s possible with distributed machine learning, we inch closer to a future where technology becomes an invisible but integral part of our world.", + "text": "1.1 Overview\nIn the early 1990s, Mark Weiser, a pioneering computer scientist, introduced the world to a revolutionary concept that would forever change how we interact with technology. This was succintly captured in the paper he wrote on “The Computer for the 21st Century” (Figure 1.1). He envisioned a future where computing would be seamlessly integrated into our environments, becoming an invisible, integral part of daily life. This vision, which he termed “ubiquitous computing,” promised a world where technology would serve us without demanding our constant attention or interaction. Fast forward to today, and we find ourselves on the cusp of realizing Weiser’s vision, thanks to the advent and proliferation of machine learning systems.\nIn the vision of ubiquitous computing (Weiser 1991), the integration of processors into everyday objects is just one aspect of a larger paradigm shift. The true essence of this vision lies in creating an intelligent environment that can anticipate our needs and act on our behalf, enhancing our experiences without requiring explicit commands. To achieve this level of pervasive intelligence, it is crucial to develop and deploy machine learning systems that span the entire ecosystem, from the cloud to the edge and even to the tiniest IoT devices.\nBy distributing machine learning capabilities across the computing continuum, we can harness the strengths of each layer while mitigating their limitations. The cloud, with its vast computational resources and storage capacity, is ideal for training complex models on large datasets and performing resource-intensive tasks. Edge devices, such as gateways and smartphones, can process data locally, enabling faster response times, improved privacy, and reduced bandwidth requirements. Finally, the tiniest IoT devices, equipped with machine learning capabilities, can make quick decisions based on sensor data, enabling highly responsive and efficient systems.\nThis distributed intelligence is particularly crucial for applications that require real-time processing, such as autonomous vehicles, industrial automation, and smart healthcare. By processing data at the most appropriate layer of the computing continuum, we can ensure that decisions are made quickly and accurately, without relying on constant communication with a central server.\nThe migration of machine learning intelligence across the ecosystem also enables more personalized and context-aware experiences. By learning from user behavior and preferences at the edge, devices can adapt to individual needs without compromising privacy. This localized intelligence can then be aggregated and refined in the cloud, creating a feedback loop that continuously improves the overall system.\nHowever, deploying machine learning systems across the computing continuum presents several challenges. Ensuring the interoperability and seamless integration of these systems requires standardized protocols and interfaces. Security and privacy concerns must also be addressed, as the distribution of intelligence across multiple layers increases the attack surface and the potential for data breaches.\nFurthermore, the varying computational capabilities and energy constraints of devices at different layers of the computing continuum necessitate the development of efficient and adaptable machine learning models. Techniques such as model compression, federated learning, and transfer learning can help address these challenges, enabling the deployment of intelligence across a wide range of devices.\nAs we move towards the realization of Weiser’s vision of ubiquitous computing, the development and deployment of machine learning systems across the entire ecosystem will be critical. By leveraging the strengths of each layer of the computing continuum, we can create an intelligent environment that seamlessly integrates with our daily lives, anticipating our needs and enhancing our experiences in ways that were once unimaginable. As we continue to push the boundaries of what’s possible with distributed machine learning, we inch closer to a future where technology becomes an invisible but integral part of our world.", "crumbs": [ "Fundamentals", "1  Introduction" @@ -235,7 +235,7 @@ "href": "contents/ml_systems/ml_systems.html#edge-ml", "title": "2  ML Systems", "section": "2.3 Edge ML", - "text": "2.3 Edge ML\n\n2.3.1 Characteristics\nDefinition of Edge ML\nEdge Machine Learning (Edge ML) runs machine learning algorithms directly on endpoint devices or closer to where the data is generated rather than relying on centralized cloud servers. This approach aims to bring computation closer to the data source, reducing the need to send large volumes of data over networks, often resulting in lower latency and improved data privacy.\nDecentralized Data Processing\nIn Edge ML, data processing happens in a decentralized fashion. Instead of sending data to remote servers, the data is processed locally on devices like smartphones, tablets, or Internet of Things (IoT) devices (Figure 2.4). This local processing allows devices to make quick decisions based on the data they collect without relying heavily on a central server’s resources. This decentralization is particularly important in real-time applications where even a slight delay can have significant consequences.\nLocal Data Storage and Computation\nLocal data storage and computation are key features of Edge ML. This setup ensures that data can be stored and analyzed directly on the devices, thereby maintaining the privacy of the data and reducing the need for constant internet connectivity. Moreover, this often leads to more efficient computation, as data doesn’t have to travel long distances, and computations are performed with a more nuanced understanding of the local context, which can sometimes result in more insightful analyses.\n\n\n\n\n\n\nFigure 2.4: Edge ML Examples. Source: Edge Impulse.\n\n\n\n\n\n2.3.2 Benefits\nReduced Latency\nOne of Edge ML’s main advantages is the significant latency reduction compared to Cloud ML. This reduced latency can be a critical benefit in situations where milliseconds count, such as in autonomous vehicles, where quick decision-making can mean the difference between safety and an accident.\nEnhanced Data Privacy\nEdge ML also offers improved data privacy, as data is primarily stored and processed locally. This minimizes the risk of data breaches that are more common in centralized data storage solutions. Sensitive information can be kept more secure, as it’s not sent over networks that could be intercepted.\nLower Bandwidth Usage\nOperating closer to the data source means less data must be sent over networks, reducing bandwidth usage. This can result in cost savings and efficiency gains, especially in environments where bandwidth is limited or costly.\n\n\n2.3.3 Challenges\nLimited Computational Resources Compared to Cloud ML\nHowever, Edge ML has its challenges. One of the main concerns is the limited computational resources compared to cloud-based solutions. Endpoint devices may have a different processing power or storage capacity than cloud servers, limiting the complexity of the machine learning models that can be deployed.\nComplexity in Managing Edge Nodes\nManaging a network of edge nodes can introduce complexity, especially regarding coordination, updates, and maintenance. Ensuring all nodes operate seamlessly and are up-to-date with the latest algorithms and security protocols can be a logistical challenge.\nSecurity Concerns at the Edge Nodes\nWhile Edge ML offers enhanced data privacy, edge nodes can sometimes be more vulnerable to physical and cyber-attacks. Developing robust security protocols that protect data at each node without compromising the system’s efficiency remains a significant challenge in deploying Edge ML solutions.\n\n\n2.3.4 Example Use Cases\nEdge ML has many applications, from autonomous vehicles and smart homes to industrial Internet of Things (IoT). These examples were chosen to highlight scenarios where real-time data processing, reduced latency, and enhanced privacy are not just beneficial but often critical to the operation and success of these technologies. They demonstrate the role that Edge ML can play in driving advancements in various sectors, fostering innovation, and paving the way for more intelligent, responsive, and adaptive systems.\nAutonomous Vehicles\nAutonomous vehicles stand as a prime example of Edge ML’s potential. These vehicles rely heavily on real-time data processing to navigate and make decisions. Localized machine learning models assist in quickly analyzing data from various sensors to make immediate driving decisions, ensuring safety and smooth operation.\nSmart Homes and Buildings\nEdge ML plays a crucial role in efficiently managing various systems in smart homes and buildings, from lighting and heating to security. By processing data locally, these systems can operate more responsively and harmoniously with the occupants’ habits and preferences, creating a more comfortable living environment.\nIndustrial IoT\nThe Industrial IoT leverages Edge ML to monitor and control complex industrial processes. Here, machine learning models can analyze data from numerous sensors in real-time, enabling predictive maintenance, optimizing operations, and enhancing safety measures. This revolution in industrial automation and efficiency is transforming manufacturing and production across various sectors.\nThe applicability of Edge ML is vast and not limited to these examples. Various other sectors, including healthcare, agriculture, and urban planning, are exploring and integrating Edge ML to develop innovative solutions responsive to real-world needs and challenges, heralding a new era of smart, interconnected systems.", + "text": "2.3 Edge ML\n\n2.3.1 Characteristics\nDefinition of Edge ML\nEdge Machine Learning (Edge ML) runs machine learning algorithms directly on endpoint devices or closer to where the data is generated rather than relying on centralized cloud servers. This approach brings computation closer to the data source, reducing the need to send large volumes of data over networks, often resulting in lower latency and improved data privacy.\nDecentralized Data Processing\nIn Edge ML, data processing happens in a decentralized fashion. Instead of sending data to remote servers, the data is processed locally on devices like smartphones, tablets, or Internet of Things (IoT) devices (Figure 2.4). This local processing allows devices to make quick decisions based on the data they collect without relying heavily on a central server’s resources. This decentralization is particularly important in real-time applications where even a slight delay can have significant consequences.\nLocal Data Storage and Computation\nLocal data storage and computation are key features of Edge ML. This setup ensures that data can be stored and analyzed directly on the devices, thereby maintaining the privacy of the data and reducing the need for constant internet connectivity. Moreover, this often leads to more efficient computation, as data doesn’t have to travel long distances, and computations are performed with a more nuanced understanding of the local context, which can sometimes result in more insightful analyses.\n\n\n\n\n\n\nFigure 2.4: Edge ML Examples. Source: Edge Impulse.\n\n\n\n\n\n2.3.2 Benefits\nReduced Latency\nOne of Edge ML’s main advantages is the significant latency reduction compared to Cloud ML. This reduced latency can be a critical benefit in situations where milliseconds count, such as in autonomous vehicles, where quick decision-making can mean the difference between safety and an accident.\nEnhanced Data Privacy\nEdge ML also offers improved data privacy, as data is primarily stored and processed locally. This minimizes the risk of data breaches that are more common in centralized data storage solutions. Sensitive information can be kept more secure, as it’s not sent over networks that could be intercepted.\nLower Bandwidth Usage\nOperating closer to the data source means less data must be sent over networks, reducing bandwidth usage. This can result in cost savings and efficiency gains, especially in environments where bandwidth is limited or costly.\n\n\n2.3.3 Challenges\nLimited Computational Resources Compared to Cloud ML\nHowever, Edge ML has its challenges. One of the main concerns is the limited computational resources compared to cloud-based solutions. Endpoint devices may have a different processing power or storage capacity than cloud servers, limiting the complexity of the machine learning models that can be deployed.\nComplexity in Managing Edge Nodes\nManaging a network of edge nodes can introduce complexity, especially regarding coordination, updates, and maintenance. Ensuring all nodes operate seamlessly and are up-to-date with the latest algorithms and security protocols can be a logistical challenge.\nSecurity Concerns at the Edge Nodes\nWhile Edge ML offers enhanced data privacy, edge nodes can sometimes be more vulnerable to physical and cyber-attacks. Developing robust security protocols that protect data at each node without compromising the system’s efficiency remains a significant challenge in deploying Edge ML solutions.\n\n\n2.3.4 Example Use Cases\nEdge ML has many applications, from autonomous vehicles and smart homes to industrial Internet of Things (IoT). These examples were chosen to highlight scenarios where real-time data processing, reduced latency, and enhanced privacy are not just beneficial but often critical to the operation and success of these technologies. They demonstrate the role that Edge ML can play in driving advancements in various sectors, fostering innovation, and paving the way for more intelligent, responsive, and adaptive systems.\nAutonomous Vehicles\nAutonomous vehicles stand as a prime example of Edge ML’s potential. These vehicles rely heavily on real-time data processing to navigate and make decisions. Localized machine learning models assist in quickly analyzing data from various sensors to make immediate driving decisions, ensuring safety and smooth operation.\nSmart Homes and Buildings\nEdge ML plays a crucial role in efficiently managing various systems in smart homes and buildings, from lighting and heating to security. By processing data locally, these systems can operate more responsively and harmoniously with the occupants’ habits and preferences, creating a more comfortable living environment.\nIndustrial IoT\nThe Industrial IoT leverages Edge ML to monitor and control complex industrial processes. Here, machine learning models can analyze data from numerous sensors in real-time, enabling predictive maintenance, optimizing operations, and enhancing safety measures. This revolution in industrial automation and efficiency is transforming manufacturing and production across various sectors.\nThe applicability of Edge ML is vast and not limited to these examples. Various other sectors, including healthcare, agriculture, and urban planning, are exploring and integrating Edge ML to develop innovative solutions responsive to real-world needs and challenges, heralding a new era of smart, interconnected systems.", "crumbs": [ "Fundamentals", "2  ML Systems" @@ -257,7 +257,7 @@ "href": "contents/ml_systems/ml_systems.html#comparison", "title": "2  ML Systems", "section": "2.5 Comparison", - "text": "2.5 Comparison\nUp to this point, we’ve explored each of the different ML variants individually. Now, let’s bring them all together for a comprehensive view. Table 2.1 offers a comparative analysis of Cloud ML, Edge ML, and TinyML based on various features and aspects. This comparison aims to provide a clear perspective on the unique advantages and distinguishing factors, aiding in making informed decisions based on the specific needs and constraints of a given application or project.\n\n\n\nTable 2.1: Comparison of feature aspects across Cloud ML, Edge ML, and TinyML.\n\n\n\n\n\n\n\n\n\n\n\nAspect\nCloud ML\nEdge ML\nTinyML\n\n\n\n\nProcessing Location\nCentralized servers (Data Centers)\nLocal devices (closer to data sources)\nOn-device (microcontrollers, embedded systems)\n\n\nLatency\nHigh (Depends on internet connectivity)\nModerate (Reduced latency compared to Cloud ML)\nLow (Immediate processing without network delay)\n\n\nData Privacy\nModerate (Data transmitted over networks)\nHigh (Data remains on local networks)\nVery High (Data processed on-device, not transmitted)\n\n\nComputational Power\nHigh (Utilizes powerful data center infrastructure)\nModerate (Utilizes local device capabilities)\nLow (Limited to the power of the embedded system)\n\n\nEnergy Consumption\nHigh (Data centers consume significant energy)\nModerate (Less than data centers, more than TinyML)\nLow (Highly energy-efficient, designed for low power)\n\n\nScalability\nHigh (Easy to scale with additional server resources)\nModerate (Depends on local device capabilities)\nLow (Limited by the hardware resources of the device)\n\n\nCost\nHigh (Recurring costs for server usage, maintenance)\nVariable (Depends on the complexity of local setup)\nLow (Primarily upfront costs for hardware components)\n\n\nConnectivity\nHigh (Requires stable internet connectivity)\nLow (Can operate with intermittent connectivity)\nVery Low (Can operate without any network connectivity)\n\n\nReal-time Processing\nModerate (Can be affected by network latency)\nHigh (Capable of real-time processing locally)\nVery High (Immediate processing with minimal latency)\n\n\nApplication Examples\nBig Data Analysis, Virtual Assistants\nAutonomous Vehicles, Smart Homes\nWearables, Sensor Networks\n\n\nComplexity\nModerate to High (Requires knowledge in cloud computing)\nModerate (Requires knowledge in local network setup)\nModerate to High (Requires expertise in embedded systems)", + "text": "2.5 Comparison\nUp to this point, we’ve explored each of the different ML variants individually. Now, let’s bring them all together for a comprehensive view. Table 2.1 offers a comparative analysis of Cloud ML, Edge ML, and TinyML based on various features and aspects. This comparison provides a clear perspective on the unique advantages and distinguishing factors, aiding in making informed decisions based on the specific needs and constraints of a given application or project.\n\n\n\nTable 2.1: Comparison of feature aspects across Cloud ML, Edge ML, and TinyML.\n\n\n\n\n\n\n\n\n\n\n\nAspect\nCloud ML\nEdge ML\nTinyML\n\n\n\n\nProcessing Location\nCentralized servers (Data Centers)\nLocal devices (closer to data sources)\nOn-device (microcontrollers, embedded systems)\n\n\nLatency\nHigh (Depends on internet connectivity)\nModerate (Reduced latency compared to Cloud ML)\nLow (Immediate processing without network delay)\n\n\nData Privacy\nModerate (Data transmitted over networks)\nHigh (Data remains on local networks)\nVery High (Data processed on-device, not transmitted)\n\n\nComputational Power\nHigh (Utilizes powerful data center infrastructure)\nModerate (Utilizes local device capabilities)\nLow (Limited to the power of the embedded system)\n\n\nEnergy Consumption\nHigh (Data centers consume significant energy)\nModerate (Less than data centers, more than TinyML)\nLow (Highly energy-efficient, designed for low power)\n\n\nScalability\nHigh (Easy to scale with additional server resources)\nModerate (Depends on local device capabilities)\nLow (Limited by the hardware resources of the device)\n\n\nCost\nHigh (Recurring costs for server usage, maintenance)\nVariable (Depends on the complexity of local setup)\nLow (Primarily upfront costs for hardware components)\n\n\nConnectivity\nHigh (Requires stable internet connectivity)\nLow (Can operate with intermittent connectivity)\nVery Low (Can operate without any network connectivity)\n\n\nReal-time Processing\nModerate (Can be affected by network latency)\nHigh (Capable of real-time processing locally)\nVery High (Immediate processing with minimal latency)\n\n\nApplication Examples\nBig Data Analysis, Virtual Assistants\nAutonomous Vehicles, Smart Homes\nWearables, Sensor Networks\n\n\nComplexity\nModerate to High (Requires knowledge in cloud computing)\nModerate (Requires knowledge in local network setup)\nModerate to High (Requires expertise in embedded systems)", "crumbs": [ "Fundamentals", "2  ML Systems" @@ -301,7 +301,7 @@ "href": "contents/dl_primer/dl_primer.html#introduction", "title": "3  DL Primer", "section": "", - "text": "3.1.1 Definition and Importance\nDeep learning, a specialized area within machine learning and artificial intelligence (AI), utilizes algorithms modeled after the structure and function of the human brain, known as artificial neural networks. This field is a foundational element in AI, driving progress in diverse sectors such as computer vision, natural language processing, and self-driving vehicles. Its significance in embedded AI systems is highlighted by its capability to handle intricate calculations and predictions, optimizing the limited resources in embedded settings. Figure 3.1 illustrates the chronological development and relative segmentation of the three fields.\n\n\n\n\n\n\nFigure 3.1: The diagram illustrates artificial intelligence as the overarching field encompassing all computational methods that mimic human cognitive functions. Machine learning is a subset of AI that includes algorithms capable of learning from data. Deep learning, a further subset of ML, specifically involves neural networks that are able to learn more complex patterns in large volumes of data. Source: NVIDIA.\n\n\n\n\n\n3.1.2 Brief History of Deep Learning\nThe idea of deep learning has origins in early artificial neural networks. It has experienced several cycles of interest, starting with the introduction of the Perceptron in the 1950s (Rosenblatt 1957), followed by the invention of backpropagation algorithms in the 1980s (Rumelhart, Hinton, and Williams 1986).\n\nRosenblatt, Frank. 1957. The Perceptron, a Perceiving and Recognizing Automaton Project Para. Cornell Aeronautical Laboratory.\n\nRumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. 1986. “Learning Representations by Back-Propagating Errors.” Nature 323 (6088): 533–36. https://doi.org/10.1038/323533a0.\n\nKrizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a Meeting Held December 3-6, 2012, Lake Tahoe, Nevada, United States, edited by Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q. Weinberger, 1106–14. https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html.\nThe term “deep learning” became prominent in the 2000s, characterized by advances in computational power and data accessibility. Important milestones include the successful training of deep networks like AlexNet (Krizhevsky, Sutskever, and Hinton 2012) by Geoffrey Hinton, a leading figure in AI, and the renewed focus on neural networks as effective tools for data analysis and modeling.\nDeep learning has recently seen exponential growth, transforming various industries. Computational growth followed an 18-month doubling pattern from 1952 to 2010, which then accelerated to a 6-month cycle from 2010 to 2022, as shown in Figure 3.2. Concurrently, we saw the emergence of large-scale models between 2015 and 2022, appearing 2 to 3 orders of magnitude faster and following a 10-month doubling cycle.\n\n\n\n\n\n\nFigure 3.2: Growth of deep learning models.\n\n\n\nMultiple factors have contributed to this surge, including advancements in computational power, the abundance of big data, and improvements in algorithmic designs. First, the growth of computational capabilities, especially the arrival of Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) (Jouppi et al. 2017), has significantly sped up the training and inference times of deep learning models. These hardware improvements have enabled the construction and training of more complex, deeper networks than what was possible in earlier years.\n\nJouppi, Norman P., Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, et al. 2017. “In-Datacenter Performance Analysis of a Tensor Processing Unit.” In Proceedings of the 44th Annual International Symposium on Computer Architecture, 1–12. ISCA ’17. New York, NY, USA: ACM. https://doi.org/10.1145/3079856.3080246.\nSecond, the digital revolution has yielded a wealth of big data, offering rich material for deep learning models to learn from and excel in tasks such as image and speech recognition, language translation, and game playing. Large, labeled datasets have been key in refining and successfully deploying deep learning applications in real-world settings.\nAdditionally, collaborations and open-source efforts have nurtured a dynamic community of researchers and practitioners, accelerating advancements in deep learning techniques. Innovations like deep reinforcement learning, transfer learning, and generative artificial intelligence have broadened the scope of what is achievable with deep learning, opening new possibilities in various sectors, including healthcare, finance, transportation, and entertainment.\nOrganizations worldwide recognize deep learning’s transformative potential and invest heavily in research and development to leverage its capabilities in providing innovative solutions, optimizing operations, and creating new business opportunities. As deep learning continues its upward trajectory, it is set to redefine how we interact with technology, enhancing convenience, safety, and connectivity in our lives.\n\n\n3.1.3 Applications of Deep Learning\nDeep learning is extensively used across numerous industries today, and its transformative impact on society is evident. In finance, it powers stock market prediction, risk assessment, and fraud detection. For instance, deep learning algorithms can predict stock market trends, guide investment strategies, and improve financial decisions. In marketing, it drives customer segmentation, personalization, and content optimization. Deep learning analyzes consumer behavior and preferences to enable highly targeted advertising and personalized content delivery. In manufacturing, deep learning streamlines production processes and enhances quality control by continuously analyzing large volumes of data. This allows companies to boost productivity and minimize waste, leading to the production of higher quality goods at lower costs. In healthcare, machine learning aids in diagnosis, treatment planning, and patient monitoring. Similarly, deep learning can make medical predictions that improve patient diagnosis and save lives. The benefits are clear: machine learning predicts with greater accuracy than humans and does so much more quickly.\nDeep learning enhances everyday products, such as strengthening Netflix’s recommender systems to provide users with more personalized recommendations. At Google, deep learning models have driven significant improvements in Google Translate, enabling it to handle over 100 languages. Autonomous vehicles from companies like Waymo, Cruise, and Motional have become a reality through the use of deep learning in their perception system. Additionally, Amazon employs deep learning at the edge in their Alexa devices to perform keyword spotting.\n\n\n3.1.4 Relevance to Embedded AI\nEmbedded AI, the integration of AI algorithms directly into hardware devices, naturally gains from deep learning capabilities. Combining deep learning algorithms and embedded systems has laid the groundwork for intelligent, autonomous devices capable of advanced on-device data processing and analysis. Deep learning aids in extracting complex patterns and information from input data, which is essential in developing smart embedded systems, from household appliances to industrial machinery. This collaboration aims to usher in a new era of intelligent, interconnected devices that can learn and adapt to user behavior and environmental conditions, optimizing performance and offering unprecedented convenience and efficiency.", + "text": "3.1.1 Definition and Importance\nDeep learning, a specialized area within machine learning and artificial intelligence (AI), utilizes algorithms modeled after the structure and function of the human brain, known as artificial neural networks. This field is a foundational element in AI, driving progress in diverse sectors such as computer vision, natural language processing, and self-driving vehicles. Its significance in embedded AI systems is highlighted by its capability to handle intricate calculations and predictions, optimizing the limited resources in embedded settings. Figure 3.1 illustrates the chronological development and relative segmentation of the three fields.\n\n\n\n\n\n\nFigure 3.1: The diagram illustrates artificial intelligence as the overarching field encompassing all computational methods that mimic human cognitive functions. Machine learning is a subset of AI that includes algorithms capable of learning from data. Deep learning, a further subset of ML, specifically involves neural networks that are able to learn more complex patterns in large volumes of data. Source: NVIDIA.\n\n\n\n\n\n3.1.2 Brief History of Deep Learning\nThe idea of deep learning has origins in early artificial neural networks. It has experienced several cycles of interest, starting with the introduction of the Perceptron in the 1950s (Rosenblatt 1957), followed by the invention of backpropagation algorithms in the 1980s (Rumelhart, Hinton, and Williams 1986).\n\nRosenblatt, Frank. 1957. The Perceptron, a Perceiving and Recognizing Automaton Project Para. Cornell Aeronautical Laboratory.\n\nRumelhart, David E., Geoffrey E. Hinton, and Ronald J. Williams. 1986. “Learning Representations by Back-Propagating Errors.” Nature 323 (6088): 533–36. https://doi.org/10.1038/323533a0.\n\nKrizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a Meeting Held December 3-6, 2012, Lake Tahoe, Nevada, United States, edited by Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q. Weinberger, 1106–14. https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html.\nThe term “deep learning” became prominent in the 2000s, characterized by advances in computational power and data accessibility. Important milestones include the successful training of deep networks like AlexNet (Krizhevsky, Sutskever, and Hinton 2012) by Geoffrey Hinton, a leading figure in AI, and the renewed focus on neural networks as effective tools for data analysis and modeling.\nDeep learning has recently seen exponential growth, transforming various industries. Computational growth followed an 18-month doubling pattern from 1952 to 2010, which then accelerated to a 6-month cycle from 2010 to 2022, as shown in Figure 3.2. Concurrently, we saw the emergence of large-scale models between 2015 and 2022, appearing 2 to 3 orders of magnitude faster and following a 10-month doubling cycle.\n\n\n\n\n\n\nFigure 3.2: Growth of deep learning models.\n\n\n\nMultiple factors have contributed to this surge, including advancements in computational power, the abundance of big data, and improvements in algorithmic designs. First, the growth of computational capabilities, especially the arrival of Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) (Jouppi et al. 2017), has significantly sped up the training and inference times of deep learning models. These hardware improvements have enabled the construction and training of more complex, deeper networks than what was possible in earlier years.\n\nJouppi, Norman P., Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, et al. 2017. “In-Datacenter Performance Analysis of a Tensor Processing Unit.” In Proceedings of the 44th Annual International Symposium on Computer Architecture, 1–12. ISCA ’17. New York, NY, USA: ACM. https://doi.org/10.1145/3079856.3080246.\nSecond, the digital revolution has yielded a wealth of big data, offering rich material for deep learning models to learn from and excel in tasks such as image and speech recognition, language translation, and game playing. Large, labeled datasets have been key in refining and successfully deploying deep learning applications in real-world settings.\nAdditionally, collaborations and open-source efforts have nurtured a dynamic community of researchers and practitioners, accelerating advancements in deep learning techniques. Innovations like deep reinforcement learning, transfer learning, and generative artificial intelligence have broadened the scope of what is achievable with deep learning, opening new possibilities in various sectors, including healthcare, finance, transportation, and entertainment.\nOrganizations worldwide recognize deep learning’s transformative potential and invest heavily in research and development to leverage its capabilities in providing innovative solutions, optimizing operations, and creating new business opportunities. As deep learning continues its upward trajectory, it is set to redefine how we interact with technology, enhancing convenience, safety, and connectivity in our lives.\n\n\n3.1.3 Applications of Deep Learning\nDeep learning is extensively used across numerous industries today, and its transformative impact on society is evident. In finance, it powers stock market prediction, risk assessment, and fraud detection. For instance, deep learning algorithms can predict stock market trends, guide investment strategies, and improve financial decisions. In marketing, it drives customer segmentation, personalization, and content optimization. Deep learning analyzes consumer behavior and preferences to enable highly targeted advertising and personalized content delivery. In manufacturing, deep learning streamlines production processes and enhances quality control by continuously analyzing large volumes of data. This allows companies to boost productivity and minimize waste, leading to the production of higher quality goods at lower costs. In healthcare, machine learning aids in diagnosis, treatment planning, and patient monitoring. Similarly, deep learning can make medical predictions that improve patient diagnosis and save lives. The benefits are clear: machine learning predicts with greater accuracy than humans and does so much more quickly.\nDeep learning enhances everyday products, such as strengthening Netflix’s recommender systems to provide users with more personalized recommendations. At Google, deep learning models have driven significant improvements in Google Translate, enabling it to handle over 100 languages. Autonomous vehicles from companies like Waymo, Cruise, and Motional have become a reality through the use of deep learning in their perception system. Additionally, Amazon employs deep learning at the edge in their Alexa devices to perform keyword spotting.\n\n\n3.1.4 Relevance to Embedded AI\nEmbedded AI, the integration of AI algorithms directly into hardware devices, naturally gains from deep learning capabilities. Combining deep learning algorithms and embedded systems has laid the groundwork for intelligent, autonomous devices capable of advanced on-device data processing and analysis. Deep learning aids in extracting complex patterns and information from input data, which is essential in developing smart embedded systems, from household appliances to industrial machinery. This collaboration ushers in a new era of intelligent, interconnected devices that can learn and adapt to user behavior and environmental conditions, optimizing performance and offering unprecedented convenience and efficiency.", "crumbs": [ "Fundamentals", "3  DL Primer" @@ -312,7 +312,7 @@ "href": "contents/dl_primer/dl_primer.html#neural-networks", "title": "3  DL Primer", "section": "3.2 Neural Networks", - "text": "3.2 Neural Networks\nDeep learning draws inspiration from the human brain’s neural networks to create decision-making patterns. This section digs into the foundational concepts of deep learning, providing insights into the more complex topics discussed later in this primer.\nNeural networks serve as the foundation of deep learning, inspired by the biological neural networks in the human brain to process and analyze data hierarchically. Neural networks are composed of basic units called perceptrons, which are typically organized into layers. Each layer consists of several perceptrons, and multiple layers are stacked to form the entire network. The connections between these layers are defined by sets of weights or parameters that determine how data is processed as it flows from the input to the output of the network.\nBelow, we examine the primary components and structures in neural networks.\n\n3.2.1 Perceptrons\nThe Perceptron is the basic unit or node that forms the foundation for more complex structures. It functions by taking multiple inputs, each representing a feature of the object under analysis, such as the characteristics of a home for predicting its price or the attributes of a song to forecast its popularity in music streaming services. These inputs are denoted as \\(x_1, x_2, ..., x_n\\).\nEach input \\(x_i\\) has a corresponding weight \\(w_{ij}\\), and the perceptron simply multiplies each input by its matching weight. This operation is similar to linear regression, where the intermediate output, \\(z\\), is computed as the sum of the products of inputs and their weights:\n\\[\nz = \\sum (x_i \\cdot w_{ij})\n\\]\nTo this intermediate calculation, a bias term \\(b\\) is added, allowing the model to better fit the data by shifting the linear output function up or down. Thus, the intermediate linear combination computed by the perceptron including the bias becomes:\n\\[\nz = \\sum (x_i \\cdot w_{ij}) + b\n\\]\nThis basic form of a perceptron can only model linear relationships between the input and output. Patterns found in nature are often complex and extend beyond linear relationships. To enable the perceptron to handle non-linear relationships, an activation function is applied to the linear output \\(z\\).\n\\[\n\\hat{y} = \\sigma(z)\n\\]\nFigure 3.3 illustrates an example where data exhibit a nonlinear pattern that could not be adequately modeled with a linear approach. The activation function, such as sigmoid, tanh, or ReLU, transforms the linear input sum into a non-linear output. The primary objective of this function is to introduce non-linearity into the model, enabling it to learn and perform more sophisticated tasks. Thus, the final output of the perceptron, including the activation function, can be expressed as:\n\n\n\n\n\n\nFigure 3.3: Activation functions enable the modeling of complex non-linear relationships. Source: Medium - Sachin Kaushik.\n\n\n\nA perceptron can be configured to perform either regression or classification tasks. For regression, the actual numerical output \\(\\hat{y}\\) is used. For classification, the output depends on whether \\(\\hat{y}\\) crosses a certain threshold. If \\(\\hat{y}\\) exceeds this threshold, the perceptron might output one class (e.g., ‘yes’), and if it does not, another class (e.g., ‘no’).\n\n\n\n\n\n\nFigure 3.4: Perceptron. Conceived in the 1950s, perceptrons paved the way for developing more intricate neural networks and have been a fundamental building block in deep learning. Source: Wikimedia - Chrislb.\n\n\n\nFigure 3.4 illustrates the fundamental building blocks of a perceptron, which serves as the foundation for more complex neural networks. A perceptron can be thought of as a miniature decision-maker, utilizing its weights, bias, and activation function to process inputs and generate outputs based on learned parameters. This concept forms the basis for understanding more intricate neural network architectures, such as multilayer perceptrons. In these advanced structures, layers of perceptrons work in concert, with each layer’s output serving as the input for the subsequent layer. This hierarchical arrangement creates a deep learning model capable of comprehending and modeling complex, abstract patterns within data. By stacking these simple units, neural networks gain the ability to tackle increasingly sophisticated tasks, from image recognition to natural language processing.\n\n\n3.2.2 Multilayer Perceptrons\nMultilayer perceptrons (MLPs) are an evolution of the single-layer perceptron model, featuring multiple layers of nodes connected in a feedforward manner. In a feedforward network, information moves in only one direction - from the input layer, through the hidden layers, to the output layer, without any cycles or loops. This structure is illustrated in Figure 3.5. The network layers include an input layer for data reception, several hidden layers for data processing, and an output layer for final result generation.\nWhile a single perceptron is limited in its capacity to model complex patterns, the real strength of neural networks emerges from the assembly of multiple layers. Each layer consists of numerous perceptrons working together, allowing the network to capture intricate and non-linear relationships within the data. With sufficient depth and breadth, these networks can approximate virtually any function, no matter how complex.\n\n\n\n\n\n\nFigure 3.5: Multilayer Perceptron. Source: Wikimedia - Charlie.\n\n\n\n\n\n3.2.3 Training Process\nA neural network receives an input, performs a calculation, and produces a prediction. The prediction is determined by the calculations performed within the sets of perceptrons found between the input and output layers. These calculations depend primarily on the input and the weights. Since you do not have control over the input, the objective during training is to adjust the weights in such a way that the output of the network provides the most accurate prediction.\nThe training process involves several key steps, beginning with the forward pass, where the existing weights of the network are used to calculate the output for a given input. This output is then compared to the true target values to calculate an error, which measures how well the network’s prediction matches the expected outcome. Following this, a backward pass is performed. This involves using the error to make adjustments to the weights of the network through a process called backpropagation. This adjustment aims to reduce the error in subsequent predictions. The cycle of forward pass, error calculation, and backward pass is repeated iteratively. This process continues until the network’s predictions are sufficiently accurate or a predefined number of iterations is reached, effectively minimizing the loss function used to measure the error.\n\nForward Pass\nThe forward pass is the initial phase where data moves through the network from the input to the output layer. At the start of training, the network’s weights are randomly initialized, setting the initial conditions for learning. During the forward pass, each layer performs specific computations on the input data using these weights and biases, and the results are then passed to the subsequent layer. The final output of this phase is the network’s prediction. This prediction is compared to the actual target values present in the dataset to calculate the loss, which can be thought of as the difference between the predicted outputs and the target values. The loss quantifies the network’s performance at this stage, providing a crucial metric for the subsequent adjustment of weights during the backward pass.\nVideo 3.1 below explains how neural networks work using handwritten digit recognition as an example application. It also touches on the math underlying neural nets.\n\n\n\n\n\n\nVideo 3.1: Neural Networks\n\n\n\n\n\n\n\n\nBackward Pass (Backpropagation)\nAfter completing the forward pass and computing the loss, which measures how far the model’s predictions deviate from the actual target values, the next step is to improve the model’s performance by adjusting the network’s weights. Since we cannot control the inputs to the model, adjusting the weights becomes our primary method for refining the model.\nWe determine how to adjust the weights of our model through a key algorithm called backpropagation. Backpropagation uses the calculated loss to determine the gradient of each weight. These gradients describe the direction and magnitude in which the weights should be adjusted. By tuning the weights based on these gradients, the model is better positioned to make predictions that are closer to the actual target values in the next forward pass.\nGrasping these foundational concepts paves the way to understanding more intricate deep learning architectures and techniques, fostering the development of more sophisticated and productive applications, especially within embedded AI systems.\nVideo 3.2 and Video 3.3 build upon Video 3.1. They cover gradient descent and backpropagation in neural networks.\n\n\n\n\n\n\nVideo 3.2: Gradient descent\n\n\n\n\n\n\n\n\n\n\n\n\nVideo 3.3: Backpropagation\n\n\n\n\n\n\n\n\n\n3.2.4 Model Architectures\nDeep learning architectures refer to the various structured approaches that dictate how neurons and layers are organized and interact in neural networks. These architectures have evolved to tackle different problems and data types effectively. This section overviews some well-known deep learning architectures and their characteristics.\n\nMultilayer Perceptrons (MLPs)\nMLPs are basic deep learning architectures comprising three layers: an input layer, one or more hidden layers, and an output layer. These layers are fully connected, meaning each neuron in a layer is linked to every neuron in the preceding and following layers. MLPs can model intricate functions and are used in various tasks, such as regression, classification, and pattern recognition. Their capacity to learn non-linear relationships through backpropagation makes them a versatile instrument in the deep learning toolkit.\nIn embedded AI systems, MLPs can function as compact models for simpler tasks like sensor data analysis or basic pattern recognition, where computational resources are limited. Their ability to learn non-linear relationships with relatively less complexity makes them a suitable choice for embedded systems.\n\n\n\n\n\n\nExercise 3.1: Multilayer Perceptrons (MLPs)\n\n\n\n\n\nWe’ve just scratched the surface of neural networks. Now, you’ll get to try and apply these concepts in practical examples. In the provided Colab notebooks, you’ll explore:\nPredicting house prices: Learn how neural networks can analyze housing data to estimate property values.   \nImage Classification: Discover how to build a network to understand the famous MNIST handwritten digit dataset.   \nReal-world medical diagnosis: Use deep learning to tackle the important task of breast cancer classification.   \n\n\n\n\n\nConvolutional Neural Networks (CNNs)\nCNNs are mainly used in image and video recognition tasks. This architecture consists of two main parts: the convolutional base and the fully connected layers. In the convolutional base, convolutional layers filter input data to identify features like edges, corners, and textures. Following each convolutional layer, a pooling layer can be applied to reduce the spatial dimensions of the data, thereby decreasing computational load and concentrating the extracted features. Unlike MLPs, which treat input features as flat, independent entities, CNNs maintain the spatial relationships between pixels, making them particularly effective for image and video data. The extracted features from the convolutional base are then passed into the fully connected layers, similar to those used in MLPs, which perform classification based on the features extracted by the convolution layers. CNNs have proven highly effective in image recognition, object detection, and other computer vision applications.\nIn embedded AI, CNNs are crucial for image and video recognition tasks, where real-time processing is often needed. They can be optimized for embedded systems using techniques like quantization and pruning to minimize memory usage and computational demands, enabling efficient object detection and facial recognition functionalities in devices with limited computational resources.\n\n\n\n\n\n\nExercise 3.2: Convolutional Neural Networks (CNNs)\n\n\n\n\n\nWe discussed that CNNs excel at identifying image features, making them ideal for tasks like object classification. Now, you’ll get to put this knowledge into action! This Colab notebook focuses on building a CNN to classify images from the CIFAR-10 dataset, which includes objects like airplanes, cars, and animals. You’ll learn about the key differences between CIFAR-10 and the MNIST dataset we explored earlier and how these differences influence model choice. By the end of this notebook, you’ll have a grasp of CNNs for image recognition and be well on your way to becoming a TinyML expert!      \n\n\n\n\n\nRecurrent Neural Networks (RNNs)\nRNNs are suitable for sequential data analysis, like time series forecasting and natural language processing. In this architecture, connections between nodes form a directed graph along a temporal sequence, allowing information to be carried across sequences through hidden state vectors. Variants of RNNs include Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), designed to capture longer dependencies in sequence data.\nThese networks can be used in voice recognition systems, predictive maintenance, or IoT devices where sequential data patterns are common. Optimizations specific to embedded platforms can assist in managing their typically high computational and memory requirements.\n\n\nGenerative Adversarial Networks (GANs)\nGANs consist of two networks, a generator and a discriminator, trained simultaneously through adversarial training (Goodfellow et al. 2020). The generator produces data that tries to mimic the real data distribution, while the discriminator aims to distinguish between real and generated data. GANs are widely used in image generation, style transfer, and data augmentation.\n\nGoodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. “Generative Adversarial Networks.” Commun. ACM 63 (11): 139–44. https://doi.org/10.1145/3422622.\nIn embedded settings, GANs could be used for on-device data augmentation to improve the training of models directly on the embedded device, enabling continual learning and adaptation to new data without the need for cloud computing resources.\n\n\nAutoencoders\nAutoencoders are neural networks for data compression and noise reduction (Bank, Koenigstein, and Giryes 2023). They are structured to encode input data into a lower-dimensional representation and then decode it back to its original form. Variants like Variational Autoencoders (VAEs) introduce probabilistic layers that allow for generative properties, finding applications in image generation and anomaly detection.\n\nBank, Dor, Noam Koenigstein, and Raja Giryes. 2023. “Autoencoders.” Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, 353–74.\nUsing autoencoders can help in efficient data transmission and storage, improving the overall performance of embedded systems with limited computational and memory resources.\n\n\nTransformer Networks\nTransformer networks have emerged as a powerful architecture, especially in natural language processing (Vaswani et al. 2017). These networks use self-attention mechanisms to weigh the influence of different input words on each output word, enabling parallel computation and capturing intricate patterns in data. Transformer networks have led to state-of-the-art results in tasks like language translation, summarization, and text generation.\n\nVaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” Adv Neural Inf Process Syst 30.\nThese networks can be optimized to perform language-related tasks directly on the device. For example, transformers can be used in embedded systems for real-time translation services or voice-assisted interfaces, where latency and computational efficiency are crucial. Techniques such as model distillation can be employed to deploy these networks on embedded devices with limited resources.\nThese architectures serve specific purposes and excel in different domains, offering a rich toolkit for addressing diverse problems in embedded AI systems. Understanding the nuances of these architectures is crucial in designing effective and efficient deep learning models for various applications.\n\n\n\n3.2.5 Traditional ML vs Deep Learning\nDeep learning extends traditional machine learning by utilizing neural networks to discern patterns in data. In contrast, traditional machine learning relies on a set of established algorithms such as decision trees, k-nearest neighbors, and support vector machines, but does not involve neural networks. To briefly highlight the differences, Table 3.1 illustrates the contrasting characteristics between traditional ML and deep learning:\n\n\n\nTable 3.1: Comparison of traditional machine learning and deep learning.\n\n\n\n\n\n\n\n\n\n\nAspect\nTraditional ML\nDeep Learning\n\n\n\n\nData Requirements\nLow to Moderate (efficient with smaller datasets)\nHigh (requires large datasets for nuanced learning)\n\n\nModel Complexity\nModerate (suitable for well-defined problems)\nHigh (detects intricate patterns, suited for complex tasks)\n\n\nComputational Resources\nLow to Moderate (cost-effective, less resource-intensive)\nHigh (demands substantial computational power and resources)\n\n\nDeployment Speed\nFast (quicker training and deployment cycles)\nSlow (prolonged training times, esp. with larger datasets)\n\n\nInterpretability\nHigh (clear insights into decision pathways)\nLow (complex layered structures, “black box” nature)\n\n\nMaintenance\nEasier (simple to update and maintain)\nComplex (requires more efforts in maintenance and updates)\n\n\n\n\n\n\n\n\n3.2.6 Choosing Traditional ML vs. DL\n\nData Availability and Volume\nAmount of Data: Traditional machine learning algorithms, such as decision trees or Naive Bayes, are often more suitable when data availability is limited. They offer robust predictions even with smaller datasets. This is particularly true in medical diagnostics for disease prediction and customer segmentation in marketing.\nData Diversity and Quality: Traditional machine learning algorithms often work well with structured data (the input to the model is a set of features, ideally independent of each other) but may require significant preprocessing effort (i.e., feature engineering). On the other hand, deep learning takes the approach of automatically performing feature engineering as part of the model architecture. This approach enables the construction of end-to-end models capable of directly mapping from unstructured input data (such as text, audio, and images) to the desired output without relying on simplistic heuristics that have limited effectiveness. However, this results in larger models demanding more data and computational resources. In noisy data, the necessity for larger datasets is further emphasized when utilizing Deep Learning.\n\n\nComplexity of the Problem\nProblem Granularity: Problems that are simple to moderately complex, which may involve linear or polynomial relationships between variables, often find a better fit with traditional machine learning methods.\nHierarchical Feature Representation: Deep learning models are excellent in tasks that require hierarchical feature representation, such as image and speech recognition. However, not all problems require this complexity, and traditional machine learning algorithms may sometimes offer simpler and equally effective solutions.\n\n\nHardware and Computational Resources\nResource Constraints: The availability of computational resources often influences the choice between traditional ML and deep learning. The former is generally less resource-intensive and thus preferable in environments with hardware limitations or budget constraints.\nScalability and Speed: Traditional machine learning algorithms, like support vector machines (SVM), often allow for faster training times and easier scalability, which is particularly beneficial in projects with tight timelines and growing data volumes.\n\n\nRegulatory Compliance\nRegulatory compliance is crucial in various industries, requiring adherence to guidelines and best practices such as the General Data Protection Regulation (GDPR) in the EU. Traditional ML models, due to their inherent interpretability, often align better with these regulations, especially in sectors like finance and healthcare.\n\n\nInterpretability\nUnderstanding the decision-making process is easier with traditional machine learning techniques than deep learning models, which function as “black boxes,” making it challenging to trace decision pathways.\n\n\n\n3.2.7 Making an Informed Choice\nGiven the constraints of embedded AI systems, understanding the differences between traditional ML techniques and deep learning becomes essential. Both avenues offer unique advantages, and their distinct characteristics often dictate the choice of one over the other in different scenarios.\nDespite this, deep learning has steadily outperformed traditional machine learning methods in several key areas due to abundant data, computational advancements, and proven effectiveness in complex tasks. Here are some specific reasons why we focus on deep learning:\n1. Superior Performance in Complex Tasks: Deep learning models, particularly deep neural networks, excel in tasks where the relationships between data points are incredibly intricate. Tasks like image and speech recognition, language translation, and playing complex games like Go and Chess have seen significant advancements primarily through deep learning algorithms.\n2. Efficient Handling of Unstructured Data: Unlike traditional machine learning methods, deep learning can more effectively process unstructured data. This is crucial in today’s data landscape, where the vast majority of data, such as text, images, and videos, is unstructured.\n3. Leveraging Big Data: With the availability of big data, deep learning models can learn and improve continually. These models excel at utilizing large datasets to improve their predictive accuracy, a limitation in traditional machine-learning approaches.\n4. Hardware Advancements and Parallel Computing: The advent of powerful GPUs and the availability of cloud computing platforms have enabled the rapid training of deep learning models. These advancements have addressed one of deep learning’s significant challenges: the need for substantial computational resources.\n5. Dynamic Adaptability and Continuous Learning: Deep learning models can dynamically adapt to new information or data. They can be trained to generalize their learning to new, unseen data, crucial in rapidly evolving fields like autonomous driving or real-time language translation.\nWhile deep learning has gained significant traction, it’s essential to understand that traditional machine learning is still relevant. As we dive deeper into the intricacies of deep learning, we will also highlight situations where traditional machine learning methods may be more appropriate due to their simplicity, efficiency, and interpretability. By focusing on deep learning in this text, we aim to equip readers with the knowledge and tools to tackle modern, complex problems across various domains while also providing insights into the comparative advantages and appropriate application scenarios for deep learning and traditional machine learning techniques.", + "text": "3.2 Neural Networks\nDeep learning draws inspiration from the human brain’s neural networks to create decision-making patterns. This section digs into the foundational concepts of deep learning, providing insights into the more complex topics discussed later in this primer.\nNeural networks serve as the foundation of deep learning, inspired by the biological neural networks in the human brain to process and analyze data hierarchically. Neural networks are composed of basic units called perceptrons, which are typically organized into layers. Each layer consists of several perceptrons, and multiple layers are stacked to form the entire network. The connections between these layers are defined by sets of weights or parameters that determine how data is processed as it flows from the input to the output of the network.\nBelow, we examine the primary components and structures in neural networks.\n\n3.2.1 Perceptrons\nThe Perceptron is the basic unit or node that forms the foundation for more complex structures. It functions by taking multiple inputs, each representing a feature of the object under analysis, such as the characteristics of a home for predicting its price or the attributes of a song to forecast its popularity in music streaming services. These inputs are denoted as \\(x_1, x_2, ..., x_n\\).\nEach input \\(x_i\\) has a corresponding weight \\(w_{ij}\\), and the perceptron simply multiplies each input by its matching weight. This operation is similar to linear regression, where the intermediate output, \\(z\\), is computed as the sum of the products of inputs and their weights:\n\\[\nz = \\sum (x_i \\cdot w_{ij})\n\\]\nTo this intermediate calculation, a bias term \\(b\\) is added, allowing the model to better fit the data by shifting the linear output function up or down. Thus, the intermediate linear combination computed by the perceptron including the bias becomes:\n\\[\nz = \\sum (x_i \\cdot w_{ij}) + b\n\\]\nThis basic form of a perceptron can only model linear relationships between the input and output. Patterns found in nature are often complex and extend beyond linear relationships. To enable the perceptron to handle non-linear relationships, an activation function is applied to the linear output \\(z\\).\n\\[\n\\hat{y} = \\sigma(z)\n\\]\nFigure 3.3 illustrates an example where data exhibit a nonlinear pattern that could not be adequately modeled with a linear approach. The activation function, such as sigmoid, tanh, or ReLU, transforms the linear input sum into a non-linear output. The primary objective of this function is to introduce non-linearity into the model, enabling it to learn and perform more sophisticated tasks. Thus, the final output of the perceptron, including the activation function, can be expressed as:\n\n\n\n\n\n\nFigure 3.3: Activation functions enable the modeling of complex non-linear relationships. Source: Medium - Sachin Kaushik.\n\n\n\nA perceptron can be configured to perform either regression or classification tasks. For regression, the actual numerical output \\(\\hat{y}\\) is used. For classification, the output depends on whether \\(\\hat{y}\\) crosses a certain threshold. If \\(\\hat{y}\\) exceeds this threshold, the perceptron might output one class (e.g., ‘yes’), and if it does not, another class (e.g., ‘no’).\n\n\n\n\n\n\nFigure 3.4: Perceptron. Conceived in the 1950s, perceptrons paved the way for developing more intricate neural networks and have been a fundamental building block in deep learning. Source: Wikimedia - Chrislb.\n\n\n\nFigure 3.4 illustrates the fundamental building blocks of a perceptron, which serves as the foundation for more complex neural networks. A perceptron can be thought of as a miniature decision-maker, utilizing its weights, bias, and activation function to process inputs and generate outputs based on learned parameters. This concept forms the basis for understanding more intricate neural network architectures, such as multilayer perceptrons. In these advanced structures, layers of perceptrons work in concert, with each layer’s output serving as the input for the subsequent layer. This hierarchical arrangement creates a deep learning model capable of comprehending and modeling complex, abstract patterns within data. By stacking these simple units, neural networks gain the ability to tackle increasingly sophisticated tasks, from image recognition to natural language processing.\n\n\n3.2.2 Multilayer Perceptrons\nMultilayer perceptrons (MLPs) are an evolution of the single-layer perceptron model, featuring multiple layers of nodes connected in a feedforward manner. In a feedforward network, information moves in only one direction - from the input layer, through the hidden layers, to the output layer, without any cycles or loops. This structure is illustrated in Figure 3.5. The network layers include an input layer for data reception, several hidden layers for data processing, and an output layer for final result generation.\nWhile a single perceptron is limited in its capacity to model complex patterns, the real strength of neural networks emerges from the assembly of multiple layers. Each layer consists of numerous perceptrons working together, allowing the network to capture intricate and non-linear relationships within the data. With sufficient depth and breadth, these networks can approximate virtually any function, no matter how complex.\n\n\n\n\n\n\nFigure 3.5: Multilayer Perceptron. Source: Wikimedia - Charlie.\n\n\n\n\n\n3.2.3 Training Process\nA neural network receives an input, performs a calculation, and produces a prediction. The prediction is determined by the calculations performed within the sets of perceptrons found between the input and output layers. These calculations depend primarily on the input and the weights. Since you do not have control over the input, the objective during training is to adjust the weights in such a way that the output of the network provides the most accurate prediction.\nThe training process involves several key steps, beginning with the forward pass, where the existing weights of the network are used to calculate the output for a given input. This output is then compared to the true target values to calculate an error, which measures how well the network’s prediction matches the expected outcome. Following this, a backward pass is performed. This involves using the error to make adjustments to the weights of the network through a process called backpropagation. This adjustment reduces the error in subsequent predictions. The cycle of forward pass, error calculation, and backward pass is repeated iteratively. This process continues until the network’s predictions are sufficiently accurate or a predefined number of iterations is reached, effectively minimizing the loss function used to measure the error.\n\nForward Pass\nThe forward pass is the initial phase where data moves through the network from the input to the output layer. At the start of training, the network’s weights are randomly initialized, setting the initial conditions for learning. During the forward pass, each layer performs specific computations on the input data using these weights and biases, and the results are then passed to the subsequent layer. The final output of this phase is the network’s prediction. This prediction is compared to the actual target values present in the dataset to calculate the loss, which can be thought of as the difference between the predicted outputs and the target values. The loss quantifies the network’s performance at this stage, providing a crucial metric for the subsequent adjustment of weights during the backward pass.\nVideo 3.1 below explains how neural networks work using handwritten digit recognition as an example application. It also touches on the math underlying neural nets.\n\n\n\n\n\n\nVideo 3.1: Neural Networks\n\n\n\n\n\n\n\n\nBackward Pass (Backpropagation)\nAfter completing the forward pass and computing the loss, which measures how far the model’s predictions deviate from the actual target values, the next step is to improve the model’s performance by adjusting the network’s weights. Since we cannot control the inputs to the model, adjusting the weights becomes our primary method for refining the model.\nWe determine how to adjust the weights of our model through a key algorithm called backpropagation. Backpropagation uses the calculated loss to determine the gradient of each weight. These gradients describe the direction and magnitude in which the weights should be adjusted. By tuning the weights based on these gradients, the model is better positioned to make predictions that are closer to the actual target values in the next forward pass.\nGrasping these foundational concepts paves the way to understanding more intricate deep learning architectures and techniques, fostering the development of more sophisticated and productive applications, especially within embedded AI systems.\nVideo 3.2 and Video 3.3 build upon Video 3.1. They cover gradient descent and backpropagation in neural networks.\n\n\n\n\n\n\nVideo 3.2: Gradient descent\n\n\n\n\n\n\n\n\n\n\n\n\nVideo 3.3: Backpropagation\n\n\n\n\n\n\n\n\n\n3.2.4 Model Architectures\nDeep learning architectures refer to the various structured approaches that dictate how neurons and layers are organized and interact in neural networks. These architectures have evolved to tackle different problems and data types effectively. This section overviews some well-known deep learning architectures and their characteristics.\n\nMultilayer Perceptrons (MLPs)\nMLPs are basic deep learning architectures comprising three layers: an input layer, one or more hidden layers, and an output layer. These layers are fully connected, meaning each neuron in a layer is linked to every neuron in the preceding and following layers. MLPs can model intricate functions and are used in various tasks, such as regression, classification, and pattern recognition. Their capacity to learn non-linear relationships through backpropagation makes them a versatile instrument in the deep learning toolkit.\nIn embedded AI systems, MLPs can function as compact models for simpler tasks like sensor data analysis or basic pattern recognition, where computational resources are limited. Their ability to learn non-linear relationships with relatively less complexity makes them a suitable choice for embedded systems.\n\n\n\n\n\n\nExercise 3.1: Multilayer Perceptrons (MLPs)\n\n\n\n\n\nWe’ve just scratched the surface of neural networks. Now, you’ll get to try and apply these concepts in practical examples. In the provided Colab notebooks, you’ll explore:\nPredicting house prices: Learn how neural networks can analyze housing data to estimate property values.   \nImage Classification: Discover how to build a network to understand the famous MNIST handwritten digit dataset.   \nReal-world medical diagnosis: Use deep learning to tackle the important task of breast cancer classification.   \n\n\n\n\n\nConvolutional Neural Networks (CNNs)\nCNNs are mainly used in image and video recognition tasks. This architecture consists of two main parts: the convolutional base and the fully connected layers. In the convolutional base, convolutional layers filter input data to identify features like edges, corners, and textures. Following each convolutional layer, a pooling layer can be applied to reduce the spatial dimensions of the data, thereby decreasing computational load and concentrating the extracted features. Unlike MLPs, which treat input features as flat, independent entities, CNNs maintain the spatial relationships between pixels, making them particularly effective for image and video data. The extracted features from the convolutional base are then passed into the fully connected layers, similar to those used in MLPs, which perform classification based on the features extracted by the convolution layers. CNNs have proven highly effective in image recognition, object detection, and other computer vision applications.\nIn embedded AI, CNNs are crucial for image and video recognition tasks, where real-time processing is often needed. They can be optimized for embedded systems using techniques like quantization and pruning to minimize memory usage and computational demands, enabling efficient object detection and facial recognition functionalities in devices with limited computational resources.\n\n\n\n\n\n\nExercise 3.2: Convolutional Neural Networks (CNNs)\n\n\n\n\n\nWe discussed that CNNs excel at identifying image features, making them ideal for tasks like object classification. Now, you’ll get to put this knowledge into action! This Colab notebook focuses on building a CNN to classify images from the CIFAR-10 dataset, which includes objects like airplanes, cars, and animals. You’ll learn about the key differences between CIFAR-10 and the MNIST dataset we explored earlier and how these differences influence model choice. By the end of this notebook, you’ll have a grasp of CNNs for image recognition and be well on your way to becoming a TinyML expert!      \n\n\n\n\n\nRecurrent Neural Networks (RNNs)\nRNNs are suitable for sequential data analysis, like time series forecasting and natural language processing. In this architecture, connections between nodes form a directed graph along a temporal sequence, allowing information to be carried across sequences through hidden state vectors. Variants of RNNs include Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), designed to capture longer dependencies in sequence data.\nThese networks can be used in voice recognition systems, predictive maintenance, or IoT devices where sequential data patterns are common. Optimizations specific to embedded platforms can assist in managing their typically high computational and memory requirements.\n\n\nGenerative Adversarial Networks (GANs)\nGANs consist of two networks, a generator and a discriminator, trained simultaneously through adversarial training (Goodfellow et al. 2020). The generator produces data that tries to mimic the real data distribution, while the discriminator distinguishes between real and generated data. GANs are widely used in image generation, style transfer, and data augmentation.\n\nGoodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. “Generative Adversarial Networks.” Commun. ACM 63 (11): 139–44. https://doi.org/10.1145/3422622.\nIn embedded settings, GANs could be used for on-device data augmentation to improve the training of models directly on the embedded device, enabling continual learning and adaptation to new data without the need for cloud computing resources.\n\n\nAutoencoders\nAutoencoders are neural networks for data compression and noise reduction (Bank, Koenigstein, and Giryes 2023). They are structured to encode input data into a lower-dimensional representation and then decode it back to its original form. Variants like Variational Autoencoders (VAEs) introduce probabilistic layers that allow for generative properties, finding applications in image generation and anomaly detection.\n\nBank, Dor, Noam Koenigstein, and Raja Giryes. 2023. “Autoencoders.” Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, 353–74.\nUsing autoencoders can help in efficient data transmission and storage, improving the overall performance of embedded systems with limited computational and memory resources.\n\n\nTransformer Networks\nTransformer networks have emerged as a powerful architecture, especially in natural language processing (Vaswani et al. 2017). These networks use self-attention mechanisms to weigh the influence of different input words on each output word, enabling parallel computation and capturing intricate patterns in data. Transformer networks have led to state-of-the-art results in tasks like language translation, summarization, and text generation.\n\nVaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” Adv Neural Inf Process Syst 30.\nThese networks can be optimized to perform language-related tasks directly on the device. For example, transformers can be used in embedded systems for real-time translation services or voice-assisted interfaces, where latency and computational efficiency are crucial. Techniques such as model distillation can be employed to deploy these networks on embedded devices with limited resources.\nThese architectures serve specific purposes and excel in different domains, offering a rich toolkit for addressing diverse problems in embedded AI systems. Understanding the nuances of these architectures is crucial in designing effective and efficient deep learning models for various applications.\n\n\n\n3.2.5 Traditional ML vs Deep Learning\nDeep learning extends traditional machine learning by utilizing neural networks to discern patterns in data. In contrast, traditional machine learning relies on a set of established algorithms such as decision trees, k-nearest neighbors, and support vector machines, but does not involve neural networks. To briefly highlight the differences, Table 3.1 illustrates the contrasting characteristics between traditional ML and deep learning:\n\n\n\nTable 3.1: Comparison of traditional machine learning and deep learning.\n\n\n\n\n\n\n\n\n\n\nAspect\nTraditional ML\nDeep Learning\n\n\n\n\nData Requirements\nLow to Moderate (efficient with smaller datasets)\nHigh (requires large datasets for nuanced learning)\n\n\nModel Complexity\nModerate (suitable for well-defined problems)\nHigh (detects intricate patterns, suited for complex tasks)\n\n\nComputational Resources\nLow to Moderate (cost-effective, less resource-intensive)\nHigh (demands substantial computational power and resources)\n\n\nDeployment Speed\nFast (quicker training and deployment cycles)\nSlow (prolonged training times, esp. with larger datasets)\n\n\nInterpretability\nHigh (clear insights into decision pathways)\nLow (complex layered structures, “black box” nature)\n\n\nMaintenance\nEasier (simple to update and maintain)\nComplex (requires more efforts in maintenance and updates)\n\n\n\n\n\n\n\n\n3.2.6 Choosing Traditional ML vs. DL\n\nData Availability and Volume\nAmount of Data: Traditional machine learning algorithms, such as decision trees or Naive Bayes, are often more suitable when data availability is limited. They offer robust predictions even with smaller datasets. This is particularly true in medical diagnostics for disease prediction and customer segmentation in marketing.\nData Diversity and Quality: Traditional machine learning algorithms often work well with structured data (the input to the model is a set of features, ideally independent of each other) but may require significant preprocessing effort (i.e., feature engineering). On the other hand, deep learning takes the approach of automatically performing feature engineering as part of the model architecture. This approach enables the construction of end-to-end models capable of directly mapping from unstructured input data (such as text, audio, and images) to the desired output without relying on simplistic heuristics that have limited effectiveness. However, this results in larger models demanding more data and computational resources. In noisy data, the necessity for larger datasets is further emphasized when utilizing Deep Learning.\n\n\nComplexity of the Problem\nProblem Granularity: Problems that are simple to moderately complex, which may involve linear or polynomial relationships between variables, often find a better fit with traditional machine learning methods.\nHierarchical Feature Representation: Deep learning models are excellent in tasks that require hierarchical feature representation, such as image and speech recognition. However, not all problems require this complexity, and traditional machine learning algorithms may sometimes offer simpler and equally effective solutions.\n\n\nHardware and Computational Resources\nResource Constraints: The availability of computational resources often influences the choice between traditional ML and deep learning. The former is generally less resource-intensive and thus preferable in environments with hardware limitations or budget constraints.\nScalability and Speed: Traditional machine learning algorithms, like support vector machines (SVM), often allow for faster training times and easier scalability, which is particularly beneficial in projects with tight timelines and growing data volumes.\n\n\nRegulatory Compliance\nRegulatory compliance is crucial in various industries, requiring adherence to guidelines and best practices such as the General Data Protection Regulation (GDPR) in the EU. Traditional ML models, due to their inherent interpretability, often align better with these regulations, especially in sectors like finance and healthcare.\n\n\nInterpretability\nUnderstanding the decision-making process is easier with traditional machine learning techniques than deep learning models, which function as “black boxes,” making it challenging to trace decision pathways.\n\n\n\n3.2.7 Making an Informed Choice\nGiven the constraints of embedded AI systems, understanding the differences between traditional ML techniques and deep learning becomes essential. Both avenues offer unique advantages, and their distinct characteristics often dictate the choice of one over the other in different scenarios.\nDespite this, deep learning has steadily outperformed traditional machine learning methods in several key areas due to abundant data, computational advancements, and proven effectiveness in complex tasks. Here are some specific reasons why we focus on deep learning:\n1. Superior Performance in Complex Tasks: Deep learning models, particularly deep neural networks, excel in tasks where the relationships between data points are incredibly intricate. Tasks like image and speech recognition, language translation, and playing complex games like Go and Chess have seen significant advancements primarily through deep learning algorithms.\n2. Efficient Handling of Unstructured Data: Unlike traditional machine learning methods, deep learning can more effectively process unstructured data. This is crucial in today’s data landscape, where the vast majority of data, such as text, images, and videos, is unstructured.\n3. Leveraging Big Data: With the availability of big data, deep learning models can learn and improve continually. These models excel at utilizing large datasets to improve their predictive accuracy, a limitation in traditional machine-learning approaches.\n4. Hardware Advancements and Parallel Computing: The advent of powerful GPUs and the availability of cloud computing platforms have enabled the rapid training of deep learning models. These advancements have addressed one of deep learning’s significant challenges: the need for substantial computational resources.\n5. Dynamic Adaptability and Continuous Learning: Deep learning models can dynamically adapt to new information or data. They can be trained to generalize their learning to new, unseen data, crucial in rapidly evolving fields like autonomous driving or real-time language translation.\nWhile deep learning has gained significant traction, it’s essential to understand that traditional machine learning is still relevant. As we dive deeper into the intricacies of deep learning, we will also highlight situations where traditional machine learning methods may be more appropriate due to their simplicity, efficiency, and interpretability. By focusing on deep learning in this text, we aim to equip readers with the knowledge and tools to tackle modern, complex problems across various domains while also providing insights into the comparative advantages and appropriate application scenarios for deep learning and traditional machine learning techniques.", "crumbs": [ "Fundamentals", "3  DL Primer" @@ -422,7 +422,7 @@ "href": "contents/data_engineering/data_engineering.html#problem-definition", "title": "5  Data Engineering", "section": "5.2 Problem Definition", - "text": "5.2 Problem Definition\nIn many machine learning domains, sophisticated algorithms take center stage, while the fundamental importance of data quality is often overlooked. This neglect gives rise to “Data Cascades” by Sambasivan et al. (2021) (see Figure 5.1)—events where lapses in data quality compound, leading to negative downstream consequences such as flawed predictions, project terminations, and even potential harm to communities. In Figure 5.1, we have an illustration of potential data pitfalls at every stage and how they influence the entire process down the line. The influence of data collection errors is especially pronounced. Any lapses in this stage will become apparent at later stages (in model evaluation and deployment) and might lead to costly consequences, such as abandoning the entire model and restarting anew. Therefore, investing in data engineering techniques from the onset will help us detect errors early.\n\n\n\n\n\n\nFigure 5.1: Data cascades: compounded costs. Source: Sambasivan et al. (2021).\n\n\nSambasivan, Nithya, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M Aroyo. 2021. ““Everyone Wants to Do the Model Work, Not the Data Work”: Data Cascades in High-Stakes AI.” In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–15.\n\n\nDespite many ML professionals recognizing the importance of data, numerous practitioners report facing these cascades. This highlights a systemic issue: while the allure of developing advanced models remains, data often needs to be more appreciated.\nTake, for example, Keyword Spotting (KWS) (see Figure 5.2). KWS is a prime example of TinyML in action and is a critical technology behind voice-enabled interfaces on endpoint devices such as smartphones. Typically functioning as lightweight wake-word engines, these systems are consistently active, listening for a specific phrase to trigger further actions. When we say “OK, Google” or “Alexa,” this initiates a process on a microcontroller embedded within the device. Despite their limited resources, these microcontrollers play an important role in enabling seamless voice interactions with devices, often operating in environments with high ambient noise. The uniqueness of the wake word helps minimize false positives, ensuring that the system is not triggered inadvertently.\nIt is important to appreciate that these keyword-spotting technologies are not isolated; they integrate seamlessly into larger systems, processing signals continuously while managing low power consumption. These systems extend beyond simple keyword recognition, evolving to facilitate diverse sound detections, such as glass breaking. This evolution is geared towards creating intelligent devices capable of understanding and responding to vocal commands, heralding a future where even household appliances can be controlled through voice interactions.\n\n\n\n\n\n\nFigure 5.2: Keyword Spotting example: interacting with Alexa. Source: Amazon.\n\n\n\nBuilding a reliable KWS model is a complex task. It demands a deep understanding of the deployment scenario, encompassing where and how these devices will operate. For instance, a KWS model’s effectiveness is not just about recognizing a word; it’s about discerning it among various accents and background noises, whether in a bustling cafe or amid the blaring sound of a television in a living room or a kitchen where these devices are commonly found. It’s about ensuring that a whispered “Alexa” in the dead of night or a shouted “OK Google” in a noisy marketplace are recognized with equal precision.\nMoreover, many current KWS voice assistants support a limited number of languages, leaving a substantial portion of the world’s linguistic diversity unrepresented. This limitation is partly due to the difficulty in gathering and monetizing data for languages spoken by smaller populations. The long-tail distribution of languages implies that many languages have limited data, making the development of supportive technologies challenging.\nThis level of accuracy and robustness hinges on the availability and quality of data, the ability to label the data correctly, and the transparency of the data for the end user before it is used to train the model. However, it all begins with clearly understanding the problem statement or definition.\nGenerally, in ML, problem definition has a few key steps:\n\nIdentifying the problem definition clearly\nSetting clear objectives\nEstablishing success benchmark\nUnderstanding end-user engagement/use\nUnderstanding the constraints and limitations of deployment\nFollowed by finally doing the data collection.\n\nA solid project foundation is essential for its trajectory and eventual success. Central to this foundation is first identifying a clear problem, such as ensuring that voice commands in voice assistance systems are recognized consistently across varying environments. Clear objectives, like creating representative datasets for diverse scenarios, provide a unified direction. Benchmarks, such as system accuracy in keyword detection, offer measurable outcomes to gauge progress. Engaging with stakeholders, from end-users to investors, provides invaluable insights and ensures alignment with market needs. Additionally, understanding platform constraints is important when exploring areas like voice assistance. Embedded systems, such as microcontrollers, come with inherent processing power, memory, and energy efficiency limitations. Recognizing these limitations ensures that functionalities, like keyword detection, are tailored to operate optimally, balancing performance with resource conservation.\nIn this context, using KWS as an example, we can break each of the steps out as follows:\n\nIdentifying the Problem: At its core, KWS aims to detect specific keywords amidst ambient sounds and other spoken words. The primary problem is to design a system that can recognize these keywords with high accuracy, low latency, and minimal false positives or negatives, especially when deployed on devices with limited computational resources.\nSetting Clear Objectives: The objectives for a KWS system might include:\n\nAchieving a specific accuracy rate (e.g., 98% accuracy in keyword detection).\nEnsuring low latency (e.g., keyword detection and response within 200 milliseconds).\nMinimizing power consumption to extend battery life on embedded devices.\nEnsuring the model’s size is optimized for the available memory on the device.\n\nBenchmarks for Success: Establish clear metrics to measure the success of the KWS system. This could include:\n\nTrue Positive Rate: The percentage of correctly identified keywords.\nFalse Positive Rate: The percentage of non-keywords incorrectly identified as keywords.\nResponse Time: The time taken from keyword utterance to system response.\nPower Consumption: Average power used during keyword detection.\n\nStakeholder Engagement and Understanding: Engage with stakeholders, which include device manufacturers, hardware and software developers, and end-users. Understand their needs, capabilities, and constraints. For instance:\n\nDevice manufacturers might prioritize low power consumption.\nSoftware developers might emphasize ease of integration.\nEnd-users would prioritize accuracy and responsiveness.\n\nUnderstanding the Constraints and Limitations of Embedded Systems: Embedded devices come with their own set of challenges:\n\nMemory Limitations: KWS models must be lightweight to fit within the memory constraints of embedded devices. Typically, KWS models need to be as small as 16KB to fit in the always-on island of the SoC. Moreover, this is just the model size. Additional application code for preprocessing may also need to fit within the memory constraints.\nProcessing Power: The computational capabilities of embedded devices are limited (a few hundred MHz of clock speed), so the KWS model must be optimized for efficiency.\nPower Consumption: Since many embedded devices are battery-powered, the KWS system must be power-efficient.\nEnvironmental Challenges: Devices might be deployed in various environments, from quiet bedrooms to noisy industrial settings. The KWS system must be robust enough to function effectively across these scenarios.\n\nData Collection and Analysis: For a KWS system, the quality and diversity of data are paramount. Considerations might include:\n\nVariety of Accents: Collect data from speakers with various accents to ensure wide-ranging recognition.\nBackground Noises: Include data samples with different ambient noises to train the model for real-world scenarios.\nKeyword Variations: People might either pronounce keywords differently or have slight variations in the wake word itself. Ensure the dataset captures these nuances.\n\nIterative Feedback and Refinement: Once a prototype KWS system is developed, it’s crucial to test it in real-world scenarios, gather feedback, and iteratively refine the model. This ensures that the system remains aligned with the defined problem and objectives. This is important because the deployment scenarios change over time as things evolve.\n\n\n\n\n\n\n\nExercise 5.1: Keyword Spotting with TensorFlow Lite Micro\n\n\n\n\n\nExplore a hands-on guide for building and deploying Keyword Spotting (KWS) systems using TensorFlow Lite Micro. Follow steps from data collection to model training and deployment to microcontrollers. Learn to create efficient KWS models that recognize specific keywords amidst background noise. Perfect for those interested in machine learning on embedded systems. Unlock the potential of voice-enabled devices with TensorFlow Lite Micro!\n\n\n\n\nThe current chapter underscores the essential role of data quality in ML, using Keyword Spotting (KWS) systems as an example. It outlines key steps, from problem definition to stakeholder engagement, emphasizing iterative feedback. The forthcoming chapter will dig deeper into data quality management, discussing its consequences and future trends, focusing on the importance of high-quality, diverse data in AI system development, addressing ethical considerations and data sourcing methods.", + "text": "5.2 Problem Definition\nIn many machine learning domains, sophisticated algorithms take center stage, while the fundamental importance of data quality is often overlooked. This neglect gives rise to “Data Cascades” by Sambasivan et al. (2021) (see Figure 5.1)—events where lapses in data quality compound, leading to negative downstream consequences such as flawed predictions, project terminations, and even potential harm to communities. In Figure 5.1, we have an illustration of potential data pitfalls at every stage and how they influence the entire process down the line. The influence of data collection errors is especially pronounced. Any lapses in this stage will become apparent at later stages (in model evaluation and deployment) and might lead to costly consequences, such as abandoning the entire model and restarting anew. Therefore, investing in data engineering techniques from the onset will help us detect errors early.\n\n\n\n\n\n\nFigure 5.1: Data cascades: compounded costs. Source: Sambasivan et al. (2021).\n\n\nSambasivan, Nithya, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M Aroyo. 2021. ““Everyone Wants to Do the Model Work, Not the Data Work”: Data Cascades in High-Stakes AI.” In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–15.\n\n\nDespite many ML professionals recognizing the importance of data, numerous practitioners report facing these cascades. This highlights a systemic issue: while the allure of developing advanced models remains, data often needs to be more appreciated.\nTake, for example, Keyword Spotting (KWS) (see Figure 5.2). KWS is a prime example of TinyML in action and is a critical technology behind voice-enabled interfaces on endpoint devices such as smartphones. Typically functioning as lightweight wake-word engines, these systems are consistently active, listening for a specific phrase to trigger further actions. When we say “OK, Google” or “Alexa,” this initiates a process on a microcontroller embedded within the device. Despite their limited resources, these microcontrollers play an important role in enabling seamless voice interactions with devices, often operating in environments with high ambient noise. The uniqueness of the wake word helps minimize false positives, ensuring that the system is not triggered inadvertently.\nIt is important to appreciate that these keyword-spotting technologies are not isolated; they integrate seamlessly into larger systems, processing signals continuously while managing low power consumption. These systems extend beyond simple keyword recognition, evolving to facilitate diverse sound detections, such as glass breaking. This evolution is geared towards creating intelligent devices capable of understanding and responding to vocal commands, heralding a future where even household appliances can be controlled through voice interactions.\n\n\n\n\n\n\nFigure 5.2: Keyword Spotting example: interacting with Alexa. Source: Amazon.\n\n\n\nBuilding a reliable KWS model is a complex task. It demands a deep understanding of the deployment scenario, encompassing where and how these devices will operate. For instance, a KWS model’s effectiveness is not just about recognizing a word; it’s about discerning it among various accents and background noises, whether in a bustling cafe or amid the blaring sound of a television in a living room or a kitchen where these devices are commonly found. It’s about ensuring that a whispered “Alexa” in the dead of night or a shouted “OK Google” in a noisy marketplace are recognized with equal precision.\nMoreover, many current KWS voice assistants support a limited number of languages, leaving a substantial portion of the world’s linguistic diversity unrepresented. This limitation is partly due to the difficulty in gathering and monetizing data for languages spoken by smaller populations. The long-tail distribution of languages implies that many languages have limited data, making the development of supportive technologies challenging.\nThis level of accuracy and robustness hinges on the availability and quality of data, the ability to label the data correctly, and the transparency of the data for the end user before it is used to train the model. However, it all begins with clearly understanding the problem statement or definition.\nGenerally, in ML, problem definition has a few key steps:\n\nIdentifying the problem definition clearly\nSetting clear objectives\nEstablishing success benchmark\nUnderstanding end-user engagement/use\nUnderstanding the constraints and limitations of deployment\nFollowed by finally doing the data collection.\n\nA solid project foundation is essential for its trajectory and eventual success. Central to this foundation is first identifying a clear problem, such as ensuring that voice commands in voice assistance systems are recognized consistently across varying environments. Clear objectives, like creating representative datasets for diverse scenarios, provide a unified direction. Benchmarks, such as system accuracy in keyword detection, offer measurable outcomes to gauge progress. Engaging with stakeholders, from end-users to investors, provides invaluable insights and ensures alignment with market needs. Additionally, understanding platform constraints is important when exploring areas like voice assistance. Embedded systems, such as microcontrollers, come with inherent processing power, memory, and energy efficiency limitations. Recognizing these limitations ensures that functionalities, like keyword detection, are tailored to operate optimally, balancing performance with resource conservation.\nIn this context, using KWS as an example, we can break each of the steps out as follows:\n\nIdentifying the Problem: At its core, KWS detects specific keywords amidst ambient sounds and other spoken words. The primary problem is to design a system that can recognize these keywords with high accuracy, low latency, and minimal false positives or negatives, especially when deployed on devices with limited computational resources.\nSetting Clear Objectives: The objectives for a KWS system might include:\n\nAchieving a specific accuracy rate (e.g., 98% accuracy in keyword detection).\nEnsuring low latency (e.g., keyword detection and response within 200 milliseconds).\nMinimizing power consumption to extend battery life on embedded devices.\nEnsuring the model’s size is optimized for the available memory on the device.\n\nBenchmarks for Success: Establish clear metrics to measure the success of the KWS system. This could include:\n\nTrue Positive Rate: The percentage of correctly identified keywords.\nFalse Positive Rate: The percentage of non-keywords incorrectly identified as keywords.\nResponse Time: The time taken from keyword utterance to system response.\nPower Consumption: Average power used during keyword detection.\n\nStakeholder Engagement and Understanding: Engage with stakeholders, which include device manufacturers, hardware and software developers, and end-users. Understand their needs, capabilities, and constraints. For instance:\n\nDevice manufacturers might prioritize low power consumption.\nSoftware developers might emphasize ease of integration.\nEnd-users would prioritize accuracy and responsiveness.\n\nUnderstanding the Constraints and Limitations of Embedded Systems: Embedded devices come with their own set of challenges:\n\nMemory Limitations: KWS models must be lightweight to fit within the memory constraints of embedded devices. Typically, KWS models need to be as small as 16KB to fit in the always-on island of the SoC. Moreover, this is just the model size. Additional application code for preprocessing may also need to fit within the memory constraints.\nProcessing Power: The computational capabilities of embedded devices are limited (a few hundred MHz of clock speed), so the KWS model must be optimized for efficiency.\nPower Consumption: Since many embedded devices are battery-powered, the KWS system must be power-efficient.\nEnvironmental Challenges: Devices might be deployed in various environments, from quiet bedrooms to noisy industrial settings. The KWS system must be robust enough to function effectively across these scenarios.\n\nData Collection and Analysis: For a KWS system, the quality and diversity of data are paramount. Considerations might include:\n\nVariety of Accents: Collect data from speakers with various accents to ensure wide-ranging recognition.\nBackground Noises: Include data samples with different ambient noises to train the model for real-world scenarios.\nKeyword Variations: People might either pronounce keywords differently or have slight variations in the wake word itself. Ensure the dataset captures these nuances.\n\nIterative Feedback and Refinement: Once a prototype KWS system is developed, it’s crucial to test it in real-world scenarios, gather feedback, and iteratively refine the model. This ensures that the system remains aligned with the defined problem and objectives. This is important because the deployment scenarios change over time as things evolve.\n\n\n\n\n\n\n\nExercise 5.1: Keyword Spotting with TensorFlow Lite Micro\n\n\n\n\n\nExplore a hands-on guide for building and deploying Keyword Spotting (KWS) systems using TensorFlow Lite Micro. Follow steps from data collection to model training and deployment to microcontrollers. Learn to create efficient KWS models that recognize specific keywords amidst background noise. Perfect for those interested in machine learning on embedded systems. Unlock the potential of voice-enabled devices with TensorFlow Lite Micro!\n\n\n\n\nThe current chapter underscores the essential role of data quality in ML, using Keyword Spotting (KWS) systems as an example. It outlines key steps, from problem definition to stakeholder engagement, emphasizing iterative feedback. The forthcoming chapter will dig deeper into data quality management, discussing its consequences and future trends, focusing on the importance of high-quality, diverse data in AI system development, addressing ethical considerations and data sourcing methods.", "crumbs": [ "Workflow", "5  Data Engineering" @@ -587,7 +587,7 @@ "href": "contents/frameworks/frameworks.html#sec-ai_frameworks-advanced", "title": "6  AI Frameworks", "section": "6.5 Advanced Features", - "text": "6.5 Advanced Features\nBeyond providing the essential tools for training machine learning models, frameworks also offer advanced features. These features include distributing training across different hardware platforms, fine-tuning large pre-trained models with ease, and facilitating federated learning. Implementing these capabilities independently would be highly complex and resource-intensive, but frameworks simplify these processes, making advanced machine learning techniques more accessible.\n\n6.5.1 Distributed training\nAs machine learning models have become larger over the years, it has become essential for large models to use multiple computing nodes in the training process. This process, distributed learning, has allowed for higher training capabilities but has also imposed challenges in implementation.\nWe can consider three different ways to spread the work of training machine learning models to multiple computing nodes. Input data partitioning (or data parallelism) refers to multiple processors running the same model on different input partitions. This is the easiest implementation and is available for many machine learning frameworks. The more challenging distribution of work comes with model parallelism, which refers to multiple computing nodes working on different parts of the model, and pipelined model parallelism, which refers to multiple computing nodes working on different layers of the model on the same input. The latter two mentioned here are active research areas.\nML frameworks that support distributed learning include TensorFlow (through its tf.distribute module), PyTorch (through its torch.nn.DataParallel and torch.nn.DistributedDataParallel modules), and MXNet (through its gluon API).\n\n\n6.5.2 Model Conversion\nMachine learning models have various methods to be represented and used within different frameworks and for different device types. For example, a model can be converted to be compatible with inference frameworks within the mobile device. The default format for TensorFlow models is checkpoint files containing weights and architectures, which are needed to retrain the models. However, models are typically converted to TensorFlow Lite format for mobile deployment. TensorFlow Lite uses a compact flat buffer representation and optimizations for fast inference on mobile hardware, discarding all the unnecessary baggage associated with training metadata, such as checkpoint file structures.\nModel optimizations like quantization (see Optimizations chapter) can further optimize models for target architectures like mobile. This reduces the precision of weights and activations to uint8 or int8 for a smaller footprint and faster execution with supported hardware accelerators. For post-training quantization, TensorFlow’s converter handles analysis and conversion automatically.\nFrameworks like TensorFlow simplify deploying trained models to mobile and embedded IoT devices through easy conversion APIs for TFLite format and quantization. Ready-to-use conversion enables high-performance inference on mobile without a manual optimization burden. Besides TFLite, other common targets include TensorFlow.js for web deployment, TensorFlow Serving for cloud services, and TensorFlow Hub for transfer learning. TensorFlow’s conversion utilities handle these scenarios to streamline end-to-end workflows.\nMore information about model conversion in TensorFlow is linked here.\n\n\n6.5.3 AutoML, No-Code/Low-Code ML\nIn many cases, machine learning can have a relatively high barrier of entry compared to other fields. To successfully train and deploy models, one needs to have a critical understanding of a variety of disciplines, from data science (data processing, data cleaning), model structures (hyperparameter tuning, neural network architecture), hardware (acceleration, parallel processing), and more depending on the problem at hand. The complexity of these problems has led to the introduction of frameworks such as AutoML, which aims to make “Machine learning available for non-Machine Learning experts” and to “automate research in machine learning.” They have constructed AutoWEKA, which aids in the complex process of hyperparameter selection, and Auto-sklearn and Auto-pytorch, an extension of AutoWEKA into the popular sklearn and PyTorch Libraries.\nWhile these efforts to automate parts of machine learning tasks are underway, others have focused on making machine learning models easier by deploying no-code/low-code machine learning, utilizing a drag-and-drop interface with an easy-to-navigate user interface. Companies such as Apple, Google, and Amazon have already created these easy-to-use platforms to allow users to construct machine learning models that can integrate into their ecosystem.\nThese steps to remove barriers to entry continue to democratize machine learning, make it easier for beginners to access, and simplify workflow for experts.\n\n\n6.5.4 Advanced Learning Methods\n\nTransfer Learning\nTransfer learning is the practice of using knowledge gained from a pre-trained model to train and improve the performance of a model for a different task. For example, models such as MobileNet and ResNet are trained on the ImageNet dataset. To do so, one may freeze the pre-trained model, utilizing it as a feature extractor to train a much smaller model built on top of the feature extraction. One can also fine-tune the entire model to fit the new task. Machine learning frameworks make it easy to load pre-trained models, freeze specific layers, and train custom layers on top. They simplify this process by providing intuitive APIs and easy access to large repositories of pre-trained models.\nTransfer learning has challenges, such as the modified model’s inability to conduct its original tasks after transfer learning. Papers such as “Learning without Forgetting” by Z. Li and Hoiem (2018) aims to address these challenges and have been implemented in modern machine learning platforms.\n\nLi, Zhizhong, and Derek Hoiem. 2018. “Learning Without Forgetting.” IEEE Trans. Pattern Anal. Mach. Intell. 40 (12): 2935–47. https://doi.org/10.1109/tpami.2017.2773081.\n\n\nFederated Learning\nFederated learning by McMahan et al. (2017) is a form of distributed computing that involves training models on personal devices rather than centralizing the data on a single server (Figure 6.7). Initially, a base global model is trained on a central server to be distributed to all devices. Using this base model, the devices individually compute the gradients and send them back to the central hub. Intuitively, this transfers model parameters instead of the data itself. Federated learning enhances privacy by keeping sensitive data on local devices and only sharing model updates with a central server. This method is particularly useful when dealing with sensitive data or when a large-scale infrastructure is impractical.\n\nMcMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017. “Communication-Efficient Learning of Deep Networks from Decentralized Data.” In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA, edited by Aarti Singh and Xiaojin (Jerry) Zhu, 54:1273–82. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v54/mcmahan17a.html.\n\n\n\n\n\n\nFigure 6.7: A centralized-server approach to federated learning. Source: NVIDIA.\n\n\n\nHowever, federated learning faces challenges such as ensuring data accuracy, managing non-IID (independent and identically distributed) data, dealing with unbalanced data production, and overcoming communication overhead and device heterogeneity. Privacy and security concerns, such as gradient inversion attacks, also pose significant challenges.\nMachine learning frameworks simplify the implementation of federated learning by providing necessary tools and libraries. For example, TensorFlow Federated (TFF) offers an open-source framework to support federated learning. TFF allows developers to simulate and implement federated learning algorithms, offering a federated core for low-level operations and high-level APIs for common federated tasks. It seamlessly integrates with TensorFlow, enabling the use of TensorFlow models and optimizers in a federated setting. TFF supports secure aggregation techniques to improve privacy and allows for customization of federated learning algorithms. By leveraging these tools, developers can efficiently distribute training, fine-tune pre-trained models, and handle federated learning’s inherent complexities.\nOther open source programs such as Flower have also been developed to simplify implementing federated learning with various machine learning frameworks.", + "text": "6.5 Advanced Features\nBeyond providing the essential tools for training machine learning models, frameworks also offer advanced features. These features include distributing training across different hardware platforms, fine-tuning large pre-trained models with ease, and facilitating federated learning. Implementing these capabilities independently would be highly complex and resource-intensive, but frameworks simplify these processes, making advanced machine learning techniques more accessible.\n\n6.5.1 Distributed training\nAs machine learning models have become larger over the years, it has become essential for large models to use multiple computing nodes in the training process. This process, distributed learning, has allowed for higher training capabilities but has also imposed challenges in implementation.\nWe can consider three different ways to spread the work of training machine learning models to multiple computing nodes. Input data partitioning (or data parallelism) refers to multiple processors running the same model on different input partitions. This is the easiest implementation and is available for many machine learning frameworks. The more challenging distribution of work comes with model parallelism, which refers to multiple computing nodes working on different parts of the model, and pipelined model parallelism, which refers to multiple computing nodes working on different layers of the model on the same input. The latter two mentioned here are active research areas.\nML frameworks that support distributed learning include TensorFlow (through its tf.distribute module), PyTorch (through its torch.nn.DataParallel and torch.nn.DistributedDataParallel modules), and MXNet (through its gluon API).\n\n\n6.5.2 Model Conversion\nMachine learning models have various methods to be represented and used within different frameworks and for different device types. For example, a model can be converted to be compatible with inference frameworks within the mobile device. The default format for TensorFlow models is checkpoint files containing weights and architectures, which are needed to retrain the models. However, models are typically converted to TensorFlow Lite format for mobile deployment. TensorFlow Lite uses a compact flat buffer representation and optimizations for fast inference on mobile hardware, discarding all the unnecessary baggage associated with training metadata, such as checkpoint file structures.\nModel optimizations like quantization (see Optimizations chapter) can further optimize models for target architectures like mobile. This reduces the precision of weights and activations to uint8 or int8 for a smaller footprint and faster execution with supported hardware accelerators. For post-training quantization, TensorFlow’s converter handles analysis and conversion automatically.\nFrameworks like TensorFlow simplify deploying trained models to mobile and embedded IoT devices through easy conversion APIs for TFLite format and quantization. Ready-to-use conversion enables high-performance inference on mobile without a manual optimization burden. Besides TFLite, other common targets include TensorFlow.js for web deployment, TensorFlow Serving for cloud services, and TensorFlow Hub for transfer learning. TensorFlow’s conversion utilities handle these scenarios to streamline end-to-end workflows.\nMore information about model conversion in TensorFlow is linked here.\n\n\n6.5.3 AutoML, No-Code/Low-Code ML\nIn many cases, machine learning can have a relatively high barrier of entry compared to other fields. To successfully train and deploy models, one needs to have a critical understanding of a variety of disciplines, from data science (data processing, data cleaning), model structures (hyperparameter tuning, neural network architecture), hardware (acceleration, parallel processing), and more depending on the problem at hand. The complexity of these problems has led to the introduction of frameworks such as AutoML, which tries to make “Machine learning available for non-Machine Learning experts” and to “automate research in machine learning.” They have constructed AutoWEKA, which aids in the complex process of hyperparameter selection, and Auto-sklearn and Auto-pytorch, an extension of AutoWEKA into the popular sklearn and PyTorch Libraries.\nWhile these efforts to automate parts of machine learning tasks are underway, others have focused on making machine learning models easier by deploying no-code/low-code machine learning, utilizing a drag-and-drop interface with an easy-to-navigate user interface. Companies such as Apple, Google, and Amazon have already created these easy-to-use platforms to allow users to construct machine learning models that can integrate into their ecosystem.\nThese steps to remove barriers to entry continue to democratize machine learning, make it easier for beginners to access, and simplify workflow for experts.\n\n\n6.5.4 Advanced Learning Methods\n\nTransfer Learning\nTransfer learning is the practice of using knowledge gained from a pre-trained model to train and improve the performance of a model for a different task. For example, models such as MobileNet and ResNet are trained on the ImageNet dataset. To do so, one may freeze the pre-trained model, utilizing it as a feature extractor to train a much smaller model built on top of the feature extraction. One can also fine-tune the entire model to fit the new task. Machine learning frameworks make it easy to load pre-trained models, freeze specific layers, and train custom layers on top. They simplify this process by providing intuitive APIs and easy access to large repositories of pre-trained models.\nTransfer learning has challenges, such as the modified model’s inability to conduct its original tasks after transfer learning. Papers such as “Learning without Forgetting” by Z. Li and Hoiem (2018) try to address these challenges and have been implemented in modern machine learning platforms.\n\nLi, Zhizhong, and Derek Hoiem. 2018. “Learning Without Forgetting.” IEEE Trans. Pattern Anal. Mach. Intell. 40 (12): 2935–47. https://doi.org/10.1109/tpami.2017.2773081.\n\n\nFederated Learning\nFederated learning by McMahan et al. (2017) is a form of distributed computing that involves training models on personal devices rather than centralizing the data on a single server (Figure 6.7). Initially, a base global model is trained on a central server to be distributed to all devices. Using this base model, the devices individually compute the gradients and send them back to the central hub. Intuitively, this transfers model parameters instead of the data itself. Federated learning enhances privacy by keeping sensitive data on local devices and only sharing model updates with a central server. This method is particularly useful when dealing with sensitive data or when a large-scale infrastructure is impractical.\n\nMcMahan, Brendan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. 2017. “Communication-Efficient Learning of Deep Networks from Decentralized Data.” In Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA, edited by Aarti Singh and Xiaojin (Jerry) Zhu, 54:1273–82. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v54/mcmahan17a.html.\n\n\n\n\n\n\nFigure 6.7: A centralized-server approach to federated learning. Source: NVIDIA.\n\n\n\nHowever, federated learning faces challenges such as ensuring data accuracy, managing non-IID (independent and identically distributed) data, dealing with unbalanced data production, and overcoming communication overhead and device heterogeneity. Privacy and security concerns, such as gradient inversion attacks, also pose significant challenges.\nMachine learning frameworks simplify the implementation of federated learning by providing necessary tools and libraries. For example, TensorFlow Federated (TFF) offers an open-source framework to support federated learning. TFF allows developers to simulate and implement federated learning algorithms, offering a federated core for low-level operations and high-level APIs for common federated tasks. It seamlessly integrates with TensorFlow, enabling the use of TensorFlow models and optimizers in a federated setting. TFF supports secure aggregation techniques to improve privacy and allows for customization of federated learning algorithms. By leveraging these tools, developers can efficiently distribute training, fine-tune pre-trained models, and handle federated learning’s inherent complexities.\nOther open source programs such as Flower have also been developed to simplify implementing federated learning with various machine learning frameworks.", "crumbs": [ "Workflow", "6  AI Frameworks" @@ -675,7 +675,7 @@ "href": "contents/training/training.html", "title": "7  AI Training", "section": "", - "text": "7.1 Introduction\nTraining is critical for developing accurate and useful AI systems using machine learning. The training aims to create a machine learning model that can generalize to new, unseen data rather than memorizing the training examples. This is done by feeding training data into algorithms that learn patterns from these examples by adjusting internal parameters.\nThe algorithms minimize a loss function, which compares their predictions on the training data to the known labels or solutions, guiding the learning. Effective training often requires high-quality, representative data sets large enough to capture variability in real-world use cases.\nIt also requires choosing an algorithm suited to the task, whether a neural network for computer vision, a reinforcement learning algorithm for robotic control, or a tree-based method for categorical prediction. Careful tuning is needed for the model structure, such as neural network depth and width, and learning parameters like step size and regularization strength.\nTechniques to prevent overfitting like regularization penalties and validation with held-out data, are also important. Overfitting can occur when a model fits the training data too closely, failing to generalize to new data. This can happen if the model is too complex or trained too long.\nTo avoid overfitting, regularization techniques can help constrain the model. One regularization method is adding a penalty term to the loss function that discourages complexity, like the L2 norm of the weights. This penalizes large parameter values. Another technique is dropout, where a percentage of neurons is randomly set to zero during training. This reduces neuron co-adaptation.\nValidation methods also help detect and avoid overfitting. Part of the training data is held out from the training loop as a validation set. The model is evaluated on this data. If validation error increases while training error decreases, overfitting occurs. The training can then be stopped early or regularized more strongly. Regularization and validation enable models to train to maximum capability without overfitting the training data.\nTraining takes significant computing resources, especially for deep neural networks used in computer vision, natural language processing, and other areas. These networks have millions of adjustable weights that must be tuned through extensive training. Hardware improvements and distributed training techniques have enabled training ever larger neural nets that can achieve human-level performance on some tasks.\nIn summary, some key points about training:\nWe will walk you through these details in the rest of the sections. Understanding how to effectively leverage data, algorithms, parameter optimization, and generalization through thorough training is essential for developing capable, deployable AI systems that work robustly in the real world.", + "text": "7.1 Introduction\nTraining is critical for developing accurate and useful AI systems using machine learning. The training creates a machine learning model that can generalize to new, unseen data rather than memorizing the training examples. This is done by feeding training data into algorithms that learn patterns from these examples by adjusting internal parameters.\nThe algorithms minimize a loss function, which compares their predictions on the training data to the known labels or solutions, guiding the learning. Effective training often requires high-quality, representative data sets large enough to capture variability in real-world use cases.\nIt also requires choosing an algorithm suited to the task, whether a neural network for computer vision, a reinforcement learning algorithm for robotic control, or a tree-based method for categorical prediction. Careful tuning is needed for the model structure, such as neural network depth and width, and learning parameters like step size and regularization strength.\nTechniques to prevent overfitting like regularization penalties and validation with held-out data, are also important. Overfitting can occur when a model fits the training data too closely, failing to generalize to new data. This can happen if the model is too complex or trained too long.\nTo avoid overfitting, regularization techniques can help constrain the model. One regularization method is adding a penalty term to the loss function that discourages complexity, like the L2 norm of the weights. This penalizes large parameter values. Another technique is dropout, where a percentage of neurons is randomly set to zero during training. This reduces neuron co-adaptation.\nValidation methods also help detect and avoid overfitting. Part of the training data is held out from the training loop as a validation set. The model is evaluated on this data. If validation error increases while training error decreases, overfitting occurs. The training can then be stopped early or regularized more strongly. Regularization and validation enable models to train to maximum capability without overfitting the training data.\nTraining takes significant computing resources, especially for deep neural networks used in computer vision, natural language processing, and other areas. These networks have millions of adjustable weights that must be tuned through extensive training. Hardware improvements and distributed training techniques have enabled training ever larger neural nets that can achieve human-level performance on some tasks.\nIn summary, some key points about training:\nWe will walk you through these details in the rest of the sections. Understanding how to effectively leverage data, algorithms, parameter optimization, and generalization through thorough training is essential for developing capable, deployable AI systems that work robustly in the real world.", "crumbs": [ "Training", "7  AI Training" @@ -741,7 +741,7 @@ "href": "contents/training/training.html#hyperparameter-tuning", "title": "7  AI Training", "section": "7.6 Hyperparameter Tuning", - "text": "7.6 Hyperparameter Tuning\nHyperparameters are important settings in machine learning models that greatly impact how well your models ultimately perform. Unlike other model parameters that are learned during training, hyperparameters are specified by the data scientists or machine learning engineers before training the model.\nChoosing the right hyperparameter values enables your models to learn patterns from data effectively. Some examples of key hyperparameters across ML algorithms include:\n\nNeural networks: Learning rate, batch size, number of hidden units, activation functions\nSupport vector machines: Regularization strength, kernel type and parameters\nRandom forests: Number of trees, tree depth\nK-means: Number of clusters\n\nThe problem is that there are no reliable rules of thumb for choosing optimal hyperparameter configurations—you typically have to try out different values and evaluate performance. This process is called hyperparameter tuning.\nIn the early years of modern deep learning, researchers were still grappling with unstable and slow convergence issues. Common pain points included training losses fluctuating wildly, gradients exploding or vanishing, and extensive trial-and-error needed to train networks reliably. As a result, an early focal point was using hyperparameters to control model optimization. For instance, seminal techniques like batch normalization allowed faster model convergence by tuning aspects of internal covariate shift. Adaptive learning rate methods also mitigated the need for extensive manual schedules. These addressed optimization issues during training, such as uncontrolled gradient divergence. Carefully adapted learning rates are also the primary control factor for achieving rapid and stable convergence even today.\nAs computational capacity expanded exponentially in subsequent years, much larger models could be trained without falling prey to pure numerical optimization issues. The focus shifted towards generalization - though efficient convergence was a core prerequisite. State-of-the-art techniques like Transformers brought in parameters in billions. At such sizes, hyperparameters around capacity, regularization, ensembling, etc., took center stage for tuning rather than only raw convergence metrics.\nThe lesson is that understanding the acceleration and stability of the optimization process itself constitutes the groundwork. Initialization schemes, batch sizes, weight decays, and other training hyperparameters remain indispensable today. Mastering fast and flawless convergence allows practitioners to expand their focus on emerging needs around tuning for metrics like accuracy, robustness, and efficiency at scale.\n\n7.6.1 Search Algorithms\nWhen it comes to the critical process of hyperparameter tuning, there are several sophisticated algorithms that machine learning practitioners rely on to search through the vast space of possible model configurations systematically. Some of the most prominent hyperparameter search algorithms include:\n\nGrid Search: The most basic search method, where you manually define a grid of values to check for each hyperparameter. For example, checking learning rates = [0.01, 0.1, 1] and batch sizes = [32, 64, 128]. The key advantage is simplicity, but it can lead to an exponential explosion in search space, making it time-consuming. It’s best suited for fine-tuning a small number of parameters.\nRandom Search: Instead of defining a grid, you randomly select values for each hyperparameter from a predefined range or set. This method is more efficient at exploring a vast hyperparameter space because it doesn’t require an exhaustive search. However, it may still miss optimal parameters since it doesn’t systematically explore all possible combinations.\nBayesian Optimization: This is an advanced probabilistic approach for adaptive exploration based on a surrogate function to model performance over iterations. It is simple and efficient—it finds highly optimized hyperparameters in fewer evaluation steps. However, it requires more investment in setup (Snoek, Larochelle, and Adams 2012).\nEvolutionary Algorithms: These algorithms mimic natural selection principles. They generate populations of hyperparameter combinations and evolve them over time-based on performance. These algorithms offer robust search capabilities better suited for complex response surfaces. However, many iterations are required for reasonable convergence.\nPopulation Based Training (PBT): A method that optimizes hyperparameters by training multiple models in parallel, allowing them to share and adapt successful configurations during training, combining elements of random search and evolutionary algorithms (Jaderberg et al. 2017).\nNeural Architecture Search: An approach to designing well-performing architectures for neural networks. Traditionally, NAS approaches use some form of reinforcement learning to propose neural network architectures, which are then repeatedly evaluated (Zoph and Le 2016).\n\n\nSnoek, Jasper, Hugo Larochelle, and Ryan P. Adams. 2012. “Practical Bayesian Optimization of Machine Learning Algorithms.” In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a Meeting Held December 3-6, 2012, Lake Tahoe, Nevada, United States, edited by Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q. Weinberger, 2960–68. https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html.\n\nJaderberg, Max, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, et al. 2017. “Population Based Training of Neural Networks.” arXiv Preprint arXiv:1711.09846, November. http://arxiv.org/abs/1711.09846v2.\n\nZoph, Barret, and Quoc V. Le. 2016. “Neural Architecture Search with Reinforcement Learning,” November, 367–92. https://doi.org/10.1002/9781394217519.ch17.\n\n\n7.6.2 System Implications\nHyperparameter tuning can significantly impact time to convergence during model training, directly affecting overall runtime. The right values for key training hyperparameters are crucial for efficient model convergence. For example, the hyperparameter’s learning rate controls the step size during gradient descent optimization. Setting a properly tuned learning rate schedule ensures the optimization algorithm converges quickly towards a good minimum. Too small a learning rate leads to painfully slow convergence, while too large a value causes the losses to fluctuate wildly. Proper tuning ensures rapid movement towards optimal weights and biases.\nSimilarly, the batch size for stochastic gradient descent impacts convergence stability. The right batch size smooths out fluctuations in parameter updates to approach the minimum faster. More batch sizes are needed to avoid noisy convergence, while large batch sizes fail to generalize and slow down convergence due to less frequent parameter updates. Tuning hyperparameters for faster convergence and reduced training duration has direct implications on cost and resource requirements for scaling machine learning systems:\n\nLower computational costs: Shorter time to convergence means lower computational costs for training models. ML training often leverages large cloud computing instances like GPU and TPU clusters that incur heavy hourly charges. Minimizing training time directly reduces this resource rental cost, which tends to dominate ML budgets for organizations. Quicker iteration also lets data scientists experiment more freely within the same budget.\nReduced training time: Reduced training time unlocks opportunities to train more models using the same computational budget. Optimized hyperparameters stretch available resources further, allowing businesses to develop and experiment with more models under resource constraints to maximize performance.\nResource efficiency: Quicker training allows allocating smaller compute instances in the cloud since models require access to the resources for a shorter duration. For example, a one-hour training job allows using less powerful GPU instances compared to multi-hour training, which requires sustained compute access over longer intervals. This achieves cost savings, especially for large workloads.\n\nThere are other benefits as well. For instance, faster convergence reduces pressure on ML engineering teams regarding provisioning training resources. Simple model retraining routines can use lower-powered resources instead of requesting access to high-priority queues for constrained production-grade GPU clusters, freeing up deployment resources for other applications.\n\n\n7.6.3 Auto Tuners\nGiven its importance, there is a wide array of commercial offerings to help with hyperparameter tuning. We will briefly touch on two examples: one focused on optimization for cloud-scale ML and the other for machine learning models targeting microcontrollers. Table 7.3 outlines the key differences:\n\n\n\nTable 7.3: Comparison of optimization platforms for different machine learning use cases.\n\n\n\n\n\n\n\n\n\n\n\nPlatform\nTarget Use Case\nOptimization Techniques\nBenefits\n\n\n\n\nGoogle’s Vertex AI\nCloud-scale machine learning\nBayesian optimization, Population-Based training\nHides complexity, enabling fast, deployment-ready models with state-of-the-art hyperparameter optimization\n\n\nEdge Impulse’s EON Tuner\nMicrocontroller (TinyML) models\nBayesian optimization\nTailors models for resource-constrained devices, simplifies optimization for embedded deployment\n\n\n\n\n\n\n\nBigML\nSeveral commercial auto-tuning platforms are available to address this problem. One solution is Google’s Vertex AI Cloud, which has extensive integrated support for state-of-the-art tuning techniques.\nOne of the most salient capabilities of Google’s Vertex AI-managed machine learning platform is efficient, integrated hyperparameter tuning for model development. Successfully training performant ML models requires identifying optimal configurations for a set of external hyperparameters that dictate model behavior, posing a challenging high-dimensional search problem. Vertex AI aims to simplify this through Automated Machine Learning (AutoML) tooling.\nSpecifically, data scientists can leverage Vertex AI’s hyperparameter tuning engines by providing a labeled dataset and choosing a model type such as a Neural Network or Random Forest classifier. Vertex launches a Hyperparameter Search job transparently on the backend, fully handling resource provisioning, model training, metric tracking, and result analysis automatically using advanced optimization algorithms.\nUnder the hood, Vertex AutoML employs various search strategies to intelligently explore the most promising hyperparameter configurations based on previous evaluation results. Among these, Bayesian Optimization is offered as it provides superior sample efficiency, requiring fewer training iterations to achieve optimized model quality compared to standard Grid Search or Random Search methods. For more complex neural architecture search spaces, Vertex AutoML utilizes Population-Based Training, which simultaneously trains multiple models and dynamically adjusts their hyperparameters by leveraging the performance of other models in the population, analogous to natural selection principles.\nVertex AI aims to democratize state-of-the-art hyperparameter search techniques at the cloud scale for all ML developers, abstracting away the underlying orchestration and execution complexity. Users focus solely on their dataset, model requirements, and accuracy goals, while Vertex manages the tuning cycle, resource allocation, model training, accuracy tracking, and artifact storage under the hood. The result is getting deployment-ready, optimized ML models faster for the target problem.\n\n\nTinyML\nEdge Impulse’s Efficient On-device Neural Network Tuner (EON Tuner) is an automated hyperparameter optimization tool designed to develop microcontroller machine learning models. It streamlines the model development process by automatically finding the best neural network configuration for efficient and accurate deployment on resource-constrained devices.\nThe key functionality of the EON Tuner is as follows. First, developers define the model hyperparameters, such as number of layers, nodes per layer, activation functions, and learning rate annealing schedule. These parameters constitute the search space that will be optimized. Next, the target microcontroller platform is selected, providing embedded hardware constraints. The user can also specify optimization objectives, such as minimizing memory footprint, lowering latency, reducing power consumption, or maximizing accuracy.\nWith the defined search space and optimization goals, the EON Tuner leverages Bayesian hyperparameter optimization to explore possible configurations intelligently. Each prospective configuration is automatically implemented as a full model specification, trained, and evaluated for quality metrics. The continual process balances exploration and exploitation to arrive at optimized settings tailored to the developer’s chosen chip architecture and performance requirements.\nThe EON Tuner frees machine learning engineers from the demandingly iterative process of hand-tuning models by automatically tuning models for embedded deployment. The tool integrates seamlessly into the Edge Impulse workflow, taking models from concept to efficiently optimized implementations on microcontrollers. The expertise encapsulated in EON Tuner regarding ML model optimization for microcontrollers ensures beginner and experienced developers alike can rapidly iterate to models fitting their project needs.\n\n\n\n\n\n\nExercise 7.2: Hyperparameter Tuning\n\n\n\n\n\nGet ready to unlock the secrets of hyperparameter tuning and take your PyTorch models to the next level! Hyperparameters are like the hidden dials and knobs that control your model’s learning superpowers. In this Colab notebook, you’ll team up with Ray Tune to find those perfect hyperparameter combinations. Learn how to define what values to search through, set up your training code for optimization, and let Ray Tune do the heavy lifting. By the end, you’ll be a hyperparameter tuning pro!\n\n\n\n\nVideo 7.3 explains the systematic organization of the hyperparameter tuning process.\n\n\n\n\n\n\nVideo 7.3: Hyperparameter", + "text": "7.6 Hyperparameter Tuning\nHyperparameters are important settings in machine learning models that greatly impact how well your models ultimately perform. Unlike other model parameters that are learned during training, hyperparameters are specified by the data scientists or machine learning engineers before training the model.\nChoosing the right hyperparameter values enables your models to learn patterns from data effectively. Some examples of key hyperparameters across ML algorithms include:\n\nNeural networks: Learning rate, batch size, number of hidden units, activation functions\nSupport vector machines: Regularization strength, kernel type and parameters\nRandom forests: Number of trees, tree depth\nK-means: Number of clusters\n\nThe problem is that there are no reliable rules of thumb for choosing optimal hyperparameter configurations—you typically have to try out different values and evaluate performance. This process is called hyperparameter tuning.\nIn the early years of modern deep learning, researchers were still grappling with unstable and slow convergence issues. Common pain points included training losses fluctuating wildly, gradients exploding or vanishing, and extensive trial-and-error needed to train networks reliably. As a result, an early focal point was using hyperparameters to control model optimization. For instance, seminal techniques like batch normalization allowed faster model convergence by tuning aspects of internal covariate shift. Adaptive learning rate methods also mitigated the need for extensive manual schedules. These addressed optimization issues during training, such as uncontrolled gradient divergence. Carefully adapted learning rates are also the primary control factor for achieving rapid and stable convergence even today.\nAs computational capacity expanded exponentially in subsequent years, much larger models could be trained without falling prey to pure numerical optimization issues. The focus shifted towards generalization - though efficient convergence was a core prerequisite. State-of-the-art techniques like Transformers brought in parameters in billions. At such sizes, hyperparameters around capacity, regularization, ensembling, etc., took center stage for tuning rather than only raw convergence metrics.\nThe lesson is that understanding the acceleration and stability of the optimization process itself constitutes the groundwork. Initialization schemes, batch sizes, weight decays, and other training hyperparameters remain indispensable today. Mastering fast and flawless convergence allows practitioners to expand their focus on emerging needs around tuning for metrics like accuracy, robustness, and efficiency at scale.\n\n7.6.1 Search Algorithms\nWhen it comes to the critical process of hyperparameter tuning, there are several sophisticated algorithms that machine learning practitioners rely on to search through the vast space of possible model configurations systematically. Some of the most prominent hyperparameter search algorithms include:\n\nGrid Search: The most basic search method, where you manually define a grid of values to check for each hyperparameter. For example, checking learning rates = [0.01, 0.1, 1] and batch sizes = [32, 64, 128]. The key advantage is simplicity, but it can lead to an exponential explosion in search space, making it time-consuming. It’s best suited for fine-tuning a small number of parameters.\nRandom Search: Instead of defining a grid, you randomly select values for each hyperparameter from a predefined range or set. This method is more efficient at exploring a vast hyperparameter space because it doesn’t require an exhaustive search. However, it may still miss optimal parameters since it doesn’t systematically explore all possible combinations.\nBayesian Optimization: This is an advanced probabilistic approach for adaptive exploration based on a surrogate function to model performance over iterations. It is simple and efficient—it finds highly optimized hyperparameters in fewer evaluation steps. However, it requires more investment in setup (Snoek, Larochelle, and Adams 2012).\nEvolutionary Algorithms: These algorithms mimic natural selection principles. They generate populations of hyperparameter combinations and evolve them over time-based on performance. These algorithms offer robust search capabilities better suited for complex response surfaces. However, many iterations are required for reasonable convergence.\nPopulation Based Training (PBT): A method that optimizes hyperparameters by training multiple models in parallel, allowing them to share and adapt successful configurations during training, combining elements of random search and evolutionary algorithms (Jaderberg et al. 2017).\nNeural Architecture Search: An approach to designing well-performing architectures for neural networks. Traditionally, NAS approaches use some form of reinforcement learning to propose neural network architectures, which are then repeatedly evaluated (Zoph and Le 2016).\n\n\nSnoek, Jasper, Hugo Larochelle, and Ryan P. Adams. 2012. “Practical Bayesian Optimization of Machine Learning Algorithms.” In Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a Meeting Held December 3-6, 2012, Lake Tahoe, Nevada, United States, edited by Peter L. Bartlett, Fernando C. N. Pereira, Christopher J. C. Burges, Léon Bottou, and Kilian Q. Weinberger, 2960–68. https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html.\n\nJaderberg, Max, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, et al. 2017. “Population Based Training of Neural Networks.” arXiv Preprint arXiv:1711.09846, November. http://arxiv.org/abs/1711.09846v2.\n\nZoph, Barret, and Quoc V. Le. 2016. “Neural Architecture Search with Reinforcement Learning,” November, 367–92. https://doi.org/10.1002/9781394217519.ch17.\n\n\n7.6.2 System Implications\nHyperparameter tuning can significantly impact time to convergence during model training, directly affecting overall runtime. The right values for key training hyperparameters are crucial for efficient model convergence. For example, the hyperparameter’s learning rate controls the step size during gradient descent optimization. Setting a properly tuned learning rate schedule ensures the optimization algorithm converges quickly towards a good minimum. Too small a learning rate leads to painfully slow convergence, while too large a value causes the losses to fluctuate wildly. Proper tuning ensures rapid movement towards optimal weights and biases.\nSimilarly, the batch size for stochastic gradient descent impacts convergence stability. The right batch size smooths out fluctuations in parameter updates to approach the minimum faster. More batch sizes are needed to avoid noisy convergence, while large batch sizes fail to generalize and slow down convergence due to less frequent parameter updates. Tuning hyperparameters for faster convergence and reduced training duration has direct implications on cost and resource requirements for scaling machine learning systems:\n\nLower computational costs: Shorter time to convergence means lower computational costs for training models. ML training often leverages large cloud computing instances like GPU and TPU clusters that incur heavy hourly charges. Minimizing training time directly reduces this resource rental cost, which tends to dominate ML budgets for organizations. Quicker iteration also lets data scientists experiment more freely within the same budget.\nReduced training time: Reduced training time unlocks opportunities to train more models using the same computational budget. Optimized hyperparameters stretch available resources further, allowing businesses to develop and experiment with more models under resource constraints to maximize performance.\nResource efficiency: Quicker training allows allocating smaller compute instances in the cloud since models require access to the resources for a shorter duration. For example, a one-hour training job allows using less powerful GPU instances compared to multi-hour training, which requires sustained compute access over longer intervals. This achieves cost savings, especially for large workloads.\n\nThere are other benefits as well. For instance, faster convergence reduces pressure on ML engineering teams regarding provisioning training resources. Simple model retraining routines can use lower-powered resources instead of requesting access to high-priority queues for constrained production-grade GPU clusters, freeing up deployment resources for other applications.\n\n\n7.6.3 Auto Tuners\nGiven its importance, there is a wide array of commercial offerings to help with hyperparameter tuning. We will briefly touch on two examples: one focused on optimization for cloud-scale ML and the other for machine learning models targeting microcontrollers. Table 7.3 outlines the key differences:\n\n\n\nTable 7.3: Comparison of optimization platforms for different machine learning use cases.\n\n\n\n\n\n\n\n\n\n\n\nPlatform\nTarget Use Case\nOptimization Techniques\nBenefits\n\n\n\n\nGoogle’s Vertex AI\nCloud-scale machine learning\nBayesian optimization, Population-Based training\nHides complexity, enabling fast, deployment-ready models with state-of-the-art hyperparameter optimization\n\n\nEdge Impulse’s EON Tuner\nMicrocontroller (TinyML) models\nBayesian optimization\nTailors models for resource-constrained devices, simplifies optimization for embedded deployment\n\n\n\n\n\n\n\nBigML\nSeveral commercial auto-tuning platforms are available to address this problem. One solution is Google’s Vertex AI Cloud, which has extensive integrated support for state-of-the-art tuning techniques.\nOne of the most salient capabilities of Google’s Vertex AI-managed machine learning platform is efficient, integrated hyperparameter tuning for model development. Successfully training performant ML models requires identifying optimal configurations for a set of external hyperparameters that dictate model behavior, posing a challenging high-dimensional search problem. Vertex AI simplifies this through Automated Machine Learning (AutoML) tooling.\nSpecifically, data scientists can leverage Vertex AI’s hyperparameter tuning engines by providing a labeled dataset and choosing a model type such as a Neural Network or Random Forest classifier. Vertex launches a Hyperparameter Search job transparently on the backend, fully handling resource provisioning, model training, metric tracking, and result analysis automatically using advanced optimization algorithms.\nUnder the hood, Vertex AutoML employs various search strategies to intelligently explore the most promising hyperparameter configurations based on previous evaluation results. Among these, Bayesian Optimization is offered as it provides superior sample efficiency, requiring fewer training iterations to achieve optimized model quality compared to standard Grid Search or Random Search methods. For more complex neural architecture search spaces, Vertex AutoML utilizes Population-Based Training, which simultaneously trains multiple models and dynamically adjusts their hyperparameters by leveraging the performance of other models in the population, analogous to natural selection principles.\nVertex AI democratizes state-of-the-art hyperparameter search techniques at the cloud scale for all ML developers, abstracting away the underlying orchestration and execution complexity. Users focus solely on their dataset, model requirements, and accuracy goals, while Vertex manages the tuning cycle, resource allocation, model training, accuracy tracking, and artifact storage under the hood. The result is getting deployment-ready, optimized ML models faster for the target problem.\n\n\nTinyML\nEdge Impulse’s Efficient On-device Neural Network Tuner (EON Tuner) is an automated hyperparameter optimization tool designed to develop microcontroller machine learning models. It streamlines the model development process by automatically finding the best neural network configuration for efficient and accurate deployment on resource-constrained devices.\nThe key functionality of the EON Tuner is as follows. First, developers define the model hyperparameters, such as number of layers, nodes per layer, activation functions, and learning rate annealing schedule. These parameters constitute the search space that will be optimized. Next, the target microcontroller platform is selected, providing embedded hardware constraints. The user can also specify optimization objectives, such as minimizing memory footprint, lowering latency, reducing power consumption, or maximizing accuracy.\nWith the defined search space and optimization goals, the EON Tuner leverages Bayesian hyperparameter optimization to explore possible configurations intelligently. Each prospective configuration is automatically implemented as a full model specification, trained, and evaluated for quality metrics. The continual process balances exploration and exploitation to arrive at optimized settings tailored to the developer’s chosen chip architecture and performance requirements.\nThe EON Tuner frees machine learning engineers from the demandingly iterative process of hand-tuning models by automatically tuning models for embedded deployment. The tool integrates seamlessly into the Edge Impulse workflow, taking models from concept to efficiently optimized implementations on microcontrollers. The expertise encapsulated in EON Tuner regarding ML model optimization for microcontrollers ensures beginner and experienced developers alike can rapidly iterate to models fitting their project needs.\n\n\n\n\n\n\nExercise 7.2: Hyperparameter Tuning\n\n\n\n\n\nGet ready to unlock the secrets of hyperparameter tuning and take your PyTorch models to the next level! Hyperparameters are like the hidden dials and knobs that control your model’s learning superpowers. In this Colab notebook, you’ll team up with Ray Tune to find those perfect hyperparameter combinations. Learn how to define what values to search through, set up your training code for optimization, and let Ray Tune do the heavy lifting. By the end, you’ll be a hyperparameter tuning pro!\n\n\n\n\nVideo 7.3 explains the systematic organization of the hyperparameter tuning process.\n\n\n\n\n\n\nVideo 7.3: Hyperparameter", "crumbs": [ "Training", "7  AI Training" @@ -880,8 +880,8 @@ ] }, { - "objectID": "contents/efficient_ai/efficient_ai.html#efficient-numerics", - "href": "contents/efficient_ai/efficient_ai.html#efficient-numerics", + "objectID": "contents/efficient_ai/efficient_ai.html#sec-efficient-numerics", + "href": "contents/efficient_ai/efficient_ai.html#sec-efficient-numerics", "title": "8  Efficient AI", "section": "8.6 Efficient Numerics", "text": "8.6 Efficient Numerics\nMachine learning, and especially deep learning, involves enormous amounts of computation. Models can have millions to billions of parameters, often trained on vast datasets. Every operation, every multiplication or addition, demands computational resources. Therefore, the precision of the numbers used in these operations can significantly impact the computational speed, energy consumption, and memory requirements. This is where the concept of efficient numerics comes into play.\n\n8.6.1 Numerical Formats\nThere are many different types of numerics. Numerics have a long history in computing systems.\nFloating point: Known as a single-precision floating point, FP32 utilizes 32 bits to represent a number, incorporating its sign, exponent, and mantissa. Understanding how floating point numbers are represented under the hood is crucial for grasping the various optimizations possible in numerical computations. The sign bit determines whether the number is positive or negative, the exponent controls the range of values that can be represented, and the mantissa determines the precision of the number. The combination of these components allows floating point numbers to represent a vast range of values with varying degrees of precision.\nVideo 8.1 provides a comprehensive overview of these three main components - sign, exponent, and mantissa - and how they work together to represent floating point numbers.\n\n\n\n\n\n\nVideo 8.1: Floating Point Numbers\n\n\n\n\n\n\nFP32 is widely adopted in many deep learning frameworks and balances accuracy and computational requirements. It is prevalent in the training phase for many neural networks due to its sufficient precision in capturing minute details during weight updates. Also known as half-precision floating point, FP16 uses 16 bits to represent a number, including its sign, exponent, and fraction. It offers a good balance between precision and memory savings. FP16 is particularly popular in deep learning training on GPUs that support mixed-precision arithmetic, combining the speed benefits of FP16 with the precision of FP32 where needed.\nSeveral other numerical formats fall into an exotic class. An exotic example is BF16 or Brain Floating Point. It is a 16-bit numerical format designed explicitly for deep learning applications. It is a compromise between FP32 and FP16, retaining the 8-bit exponent from FP32 while reducing the mantissa to 7 bits (as compared to FP32’s 23-bit mantissa). This structure prioritizes range over precision. BF16 has achieved training results comparable in accuracy to FP32 while using significantly less memory and computational resources (Kalamkar et al. 2019). This makes it suitable not just for inference but also for training deep neural networks.\n\nKalamkar, Dhiraj, Dheevatsa Mudigere, Naveen Mellempudi, Dipankar Das, Kunal Banerjee, Sasikanth Avancha, Dharma Teja Vooturi, et al. 2019. “A Study of BFLOAT16 for Deep Learning Training.” https://arxiv.org/abs/1905.12322.\nBy retaining the 8-bit exponent of FP32, BF16 offers a similar range, which is crucial for deep learning tasks where certain operations can result in very large or very small numbers. At the same time, by truncating precision, BF16 allows for reduced memory and computational requirements compared to FP32. BF16 has emerged as a promising middle ground in the landscape of numerical formats for deep learning, providing an efficient and effective alternative to the more traditional FP32 and FP16 formats.\nFigure 8.5 shows three different floating-point formats: Float32, Float16, and BFloat16.\n\n\n\n\n\n\nFigure 8.5: Three floating-point formats.\n\n\n\nInteger: These are integer representations using 8, 4, and 2 bits. They are often used during the inference phase of neural networks, where the weights and activations of the model are quantized to these lower precisions. Integer representations are deterministic and offer significant speed and memory advantages over floating-point representations. For many inference tasks, especially on edge devices, the slight loss in accuracy due to quantization is often acceptable, given the efficiency gains. An extreme form of integer numerics is for binary neural networks (BNNs), where weights and activations are constrained to one of two values: +1 or -1.\nVariable bit widths: Beyond the standard widths, research is ongoing into extremely low bit-width numerics, even down to binary or ternary representations. Extremely low bit-width operations can offer significant speedups and further reduce power consumption. While challenges remain in maintaining model accuracy with such drastic quantization, advances continue to be made in this area.\nEfficient numerics is not just about reducing the bit-width of numbers but understanding the trade-offs between accuracy and efficiency. As machine learning models become more pervasive, especially in real-world, resource-constrained environments, the focus on efficient numerics will continue to grow. By thoughtfully selecting and leveraging the appropriate numeric precision, one can achieve robust model performance while optimizing for speed, memory, and energy. Table 8.1 summarizes these trade-offs.\n\n\n\nTable 8.1: Comparing precision levels in deep learning.\n\n\n\n\n\n\n\n\n\n\nPrecision\nPros\nCons\n\n\n\n\nFP32 (Floating Point 32-bit)\n\nStandard precision used in most deep learning frameworks.\nHigh accuracy due to ample representational capacity.\nWell-suited for training\n\n\nHigh memory usage.\nSlower inference times compared to quantized models.\nHigher energy consumption.\n\n\n\nFP16 (Floating Point 16-bit)\n\nReduces memory usage compared to FP32.\nSpeeds up computations on hardware that supports FP16.\nOften used in mixed-precision training to balance speed and accuracy.\n\n\nLower representational capacity compared to FP32.\nRisk of numerical instability in some models or layers.\n\n\n\nINT8 (8-bit Integer)\n\nSignificantly reduced memory footprint compared to floating-point representations.\nFaster inference if hardware supports INT8 computations.\nSuitable for many post-training quantization scenarios.\n\n\nQuantization can lead to some accuracy loss.\nRequires careful calibration during quantization to minimize accuracy degradation.\n\n\n\nINT4 (4-bit Integer)\n\nEven lower memory usage than INT8.\nFurther speedup potential for inference.\n\n\nHigher risk of accuracy loss compared to INT8.\nCalibration during quantization becomes more critical.\n\n\n\nBinary\n\nMinimal memory footprint (only 1 bit per parameter).\nExtremely fast inference due to bitwise operations.\nPower efficient.\n\n\nSignificant accuracy drop for many tasks.\nComplex training dynamics due to extreme quantization.\n\n\n\nTernary\n\nLow memory usage but slightly more than binary.\nOffers a middle ground between representation and efficiency.\n\n\nAccuracy might still be lower than that of higher precision models.\nTraining dynamics can be complex.\n\n\n\n\n\n\n\n\n\n8.6.2 Efficiency Benefits\nNumerical efficiency matters for machine learning workloads for several reasons:\nComputational Efficiency : High-precision computations (like FP32 or FP64) can be slow and resource-intensive. Reducing numeric precision can achieve faster computation times, especially on specialized hardware that supports lower precision.\nMemory Efficiency: Storage requirements decrease with reduced numeric precision. For instance, FP16 requires half the memory of FP32. This is crucial when deploying models to edge devices with limited memory or working with large models.\nPower Efficiency: Lower precision computations often consume less power, which is especially important for battery-operated devices.\nNoise Introduction: Interestingly, the noise introduced using lower precision can sometimes act as a regularizer, helping to prevent overfitting in some models.\nHardware Acceleration: Many modern AI accelerators and GPUs are optimized for lower precision operations, leveraging the efficiency benefits of such numerics.", @@ -950,7 +950,7 @@ "href": "contents/optimizations/optimizations.html#sec-model_ops_representation", "title": "9  Model Optimizations", "section": "9.2 Efficient Model Representation", - "text": "9.2 Efficient Model Representation\nThe first avenue of attack for model optimization starts in familiar territory for most ML practitioners: efficient model representation is often first tackled at the highest level of parametrization abstraction - the model’s architecture itself.\nMost traditional ML practitioners design models with a general high-level objective in mind, whether it be image classification, person detection, or keyword spotting as mentioned previously in this textbook. Their designs generally end up naturally fitting into some soft constraints due to limited compute resources during development, but generally these designs are not aware of later constraints, such as those required if the model is to be deployed on a more constrained device instead of the cloud.\nIn this section, we’ll discuss how practitioners can harness principles of hardware-software co-design even at a model’s high level architecture to make their models compatible with edge devices. From most to least hardware aware at this level of modification, we discuss several of the most common strategies for efficient model parametrization: pruning, model compression, and edge-friendly model architectures. You were introduced to pruning and model compression in Section 8.4; now, this section will go one step beyond the definitions to provide you with a technical understanding of how these techniques work.\n\n9.2.1 Pruning\n\nOverview\nModel pruning is a technique in machine learning that aims to reduce the size and complexity of a neural network model while maintaining its predictive capabilities as much as possible. The goal of model pruning is to remove redundant or non-essential components of the model, including connections between neurons, individual neurons, or even entire layers of the network.\nThis process typically involves analyzing the machine learning model to identify and remove weights, nodes, or layers that have little impact on the model’s outputs. By selectively pruning a model in this way, the total number of parameters can be reduced significantly without substantial declines in model accuracy. The resulting compressed model requires less memory and computational resources to train and run while enabling faster inference times.\nModel pruning is especially useful when deploying machine learning models to devices with limited compute resources, such as mobile phones or TinyML systems. The technique facilitates the deployment of larger, more complex models on these devices by reducing their resource demands. Additionally, smaller models require less data to generalize well and are less prone to overfitting. By providing an efficient way to simplify models, model pruning has become a vital technique for optimizing neural networks in machine learning.\nThere are several common pruning techniques used in machine learning, these include structured pruning, unstructured pruning, iterative pruning, bayesian pruning, and even random pruning. In addition to pruning the weights, one can also prune the activations. Activation pruning specifically targets neurons or filters that activate rarely or have overall low activation. There are numerous other methods, such as sensitivity and movement pruning. For a comprehensive list of methods, the reader is encouraged to read the following paper: “A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations” (2023).\nSo how does one choose the type of pruning methods? Many variations of pruning techniques exist where each varies the heuristic of what should be kept and pruned from the model as well as number of times pruning occurs. Traditionally, pruning happens after the model is fully trained, where the pruned model may experience mild accuracy loss. However, as we will discuss further, recent discoveries have found that pruning can be used during training (i.e., iteratively) to identify more efficient and accurate model representations.\n\n\nStructured Pruning\nWe start with structured pruning, a technique that reduces the size of a neural network by eliminating entire model-specific substructures while maintaining the overall model structure. It removes entire neurons/channels or layers based on importance criteria. For example, for a convolutional neural network (CNN), this could be certain filter instances or channels. For fully connected networks, this could be neurons themselves while maintaining full connectivity or even be elimination of entire model layers that are deemed to be insignificant. This type of pruning often leads to regular, structured sparse networks that are hardware friendly.\nBest practices have started to emerge on how to think about structured pruning. There are three main components:\n\n1. Structures to Target for Pruning\nGiven the variety of approaches, different structures within a neural network are pruned based on specific criteria. The primary structures for pruning include neurons, channels, and sometimes entire layers, each with its unique implications and methodologies. The goal in each approach is to ensure that the reduced model retains as much of the original model’s predictive prowess as possible while improving computational efficiency and reducing size.\nWhen neurons are pruned, we are removing entire neurons along with their associated weights and biases, thereby reducing the width of the layer. This type of pruning is often utilized in fully connected layers.\nWith channel pruning, which is predominantly applied in convolutional neural networks (CNNs), it involves eliminating entire channels or filters, which in turn reduces the depth of the feature maps and impacts the network’s ability to extract certain features from the input data. This is particularly crucial in image processing tasks where computational efficiency is paramount.\nFinally, layer pruning takes a more aggressive approach by removing entire layers of the network. This significantly reduces the network’s depth and thereby its capacity to model complex patterns and hierarchies in the data. This approach necessitates a careful balance to ensure that the model’s predictive capability is not unduly compromised.\nFigure 9.2 demonstrates the difference between channel/filter wise pruning and layer pruning. When we prune a channel, we have to reconfigure the model’s architecture in order to adapt to the structural changes. One adjustment is changing the number of input channels in the subsequent layer (here, the third and deepest layer): changing the depths of the filters that are applied to the layer with the pruned channel. On the other hand, pruning an entire layer (removing all the channels in the layer) requires more drastic adjustments. The main one involves modifying the connections between the remaining layers to replace or bypass the pruned layer. In our case, we reconfigure to connect the first and last layers. In all pruning cases, we have to fine-tune the new structure to adjust the weights.\n\n\n\n\n\n\nFigure 9.2: Channel vs layer pruning.\n\n\n\n\n\n2. Establishing a Criteria for Pruning\nEstablishing well-defined criteria for determining which specific structures to prune from a neural network model is a crucial component of the model pruning process. The core goal here is to identify and remove components that contribute the least to the model’s predictive capabilities, while retaining structures integral to preserving the model’s accuracy.\nA widely adopted and effective strategy for systematically pruning structures relies on computing importance scores for individual components like neurons, filters, channels or layers. These scores serve as quantitative metrics to gauge the significance of each structure and its effect on the model’s output.\nThere are several techniques for assigning these importance scores:\n\nWeight Magnitude-Based Pruning: This approach assigns importance scores to a structure by evaluating the aggregate magnitude of their associated weights. Structures with smaller overall weight magnitudes are considered less critical to the network’s performance.\nGradient-Based Pruning: This technique utilizes the gradients of the loss function with respect to the weights associated with a structure. Structures with low cumulative gradient magnitudes, indicating minimal impact on the loss when altered, are prime candidates for pruning.\nActivation-Based Pruning: This method tracks how often a neuron or filter is activated by storing this information in a parameter called the activation counter. Each time the structure is activated, the counter is incremented. A low activation count suggests that the structure is less relevant.\nTaylor Expansion-Based Pruning: This approach approximates the change in the loss function from removing a given weight. By assessing the cumulative loss disturbance from removing all the weights associated with a structure, you can identify structures with negligible impact on the loss, making them suitable candidates for pruning.\n\nThe idea is to measure, either directly or indirectly, the contribution of each component to the model’s output. Structures with minimal influence according to the defined criteria are pruned first. This enables selective, optimized pruning that maximally compresses models while preserving predictive capacity. In general, it is important to evaluate the impact of removing particular structures on the model’s output, with recent works such as (Rachwan et al. 2022) and (Lubana and Dick 2020) investigating combinations of techniques like magnitude-based pruning and gradient-based pruning.\n\nRachwan, John, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, and Stephan Günnemann. 2022. “Winning the Lottery Ahead of Time: Efficient Early Network Pruning.” In International Conference on Machine Learning, 18293–309. PMLR.\n\nLubana, Ekdeep Singh, and Robert P Dick. 2020. “A Gradient Flow Framework for Analyzing Network Pruning.” arXiv Preprint arXiv:2009.11839.\n\n\n3. Selecting a pruning strategy\nNow that you understand some techniques for determining the importance of structures within a neural network, the next step is to decide how to apply these insights. This involves selecting an appropriate pruning strategy, which dictates how and when the identified structures are removed and how the model is fine-tuned to maintain its performance. Two main structured pruning strategies exist: iterative pruning and one-shot pruning.\nIterative pruning gradually removes structures across multiple cycles of pruning followed by fine-tuning. In each cycle, a small set of structures are pruned based on importance criteria. The model is then fine-tuned, allowing it to adjust smoothly to the structural changes before the next pruning iteration. This gradual, cyclic approach prevents abrupt accuracy drops. It allows the model to slowly adapt as structures are reduced across iterations.\nConsider a situation where we wish to prune the 6 least effective channels (based on some specific criteria) from a convolutional neural network. In Figure 9.3, we show a simplified pruning process carried over 3 iterations. In every iteration, we only prune 2 channels. Removing the channels results in accuracy degradation. In the first iteration, the accuracy drops from 0.995 to 0.971. However, after we fine-tune the model on the new structure, we are able to recover from the performance loss, bringing the accuracy up to 0.992. Since the structural changes are minor and gradual, the network can more easily adapt to them. Running the same process 2 more times, we end up with a final accuracy of 0.991 (a loss of only 0.4% from the original) and 27% decrease in the number of channels. Thus, iterative pruning enables us to maintain performance while benefiting from increased computational efficiency due to the decreased model size.\n\n\n\n\n\n\nFigure 9.3: Iterative pruning.\n\n\n\nOne-shot pruning takes a more aggressive approach by pruning a large portion of structures simultaneously in one shot based on predefined importance criteria. This is followed by extensive fine-tuning to recover model accuracy. While faster, this aggressive strategy can degrade accuracy if the model cannot recover during fine-tuning.\nThe choice between these strategies involves weighing factors like model size, target sparsity level, available compute and acceptable accuracy losses. One-shot pruning can rapidly compress models, but iterative pruning may enable better accuracy retention for a target level of pruning. In practice, the strategy is tailored based on use case constraints. The overarching aim is to generate an optimal strategy that removes redundancy, achieves efficiency gains through pruning, and finely tunes the model to stabilize accuracy at an acceptable level for deployment.\nNow consider the same network we had in the iterative pruning example. Whereas in the iterative process we pruned 2 channels at a time, in the one-shot pruning we would prune the 6 channels at once (Figure 9.4). Removing 27% of the network’s channel simultaneously alters the structure significantly, causing the accuracy to drop from 0.995 to 0.914. Given the major changes, the network is not able to properly adapt during fine-tuning, and the accuracy went up to 0.943, a 5% degradation from the accuracy of the unpruned network. While the final structures in both iterative pruning and oneshot pruning processes are identical, the former is able to maintain high performance while the latter suffers significant degradations.\n\n\n\n\n\n\nFigure 9.4: One-shot pruning.\n\n\n\n\n\n\nAdvantages of Structured Pruning\nStructured pruning brings forth a myriad of advantages that cater to various facets of model deployment and utilization, especially in environments where computational resources are constrained.\n\nComputational Efficiency: By eliminating entire structures, such as neurons or channels, structured pruning significantly diminishes the computational load during both training and inference phases, thereby enabling faster model predictions and training convergence. Moreover, the removal of structures inherently reduces the model’s memory footprint, ensuring that it demands less storage and memory during operation, which is particularly beneficial in memory-constrained environments like TinyML systems.\nHardware Efficiency: Structured pruning often results in models that are more amenable to deployment on specialized hardware, such as Field-Programmable Gate Arrays (FPGAs) or Application-Specific Integrated Circuits (ASICs), due to the regularity and simplicity of the pruned architecture. With reduced computational requirements, it translates to lower energy consumption, which is crucial for battery-powered devices and sustainable computing practices.\nMaintenance and Deployment: The pruned model, while smaller, retains its original architectural form, which can simplify the deployment pipeline and ensure compatibility with existing systems and frameworks. Also, with fewer parameters and simpler structures, the pruned model becomes easier to manage and monitor in production environments, potentially reducing the overhead associated with model maintenance and updates. Later on, when we dive into MLOps, this need will become apparent.\n\n\n\nUnstructured Pruning\nUnstructured pruning is, as its name suggests, pruning the model without regard to model-specific substructure. As mentioned above, it offers a greater aggression in pruning and can achieve higher model sparsities while maintaining accuracy given less constraints on what can and can’t be pruned. Generally, post-training unstructured pruning consists of an importance criterion for individual model parameters/weights, pruning/removal of weights that fall below the criteria, and optional fine-tuning after to try and recover the accuracy lost during weight removal.\nUnstructured pruning has some advantages over structured pruning: removing individual weights instead of entire model substructures often leads in practice to lower model accuracy decreases. Furthermore, generally determining the criterion of importance for an individual weight is much simpler than for an entire substructure of parameters in structured pruning, making the former preferable for cases where that overhead is hard or unclear to compute. Similarly, the actual process of structured pruning is generally less flexible, as removing individual weights is generally simpler than removing entire substructures and ensuring the model still works.\nUnstructured pruning, while offering the potential for significant model size reduction and enhanced deployability, brings with it challenges related to managing sparse representations and ensuring computational efficiency. It is particularly useful in scenarios where achieving the highest possible model compression is paramount and where the deployment environment can handle sparse computations efficiently.\nTable 9.1 provides a concise comparison between structured and unstructured pruning. In this table, aspects related to the nature and architecture of the pruned model (Definition, Model Regularity, and Compression Level) are grouped together, followed by aspects related to computational considerations (Computational Efficiency and Hardware Compatibility), and ending with aspects related to the implementation and adaptation of the pruned model (Implementation Complexity and Fine-Tuning Complexity). Both pruning strategies offer unique advantages and challenges, as shown in Table 9.1, and the selection between them should be influenced by specific project and deployment requirements.\n\n\n\nTable 9.1: Comparison of structured versus unstructured pruning.\n\n\n\n\n\n\n\n\n\n\nAspect\nStructured Pruning\nUnstructured Pruning\n\n\n\n\nDefinition\nPruning entire structures (e.g., neurons, channels, layers) within the network\nPruning individual weights or neurons, resulting in sparse matrices or non-regular network structures\n\n\nModel Regularity\nMaintains a regular, structured network architecture\nResults in irregular, sparse network architectures\n\n\nCompression Level\nMay offer limited model compression compared to unstructured pruning\nCan achieve higher model compression due to fine-grained pruning\n\n\nComputational Efficiency\nTypically more computationally efficient due to maintaining regular structures\nCan be computationally inefficient due to sparse weight matrices, unless specialized hardware/software is used\n\n\nHardware Compatibility\nGenerally better compatible with various hardware due to regular structures\nMay require hardware that efficiently handles sparse computations to realize benefits\n\n\nImplementation Complexity\nOften simpler to implement and manage due to maintaining network structure\nCan be complex to manage and compute due to sparse representations\n\n\nFine-Tuning Complexity\nMay require less complex fine-tuning strategies post-pruning\nMight necessitate more complex retraining or fine-tuning strategies post-pruning\n\n\n\n\n\n\nIn Figure 9.5 we have examples that illustrate the differences between unstructured and structured pruning. Observe that unstructured pruning can lead to models that no longer obey high-level structural guarantees of their original unpruned counterparts: the left network is no longer a fully connected network after pruning. Structured pruning on the other hand maintains those invariants: in the middle, the fully connected network is pruned in a way that the pruned network is still fully connected; likewise, the CNN maintains its convolutional structure, albeit with fewer filters.\n\n\n\n\n\n\nFigure 9.5: Unstructured vs structured pruning. Source: Qi et al. (2021).\n\n\nQi, Chen, Shibo Shen, Rongpeng Li, Zhifeng Zhao, Qing Liu, Jing Liang, and Honggang Zhang. 2021. “An Efficient Pruning Scheme of Deep Neural Networks for Internet of Things Applications.” EURASIP Journal on Advances in Signal Processing 2021 (1): 31. https://doi.org/10.1186/s13634-021-00744-4.\n\n\n\n\nLottery Ticket Hypothesis\nPruning has evolved from a purely post-training technique that came at the cost of some accuracy, to a powerful meta-learning approach applied during training to reduce model complexity. This advancement in turn improves compute, memory, and latency efficiency at both training and inference.\nA breakthrough finding that catalyzed this evolution was the lottery ticket hypothesis by Frankle and Carbin (2019). Their work states that within dense neural networks, there exist sparse subnetworks, referred to as “winning tickets,” that can match or even exceed the performance of the original model when trained in isolation. Specifically, these winning tickets, when initialized using the same weights as the original network, can achieve similarly high training convergence and accuracy on a given task. It is worthwhile pointing out that they empirically discovered the lottery ticket hypothesis, which was later formalized.\n\nFrankle, Jonathan, and Michael Carbin. 2019. “The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.” In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https://openreview.net/forum?id=rJl-b3RcF7.\nThe intuition behind this hypothesis is that, during the training process of a neural network, many neurons and connections become redundant or unimportant, particularly with the inclusion of training techniques encouraging redundancy like dropout. Identifying, pruning out, and initializing these “winning tickets’’ allows for faster training and more efficient models, as they contain the essential model decision information for the task. Furthermore, as generally known with the bias-variance tradeoff theory, these tickets suffer less from overparameterization and thus generalize better rather than overfitting to the task.\nIn Figure 9.6 we have an example experiment showing pruning and training experiments on a fully connected LeNet over a variety of pruning ratios. In the left plot, notice how heavy pruning reveals a more efficient subnetwork (in green) that is 21.1% the size of the original network (in blue), The subnetwork achieves higher accuracy and in a faster manner than the unpruned version (green line is above the blue line). However, pruning has a limit (sweet spot), and further pruning will produce performance degradations and eventually drop below the unpruned version’s performance (notice how the red, purple, and brown subnetworks gradually drop in accuracy performance) due to the significant loss in the number of parameters.\n\n\n\n\n\n\nFigure 9.6: Lottery ticket hypothesis experiments.\n\n\n\nTo uncover these winning lottery tickets within a neural network, a systematic process is followed. This process, which is illustrated in Figure 9.7 (left side), involves iteratively training, pruning, and reinitializing the network. The steps below outline this approach:\n\nInitialize the network’s weights to random values.\nTrain the network until it converges to the desired performance.\nPrune out some percentage of the edges with the lowest weight values.\nReinitialize the network with the same random values from step 1.\nRepeat steps 2-4 for a number of times, or as long as the accuracy doesn’t significantly degrade.\n\nWhen we finish, we are left with a pruned network (Figure 9.7 right side), which is a subnetwork of the one we start with. The subnetwork should have a significantly smaller structure, while maintaining a comparable level of accuracy.\n\n\n\n\n\n\nFigure 9.7: Finding the winning ticket subnetwork.\n\n\n\n\n\nChallenges & Limitations\nThere is no free lunch with pruning optimizations, with some choices coming with both improvements and costs to considers. Below we discuss some tradeoffs for practitioners to consider.\n\nManaging Sparse Weight Matrices: A sparse weight matrix is a matrix in which many of the elements are zero. Unstructured pruning often results in sparse weight matrices, where many weights are pruned to zero. While this reduces model size, it also introduces several challenges. Computational inefficiency can arise because standard hardware is optimized for dense matrix operations. Without optimizations that take advantage of sparsity, the computational savings from pruning can be lost. Although sparse matrices can be stored without specialized formats, effectively leveraging their sparsity requires careful handling to avoid wasting resources. Algorithmically, navigating sparse structures requires efficiently skipping over zero entries, which adds complexity to the computation and model updates.\nQuality vs. Size Reduction: A key challenge in both structured and unstructured pruning is balancing size reduction with maintaining or improving predictive performance. Establishing robust pruning criteria, whether for removing entire structures (structured pruning) or individual weights (unstructured pruning), is essential. These pruning criteria chosen must accurately identify elements whose removal minimally impacts performance. Careful experimentation is often needed to ensure the pruned model remains efficient while maintaining its predictive performance.\nFine-Tuning and Retraining: Post-pruning fine-tuning is imperative in both structured and unstructured pruning to recover lost performance and stabilize the model. The challenge encompasses determining the extent, duration, and nature of the fine-tuning process, which can be influenced by the pruning method and the degree of pruning applied.\nHardware Compatibility and Efficiency: Especially pertinent to unstructured pruning, hardware compatibility and efficiency become critical. Unstructured pruning often results in sparse weight matrices, which may not be efficiently handled by certain hardware, potentially negating the computational benefits of pruning (see Figure 9.8). Ensuring that pruned models, particularly those resulting from unstructured pruning, are scalable, compatible, and efficient on the target hardware is a significant consideration.\nLegal and Ethical Considerations: Last but not least, adherence to legal and ethical guidelines is important, especially in domains with significant consequences. Pruning methods must undergo rigorous validation, testing, and potentially certification processes to ensure compliance with relevant regulations and standards, though arguably at this time no such formal standards and best practices exist that are vetted and validated by 3rd party entities. This is particularly crucial in high-stakes applications like medical AI and autonomous driving, where quality drops due to pruning-like optimizations can be life-threatening. Moreover, ethical considerations extend beyond safety to fairness and equality; recent work by (Tran et al. 2022) has revealed that pruning can disproportionately impact people of color, underscoring the need for comprehensive ethical evaluation in the pruning process.\n\n\nTran, Cuong, Ferdinando Fioretto, Jung-Eun Kim, and Rakshit Naidu. 2022. “Pruning Has a Disparate Impact on Model Accuracy.” Adv Neural Inf Process Syst 35: 17652–64.\n\n\n\n\n\n\nFigure 9.8: Sparse weight matrix.\n\n\n\n\n\n\n\n\n\nExercise 9.1: Pruning\n\n\n\n\n\nImagine your neural network is a giant, overgrown bush. Pruning is like strategically trimming away branches to make it stronger and more efficient! In the Colab, you’ll learn how to do this trimming in TensorFlow. Understanding these concepts will give you the foundation to see how pruning makes models small enough to run on your phone!\n\n\n\n\n\n\n\n9.2.2 Model Compression\nModel compression techniques are crucial for deploying deep learning models on resource-constrained devices. These techniques aim to create smaller, more efficient models that preserve the predictive performance of the original models.\n\nKnowledge Distillation\nOne popular technique is knowledge distillation (KD), which transfers knowledge from a large, complex “teacher” model to a smaller “student” model. The key idea is to train the student model to mimic the teacher’s outputs. The concept of KD was first popularized by Hinton (2005).\n\nHinton, Geoffrey. 2005. “Van Nostrand’s Scientific Encyclopedia.” Wiley. https://doi.org/10.1002/0471743984.vse0673.\n\nOverview and Benefits\nKnowledge distillation involves transferring knowledge from a large, complex teacher model to a smaller student model. The core idea is to use the teacher’s outputs, known as soft targets, to guide the training of the student model. Unlike traditional “hard targets” (the true labels), soft targets are the probability distributions over classes that the teacher model predicts. These distributions provide richer information about the relationships between classes, which can help the student model learn more effectively.\nYou have learned that the softmax function converts a model’s raw outputs into a probability distribution over classes. A key technique in KD is temperature scaling, which is applied to the softmax function of the teacher model’s outputs. By introducing a temperature parameter, the distribution can be adjusted: a higher temperature produces softer probabilities, meaning the differences between class probabilities become less extreme. This softening effect results in a more uniform distribution, where the model’s confidence in the most likely class is reduced, and other classes have higher, non-zero probabilities. This is valuable for the student model because it allows it to learn not just from the most likely class but from the relative probabilities of all classes, capturing subtle patterns that might be missed if trained only on hard targets. Thus, temperature scaling facilitates the transfer of more nuanced knowledge from the teacher to the student model.\nThe loss function in knowledge distillation typically combines two components: a distillation loss and a classification loss. The distillation loss, often calculated using Kullback-Leibler (KL) divergence, measures the difference between the soft targets produced by the teacher model and the outputs of the student model, encouraging the student to mimic the teacher’s predictions. Meanwhile, the classification loss ensures that the student model correctly predicts the true labels based on the original data. Together, these two components help the student model retain the knowledge of the teacher while adhering to the ground truth labels.\nThese components, when adeptly configured and harmonized, enable the student model to assimilate the teacher model’s knowledge, crafting a pathway towards efficient and robust smaller models that retain the predictive prowess of their larger counterparts. Figure 9.9 visualizes the training procedure of knowledge distillation. Note how the logits or soft labels of the teacher model are used to provide a distillation loss for the student model to learn from.\n\n\n\n\n\n\nFigure 9.9: Knowledge distillation training process. Source: IntelLabs (2023).\n\n\nIntelLabs. 2023. “Knowledge Distillation - Neural Network Distiller.” https://intellabs.github.io/distiller/knowledge_distillation.html.\n\n\n\n\nChallenges\nHowever, KD has a unique set of challenges and considerations that researchers and practitioners must attentively address. One of the challenges is in the meticulous tuning of hyperparameters, such as the temperature parameter in the softmax function and the weighting between the distillation and classification loss in the objective function. Striking a balance that effectively leverages the softened outputs of the teacher model while maintaining fidelity to the true data labels is non-trivial and can significantly impact the student model’s performance and generalization capabilities.\nFurthermore, the architecture of the student model itself poses a considerable challenge. Designing a model that is compact to meet computational and memory constraints, while still being capable of assimilating the essential knowledge from the teacher model, demands a nuanced understanding of model capacity and the inherent trade-offs involved in compression. The student model must be carefully architected to navigate the dichotomy of size and performance, ensuring that the distilled knowledge is meaningfully captured and utilized. Moreover, the choice of teacher model, which inherently influences the quality and nature of the knowledge to be transferred, is important and it introduces an added layer of complexity to the KD process.\nThese challenges underscore the necessity for a thorough and nuanced approach to implementing KD, ensuring that the resultant student models are both efficient and effective in their operational contexts.\n\n\n\nLow-rank Matrix Factorization\nSimilar in approximation theme, low-rank matrix factorization (LRMF) is a mathematical technique used in linear algebra and data analysis to approximate a given matrix by decomposing it into two or more lower-dimensional matrices. The fundamental idea is to express a high-dimensional matrix as a product of lower-rank matrices, which can help reduce the complexity of data while preserving its essential structure. Mathematically, given a matrix \\(A \\in \\mathbb{R}^{m \\times n}\\), LRMF seeks matrices \\(U \\in \\mathbb{R}^{m \\times k}\\) and \\(V \\in \\mathbb{R}^{k \\times n}\\) such that \\(A \\approx UV\\), where \\(k\\) is the rank and is typically much smaller than \\(m\\) and \\(n\\).\n\nBackground and Benefits\nOne of the seminal works in the realm of matrix factorization, particularly in the context of recommendation systems, is the paper by Koren, Bell, and Volinsky (2009). The authors look into various factorization models, providing insights into their efficacy in capturing the underlying patterns in the data and enhancing predictive accuracy in collaborative filtering. LRMF has been widely applied in recommendation systems (such as Netflix, Facebook, etc.), where the user-item interaction matrix is factorized to capture latent factors corresponding to user preferences and item attributes.\n\nKoren, Yehuda, Robert Bell, and Chris Volinsky. 2009. “Matrix Factorization Techniques for Recommender Systems.” Computer 42 (8): 30–37. https://doi.org/10.1109/mc.2009.263.\nThe main advantage of low-rank matrix factorization lies in its ability to reduce data dimensionality as shown in Figure 9.10, where there are fewer parameters to store, making it computationally more efficient and reducing storage requirements at the cost of some additional compute. This can lead to faster computations and more compact data representations, which is especially valuable when dealing with large datasets. Additionally, it may aid in noise reduction and can reveal underlying patterns and relationships in the data.\nFigure 9.10 illustrates the decrease in parameterization enabled by low-rank matrix factorization. Observe how the matrix \\(M\\) can be approximated by the product of matrices \\(L_k\\) and \\(R_k^T\\). For intuition, most fully connected layers in networks are stored as a projection matrix \\(M\\), which requires \\(m \\times n\\) parameter to be loaded on computation. However, by decomposing and approximating it as the product of two lower rank matrices, we thus only need to store \\(m \\times k + k\\times n\\) parameters in terms of storage while incurring an additional compute cost of the matrix multiplication. So long as \\(k < n/2\\), this factorization has fewer parameters total to store while adding a computation of runtime \\(O(mkn)\\) (Gu 2023).\n\nGu, Ivy. 2023. “Deep Learning Model Compression (Ii) by Ivy Gu Medium.” https://ivygdy.medium.com/deep-learning-model-compression-ii-546352ea9453.\n\n\n\n\n\n\nFigure 9.10: Low matrix factorization. Source: The Clever Machine.\n\n\n\n\n\nChallenges\nBut practitioners and researchers encounter a spectrum of challenges and considerations that necessitate careful attention and strategic approaches. As with any lossy compression technique, we may lose information during this approximation process: choosing the correct rank that balances the information lost and the computational costs is tricky as well and adds an additional hyper-parameter to tune for.\nLow-rank matrix factorization is a valuable tool for dimensionality reduction and making compute fit onto edge devices but, like other techniques, needs to be carefully tuned to the model and task at hand. A key challenge resides in managing the computational complexity inherent to LRMF, especially when grappling with high-dimensional and large-scale data. The computational burden, particularly in the context of real-time applications and massive datasets, remains a significant hurdle for effectively using LRMF.\nMoreover, the conundrum of choosing the optimal rank \\(k\\), for the factorization introduces another layer of complexity. The selection of \\(k\\) inherently involves a trade-off between approximation accuracy and model simplicity, and identifying a rank that adeptly balances these conflicting objectives often demands a combination of domain expertise, empirical validation, and sometimes, heuristic approaches. The challenge is further amplified when the data encompasses noise or when the inherent low-rank structure is not pronounced, making the determination of a suitable \\(k\\) even more elusive.\nHandling missing or sparse data, a common occurrence in applications like recommendation systems, poses another substantial challenge. Traditional matrix factorization techniques, such as Singular Value Decomposition (SVD), are not directly applicable to matrices with missing entries, necessitating the development and application of specialized algorithms that can factorize incomplete matrices while mitigating the risks of overfitting to the observed entries. This often involves incorporating regularization terms or constraining the factorization in specific ways, which in turn introduces additional hyperparameters that need to be judiciously selected.\nFurthermore, in scenarios where data evolves or grows over time, developing LRMF models that can adapt to new data without necessitating a complete re-factorization is a critical yet challenging endeavor. Online and incremental matrix factorization algorithms seek to address this by enabling the update of factorized matrices as new data arrives, yet ensuring stability, accuracy, and computational efficiency in these dynamic settings remains an intricate task. This is particularly challenging in the space of TinyML, where edge redeployment for refreshed models can be quite challenging.\n\n\n\nTensor Decomposition\nYou have learned in Section 6.4.1 that tensors are flexible structures, commonly used by ML Frameworks, that can represent data in higher dimensions. Similar to low-rank matrix factorization, more complex models may store weights in higher dimensions, such as tensors. Tensor decomposition is the higher-dimensional analogue of matrix factorization, where a model tensor is decomposed into lower rank components (see Figure 9.11). These lower-rank components are easier to compute on and store but may suffer from the same issues mentioned above, such as information loss and the need for nuanced hyperparameter tuning. Mathematically, given a tensor \\(\\mathcal{A}\\), tensor decomposition seeks to represent \\(\\mathcal{A}\\) as a combination of simpler tensors, facilitating a compressed representation that approximates the original data while minimizing the loss of information.\nThe work of Tamara G. Kolda and Brett W. Bader, “Tensor Decompositions and Applications” (2009), stands out as a seminal paper in the field of tensor decompositions. The authors provide a comprehensive overview of various tensor decomposition methods, exploring their mathematical underpinnings, algorithms, and a wide array of applications, ranging from signal processing to data mining. Of course, the reason we are discussing it is because it has huge potential for system performance improvements, particularly in the space of TinyML, where throughput and memory footprint savings are crucial to feasibility of deployments.\n\n\n\n\n\n\nFigure 9.11: Tensor decomposition. Source: Xinyu (n.d.).\n\n\nXinyu, Chen. n.d.\n\n\n\n\n\n\n\n\nExercise 9.2: Scalable Model Compression with TensorFlow\n\n\n\n\n\nThis Colab dives into a technique for compressing models while maintaining high accuracy. The key idea is to train a model with an extra penalty term that encourages the model to be more compressible. Then, the model is encoded using a special coding scheme that aligns with this penalty. This approach allows you to achieve compressed models that perform just as well as the original models and is useful in deploying models to devices with limited resources like mobile phones and edge devices.\n\n\n\n\n\n\n\n9.2.3 Edge-Aware Model Design\nNow, we reach the other end of the hardware-software gradient, where we specifically make model architecture decisions directly given knowledge of the edge devices we wish to deploy on.\nAs covered in previous sections, edge devices are constrained specifically with limitations on memory and parallelizable computations: as such, if there are critical inference speed requirements, computations must be flexible enough to satisfy hardware constraints, something that can be designed at the model architecture level. Furthermore, trying to cram SOTA large ML models onto edge devices even after pruning and compression is generally infeasible purely due to size: the model complexity itself must be chosen with more nuance as to more feasibly fit the device. Edge ML developers have approached this architectural challenge both through designing bespoke edge ML model architectures and through device-aware neural architecture search (NAS), which can more systematically generate feasible on-device model architectures.\n\nModel Design Techniques\nOne edge friendly architecture design, commonly used in deep learning for image processing, is depthwise separable convolutions. It consists of two distinct steps: the first is the depthwise convolution, where each input channel is convolved independently with its own set of learnable filters, as shown in Figure 9.12. This step reduces computational complexity by a significant margin compared to standard convolutions, as it drastically reduces the number of parameters and computations involved. The second step is the pointwise convolution, which combines the output of the depthwise convolution channels through a 1x1 convolution, creating inter-channel interactions. This approach offers several advantages. Benefits include reduced model size, faster inference times, and often better generalization due to fewer parameters, making it suitable for mobile and embedded applications. However, depthwise separable convolutions may not capture complex spatial interactions as effectively as standard convolutions and might require more depth (layers) to achieve the same level of representational power, potentially leading to longer training times. Nonetheless, their efficiency in terms of parameters and computation makes them a popular choice in modern convolutional neural network architectures.\n\n\n\n\n\n\nFigure 9.12: Depthwise separable convolutions. Source: Hegde (2023).\n\n\nHegde, Sumant. 2023. “An Introduction to Separable Convolutions - Analytics Vidhya.” https://www.analyticsvidhya.com/blog/2021/11/an-introduction-to-separable-convolutions/.\n\n\n\n\nExample Model Architectures\nIn this vein, a number of recent architectures have been, from inception, specifically designed for maximizing accuracy on an edge deployment, notably SqueezeNet, MobileNet, and EfficientNet.\n\nSqueezeNet by Iandola et al. (2016) for instance, utilizes a compact architecture with 1x1 convolutions and fire modules to minimize the number of parameters while maintaining strong accuracy.\nMobileNet by Howard et al. (2017), on the other hand, employs the aforementioned depthwise separable convolutions to reduce both computation and model size.\nEfficientNet by Tan and Le (2023) takes a different approach by optimizing network scaling (i.e. varying the depth, width and resolution of a network) and compound scaling, a more nuanced variation network scaling, to achieve superior performance with fewer parameters.\n\n\nIandola, Forrest N, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer. 2016. “SqueezeNet: Alexnet-level Accuracy with 50x Fewer Parameters and 0.5 MB Model Size.” ArXiv Preprint abs/1602.07360. https://arxiv.org/abs/1602.07360.\n\nHoward, Andrew G., Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.” ArXiv Preprint. https://arxiv.org/abs/1704.04861.\n\nTan, Mingxing, and Quoc V. Le. 2023. “Demystifying Deep Learning.” Wiley. https://doi.org/10.1002/9781394205639.ch6.\nThese models are essential in the context of edge computing where limited processing power and memory require lightweight yet effective models that can efficiently perform tasks such as image recognition, object detection, and more. Their design principles showcase the importance of intentionally tailored model architecture for edge computing, where performance and efficiency must fit within constraints.\n\n\nStreamlining Model Architecture Search\nLastly, to address the challenge of finding efficient model architectures that are compatible with edge devices, researchers have developed systematized pipelines that streamline the search for performant designs. Two notable frameworks in this space are TinyNAS by J. Lin et al. (2020) and MorphNet by Gordon et al. (2018), which automate the process of optimizing neural network architectures for edge deployment.\n\nGordon, Ariel, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang, and Edward Choi. 2018. “MorphNet: Fast &Amp; Simple Resource-Constrained Structure Learning of Deep Networks.” In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1586–95. IEEE. https://doi.org/10.1109/cvpr.2018.00171.\nTinyNAS is an innovative neural architecture search framework introduced in the MCUNet paper, designed to efficiently discover lightweight neural network architectures for edge devices with limited computational resources. Leveraging reinforcement learning and a compact search space of micro neural modules, TinyNAS optimizes for both accuracy and latency, enabling the deployment of deep learning models on microcontrollers, IoT devices, and other resource-constrained platforms. Specifically, TinyNAS, in conjunction with a network optimizer TinyEngine, generates different search spaces by scaling the input resolution and the model width of a model, then collects the computation FLOPs distribution of satisfying networks within the search space to evaluate its priority. TinyNAS relies on the assumption that a search space that accommodates higher FLOPs under memory constraint can produce higher accuracy models, something that the authors verified in practice in their work. In empirical performance, TinyEngine reduced the peak memory usage of models by around 3.4 times and accelerated inference by 1.7 to 3.3 times compared to TFLite and CMSIS-NN.\nSimilarly, MorphNet is a neural network optimization framework designed to automatically reshape and morph the architecture of deep neural networks, optimizing them for specific deployment requirements. It achieves this through two steps: first, it leverages a set of customizable network morphing operations, such as widening or deepening layers, to dynamically adjust the network’s structure. These operations enable the network to adapt to various computational constraints, including model size, latency, and accuracy targets, which are extremely prevalent in edge computing usage. In the second step, MorphNet uses a reinforcement learning-based approach to search for the optimal permutation of morphing operations, effectively balancing the trade-off between model size and performance. This innovative method allows deep learning practitioners to automatically tailor neural network architectures to specific application and hardware requirements, ensuring efficient and effective deployment across various platforms.\nTinyNAS and MorphNet represent a few of the many significant advancements in the field of systematic neural network optimization, allowing architectures to be systematically chosen and generated to fit perfectly within problem constraints.\n\n\n\n\n\n\nExercise 9.3: Edge-Aware Model Design\n\n\n\n\n\nImagine you’re building a tiny robot that can identify different flowers. It needs to be smart, but also small and energy-efficient! In the “Edge-Aware Model Design” world, we learned about techniques like depthwise separable convolutions and architectures like SqueezeNet, MobileNet, and EfficientNet – all designed to pack intelligence into compact models. Now, let’s see these ideas in action with some xColabs:\nSqueezeNet in Action: Maybe you’d like a Colab showing how to train a SqueezeNet model on a flower image dataset. This would demonstrate its small size and how it learns to recognize patterns despite its efficiency.\n\nMobileNet Exploration: Ever wonder if those tiny image models are just as good as the big ones? Let’s find out! In this Colab, we’re pitting MobileNet, the lightweight champion, against a classic image classification model. We’ll race them for speed, measure their memory needs, and see who comes out on top for accuracy. Get ready for a battle of the image brains!", + "text": "9.2 Efficient Model Representation\nThe first avenue of attack for model optimization starts in familiar territory for most ML practitioners: efficient model representation is often first tackled at the highest level of parametrization abstraction - the model’s architecture itself.\nMost traditional ML practitioners design models with a general high-level objective in mind, whether it be image classification, person detection, or keyword spotting as mentioned previously in this textbook. Their designs generally end up naturally fitting into some soft constraints due to limited compute resources during development, but generally these designs are not aware of later constraints, such as those required if the model is to be deployed on a more constrained device instead of the cloud.\nIn this section, we’ll discuss how practitioners can harness principles of hardware-software co-design even at a model’s high level architecture to make their models compatible with edge devices. From most to least hardware aware at this level of modification, we discuss several of the most common strategies for efficient model parametrization: pruning, model compression, and edge-friendly model architectures. You were introduced to pruning and model compression in Section 8.4; now, this section will go one step beyond the definitions to provide you with a technical understanding of how these techniques work.\n\n9.2.1 Pruning\n\nOverview\nModel pruning is a technique in machine learning that reduces the size and complexity of a neural network model while maintaining its predictive capabilities as much as possible. The goal of model pruning is to remove redundant or non-essential components of the model, including connections between neurons, individual neurons, or even entire layers of the network.\nThis process typically involves analyzing the machine learning model to identify and remove weights, nodes, or layers that have little impact on the model’s outputs. By selectively pruning a model in this way, the total number of parameters can be reduced significantly without substantial declines in model accuracy. The resulting compressed model requires less memory and computational resources to train and run while enabling faster inference times.\nModel pruning is especially useful when deploying machine learning models to devices with limited compute resources, such as mobile phones or TinyML systems. The technique facilitates the deployment of larger, more complex models on these devices by reducing their resource demands. Additionally, smaller models require less data to generalize well and are less prone to overfitting. By providing an efficient way to simplify models, model pruning has become a vital technique for optimizing neural networks in machine learning.\nThere are several common pruning techniques used in machine learning, these include structured pruning, unstructured pruning, iterative pruning, bayesian pruning, and even random pruning. In addition to pruning the weights, one can also prune the activations. Activation pruning specifically targets neurons or filters that activate rarely or have overall low activation. There are numerous other methods, such as sensitivity and movement pruning. For a comprehensive list of methods, the reader is encouraged to read the following paper: “A Survey on Deep Neural Network Pruning: Taxonomy, Comparison, Analysis, and Recommendations” (2023).\nSo how does one choose the type of pruning methods? Many variations of pruning techniques exist where each varies the heuristic of what should be kept and pruned from the model as well as number of times pruning occurs. Traditionally, pruning happens after the model is fully trained, where the pruned model may experience mild accuracy loss. However, as we will discuss further, recent discoveries have found that pruning can be used during training (i.e., iteratively) to identify more efficient and accurate model representations.\n\n\nStructured Pruning\nWe start with structured pruning, a technique that reduces the size of a neural network by eliminating entire model-specific substructures while maintaining the overall model structure. It removes entire neurons/channels or layers based on importance criteria. For example, for a convolutional neural network (CNN), this could be certain filter instances or channels. For fully connected networks, this could be neurons themselves while maintaining full connectivity or even be elimination of entire model layers that are deemed to be insignificant. This type of pruning often leads to regular, structured sparse networks that are hardware friendly.\nBest practices have started to emerge on how to think about structured pruning. There are three main components:\n\n1. Structures to Target for Pruning\nGiven the variety of approaches, different structures within a neural network are pruned based on specific criteria. The primary structures for pruning include neurons, channels, and sometimes entire layers, each with its unique implications and methodologies. The goal in each approach is to ensure that the reduced model retains as much of the original model’s predictive prowess as possible while improving computational efficiency and reducing size.\nWhen neurons are pruned, we are removing entire neurons along with their associated weights and biases, thereby reducing the width of the layer. This type of pruning is often utilized in fully connected layers.\nWith channel pruning, which is predominantly applied in convolutional neural networks (CNNs), it involves eliminating entire channels or filters, which in turn reduces the depth of the feature maps and impacts the network’s ability to extract certain features from the input data. This is particularly crucial in image processing tasks where computational efficiency is paramount.\nFinally, layer pruning takes a more aggressive approach by removing entire layers of the network. This significantly reduces the network’s depth and thereby its capacity to model complex patterns and hierarchies in the data. This approach necessitates a careful balance to ensure that the model’s predictive capability is not unduly compromised.\nFigure 9.2 demonstrates the difference between channel/filter wise pruning and layer pruning. When we prune a channel, we have to reconfigure the model’s architecture in order to adapt to the structural changes. One adjustment is changing the number of input channels in the subsequent layer (here, the third and deepest layer): changing the depths of the filters that are applied to the layer with the pruned channel. On the other hand, pruning an entire layer (removing all the channels in the layer) requires more drastic adjustments. The main one involves modifying the connections between the remaining layers to replace or bypass the pruned layer. In our case, we reconfigure to connect the first and last layers. In all pruning cases, we have to fine-tune the new structure to adjust the weights.\n\n\n\n\n\n\nFigure 9.2: Channel vs layer pruning.\n\n\n\n\n\n2. Establishing a Criteria for Pruning\nEstablishing well-defined criteria for determining which specific structures to prune from a neural network model is a crucial component of the model pruning process. The core goal here is to identify and remove components that contribute the least to the model’s predictive capabilities, while retaining structures integral to preserving the model’s accuracy.\nA widely adopted and effective strategy for systematically pruning structures relies on computing importance scores for individual components like neurons, filters, channels or layers. These scores serve as quantitative metrics to gauge the significance of each structure and its effect on the model’s output.\nThere are several techniques for assigning these importance scores:\n\nWeight Magnitude-Based Pruning: This approach assigns importance scores to a structure by evaluating the aggregate magnitude of their associated weights. Structures with smaller overall weight magnitudes are considered less critical to the network’s performance.\nGradient-Based Pruning: This technique utilizes the gradients of the loss function with respect to the weights associated with a structure. Structures with low cumulative gradient magnitudes, indicating minimal impact on the loss when altered, are prime candidates for pruning.\nActivation-Based Pruning: This method tracks how often a neuron or filter is activated by storing this information in a parameter called the activation counter. Each time the structure is activated, the counter is incremented. A low activation count suggests that the structure is less relevant.\nTaylor Expansion-Based Pruning: This approach approximates the change in the loss function from removing a given weight. By assessing the cumulative loss disturbance from removing all the weights associated with a structure, you can identify structures with negligible impact on the loss, making them suitable candidates for pruning.\n\nThe idea is to measure, either directly or indirectly, the contribution of each component to the model’s output. Structures with minimal influence according to the defined criteria are pruned first. This enables selective, optimized pruning that maximally compresses models while preserving predictive capacity. In general, it is important to evaluate the impact of removing particular structures on the model’s output, with recent works such as (Rachwan et al. 2022) and (Lubana and Dick 2020) investigating combinations of techniques like magnitude-based pruning and gradient-based pruning.\n\nRachwan, John, Daniel Zügner, Bertrand Charpentier, Simon Geisler, Morgane Ayle, and Stephan Günnemann. 2022. “Winning the Lottery Ahead of Time: Efficient Early Network Pruning.” In International Conference on Machine Learning, 18293–309. PMLR.\n\nLubana, Ekdeep Singh, and Robert P Dick. 2020. “A Gradient Flow Framework for Analyzing Network Pruning.” arXiv Preprint arXiv:2009.11839.\n\n\n3. Selecting a pruning strategy\nNow that you understand some techniques for determining the importance of structures within a neural network, the next step is to decide how to apply these insights. This involves selecting an appropriate pruning strategy, which dictates how and when the identified structures are removed and how the model is fine-tuned to maintain its performance. Two main structured pruning strategies exist: iterative pruning and one-shot pruning.\nIterative pruning gradually removes structures across multiple cycles of pruning followed by fine-tuning. In each cycle, a small set of structures are pruned based on importance criteria. The model is then fine-tuned, allowing it to adjust smoothly to the structural changes before the next pruning iteration. This gradual, cyclic approach prevents abrupt accuracy drops. It allows the model to slowly adapt as structures are reduced across iterations.\nConsider a situation where we wish to prune the 6 least effective channels (based on some specific criteria) from a convolutional neural network. In Figure 9.3, we show a simplified pruning process carried over 3 iterations. In every iteration, we only prune 2 channels. Removing the channels results in accuracy degradation. In the first iteration, the accuracy drops from 0.995 to 0.971. However, after we fine-tune the model on the new structure, we are able to recover from the performance loss, bringing the accuracy up to 0.992. Since the structural changes are minor and gradual, the network can more easily adapt to them. Running the same process 2 more times, we end up with a final accuracy of 0.991 (a loss of only 0.4% from the original) and 27% decrease in the number of channels. Thus, iterative pruning enables us to maintain performance while benefiting from increased computational efficiency due to the decreased model size.\n\n\n\n\n\n\nFigure 9.3: Iterative pruning.\n\n\n\nOne-shot pruning takes a more aggressive approach by pruning a large portion of structures simultaneously in one shot based on predefined importance criteria. This is followed by extensive fine-tuning to recover model accuracy. While faster, this aggressive strategy can degrade accuracy if the model cannot recover during fine-tuning.\nThe choice between these strategies involves weighing factors like model size, target sparsity level, available compute and acceptable accuracy losses. One-shot pruning can rapidly compress models, but iterative pruning may enable better accuracy retention for a target level of pruning. In practice, the strategy is tailored based on use case constraints. The overarching aim is to generate an optimal strategy that removes redundancy, achieves efficiency gains through pruning, and finely tunes the model to stabilize accuracy at an acceptable level for deployment.\nNow consider the same network we had in the iterative pruning example. Whereas in the iterative process we pruned 2 channels at a time, in the one-shot pruning we would prune the 6 channels at once (Figure 9.4). Removing 27% of the network’s channel simultaneously alters the structure significantly, causing the accuracy to drop from 0.995 to 0.914. Given the major changes, the network is not able to properly adapt during fine-tuning, and the accuracy went up to 0.943, a 5% degradation from the accuracy of the unpruned network. While the final structures in both iterative pruning and oneshot pruning processes are identical, the former is able to maintain high performance while the latter suffers significant degradations.\n\n\n\n\n\n\nFigure 9.4: One-shot pruning.\n\n\n\n\n\n\nAdvantages of Structured Pruning\nStructured pruning brings forth a myriad of advantages that cater to various facets of model deployment and utilization, especially in environments where computational resources are constrained.\n\nComputational Efficiency: By eliminating entire structures, such as neurons or channels, structured pruning significantly diminishes the computational load during both training and inference phases, thereby enabling faster model predictions and training convergence. Moreover, the removal of structures inherently reduces the model’s memory footprint, ensuring that it demands less storage and memory during operation, which is particularly beneficial in memory-constrained environments like TinyML systems.\nHardware Efficiency: Structured pruning often results in models that are more amenable to deployment on specialized hardware, such as Field-Programmable Gate Arrays (FPGAs) or Application-Specific Integrated Circuits (ASICs), due to the regularity and simplicity of the pruned architecture. With reduced computational requirements, it translates to lower energy consumption, which is crucial for battery-powered devices and sustainable computing practices.\nMaintenance and Deployment: The pruned model, while smaller, retains its original architectural form, which can simplify the deployment pipeline and ensure compatibility with existing systems and frameworks. Also, with fewer parameters and simpler structures, the pruned model becomes easier to manage and monitor in production environments, potentially reducing the overhead associated with model maintenance and updates. Later on, when we dive into MLOps, this need will become apparent.\n\n\n\nUnstructured Pruning\nUnstructured pruning is, as its name suggests, pruning the model without regard to model-specific substructure. As mentioned above, it offers a greater aggression in pruning and can achieve higher model sparsities while maintaining accuracy given less constraints on what can and can’t be pruned. Generally, post-training unstructured pruning consists of an importance criterion for individual model parameters/weights, pruning/removal of weights that fall below the criteria, and optional fine-tuning after to try and recover the accuracy lost during weight removal.\nUnstructured pruning has some advantages over structured pruning: removing individual weights instead of entire model substructures often leads in practice to lower model accuracy decreases. Furthermore, generally determining the criterion of importance for an individual weight is much simpler than for an entire substructure of parameters in structured pruning, making the former preferable for cases where that overhead is hard or unclear to compute. Similarly, the actual process of structured pruning is generally less flexible, as removing individual weights is generally simpler than removing entire substructures and ensuring the model still works.\nUnstructured pruning, while offering the potential for significant model size reduction and enhanced deployability, brings with it challenges related to managing sparse representations and ensuring computational efficiency. It is particularly useful in scenarios where achieving the highest possible model compression is paramount and where the deployment environment can handle sparse computations efficiently.\nTable 9.1 provides a concise comparison between structured and unstructured pruning. In this table, aspects related to the nature and architecture of the pruned model (Definition, Model Regularity, and Compression Level) are grouped together, followed by aspects related to computational considerations (Computational Efficiency and Hardware Compatibility), and ending with aspects related to the implementation and adaptation of the pruned model (Implementation Complexity and Fine-Tuning Complexity). Both pruning strategies offer unique advantages and challenges, as shown in Table 9.1, and the selection between them should be influenced by specific project and deployment requirements.\n\n\n\nTable 9.1: Comparison of structured versus unstructured pruning.\n\n\n\n\n\n\n\n\n\n\nAspect\nStructured Pruning\nUnstructured Pruning\n\n\n\n\nDefinition\nPruning entire structures (e.g., neurons, channels, layers) within the network\nPruning individual weights or neurons, resulting in sparse matrices or non-regular network structures\n\n\nModel Regularity\nMaintains a regular, structured network architecture\nResults in irregular, sparse network architectures\n\n\nCompression Level\nMay offer limited model compression compared to unstructured pruning\nCan achieve higher model compression due to fine-grained pruning\n\n\nComputational Efficiency\nTypically more computationally efficient due to maintaining regular structures\nCan be computationally inefficient due to sparse weight matrices, unless specialized hardware/software is used\n\n\nHardware Compatibility\nGenerally better compatible with various hardware due to regular structures\nMay require hardware that efficiently handles sparse computations to realize benefits\n\n\nImplementation Complexity\nOften simpler to implement and manage due to maintaining network structure\nCan be complex to manage and compute due to sparse representations\n\n\nFine-Tuning Complexity\nMay require less complex fine-tuning strategies post-pruning\nMight necessitate more complex retraining or fine-tuning strategies post-pruning\n\n\n\n\n\n\nIn Figure 9.5 we have examples that illustrate the differences between unstructured and structured pruning. Observe that unstructured pruning can lead to models that no longer obey high-level structural guarantees of their original unpruned counterparts: the left network is no longer a fully connected network after pruning. Structured pruning on the other hand maintains those invariants: in the middle, the fully connected network is pruned in a way that the pruned network is still fully connected; likewise, the CNN maintains its convolutional structure, albeit with fewer filters.\n\n\n\n\n\n\nFigure 9.5: Unstructured vs structured pruning. Source: Qi et al. (2021).\n\n\nQi, Chen, Shibo Shen, Rongpeng Li, Zhifeng Zhao, Qing Liu, Jing Liang, and Honggang Zhang. 2021. “An Efficient Pruning Scheme of Deep Neural Networks for Internet of Things Applications.” EURASIP Journal on Advances in Signal Processing 2021 (1): 31. https://doi.org/10.1186/s13634-021-00744-4.\n\n\n\n\nLottery Ticket Hypothesis\nPruning has evolved from a purely post-training technique that came at the cost of some accuracy, to a powerful meta-learning approach applied during training to reduce model complexity. This advancement in turn improves compute, memory, and latency efficiency at both training and inference.\nA breakthrough finding that catalyzed this evolution was the lottery ticket hypothesis by Frankle and Carbin (2019). Their work states that within dense neural networks, there exist sparse subnetworks, referred to as “winning tickets,” that can match or even exceed the performance of the original model when trained in isolation. Specifically, these winning tickets, when initialized using the same weights as the original network, can achieve similarly high training convergence and accuracy on a given task. It is worthwhile pointing out that they empirically discovered the lottery ticket hypothesis, which was later formalized.\n\nFrankle, Jonathan, and Michael Carbin. 2019. “The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.” In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https://openreview.net/forum?id=rJl-b3RcF7.\nThe intuition behind this hypothesis is that, during the training process of a neural network, many neurons and connections become redundant or unimportant, particularly with the inclusion of training techniques encouraging redundancy like dropout. Identifying, pruning out, and initializing these “winning tickets’’ allows for faster training and more efficient models, as they contain the essential model decision information for the task. Furthermore, as generally known with the bias-variance tradeoff theory, these tickets suffer less from overparameterization and thus generalize better rather than overfitting to the task.\nIn Figure 9.6 we have an example experiment showing pruning and training experiments on a fully connected LeNet over a variety of pruning ratios. In the left plot, notice how heavy pruning reveals a more efficient subnetwork (in green) that is 21.1% the size of the original network (in blue), The subnetwork achieves higher accuracy and in a faster manner than the unpruned version (green line is above the blue line). However, pruning has a limit (sweet spot), and further pruning will produce performance degradations and eventually drop below the unpruned version’s performance (notice how the red, purple, and brown subnetworks gradually drop in accuracy performance) due to the significant loss in the number of parameters.\n\n\n\n\n\n\nFigure 9.6: Lottery ticket hypothesis experiments.\n\n\n\nTo uncover these winning lottery tickets within a neural network, a systematic process is followed. This process, which is illustrated in Figure 9.7 (left side), involves iteratively training, pruning, and reinitializing the network. The steps below outline this approach:\n\nInitialize the network’s weights to random values.\nTrain the network until it converges to the desired performance.\nPrune out some percentage of the edges with the lowest weight values.\nReinitialize the network with the same random values from step 1.\nRepeat steps 2-4 for a number of times, or as long as the accuracy doesn’t significantly degrade.\n\nWhen we finish, we are left with a pruned network (Figure 9.7 right side), which is a subnetwork of the one we start with. The subnetwork should have a significantly smaller structure, while maintaining a comparable level of accuracy.\n\n\n\n\n\n\nFigure 9.7: Finding the winning ticket subnetwork.\n\n\n\n\n\nChallenges & Limitations\nThere is no free lunch with pruning optimizations, with some choices coming with both improvements and costs to considers. Below we discuss some tradeoffs for practitioners to consider.\n\nManaging Sparse Weight Matrices: A sparse weight matrix is a matrix in which many of the elements are zero. Unstructured pruning often results in sparse weight matrices, where many weights are pruned to zero. While this reduces model size, it also introduces several challenges. Computational inefficiency can arise because standard hardware is optimized for dense matrix operations. Without optimizations that take advantage of sparsity, the computational savings from pruning can be lost. Although sparse matrices can be stored without specialized formats, effectively leveraging their sparsity requires careful handling to avoid wasting resources. Algorithmically, navigating sparse structures requires efficiently skipping over zero entries, which adds complexity to the computation and model updates.\nQuality vs. Size Reduction: A key challenge in both structured and unstructured pruning is balancing size reduction with maintaining or improving predictive performance. Establishing robust pruning criteria, whether for removing entire structures (structured pruning) or individual weights (unstructured pruning), is essential. These pruning criteria chosen must accurately identify elements whose removal minimally impacts performance. Careful experimentation is often needed to ensure the pruned model remains efficient while maintaining its predictive performance.\nFine-Tuning and Retraining: Post-pruning fine-tuning is imperative in both structured and unstructured pruning to recover lost performance and stabilize the model. The challenge encompasses determining the extent, duration, and nature of the fine-tuning process, which can be influenced by the pruning method and the degree of pruning applied.\nHardware Compatibility and Efficiency: Especially pertinent to unstructured pruning, hardware compatibility and efficiency become critical. Unstructured pruning often results in sparse weight matrices, which may not be efficiently handled by certain hardware, potentially negating the computational benefits of pruning (see Figure 9.8). Ensuring that pruned models, particularly those resulting from unstructured pruning, are scalable, compatible, and efficient on the target hardware is a significant consideration.\nLegal and Ethical Considerations: Last but not least, adherence to legal and ethical guidelines is important, especially in domains with significant consequences. Pruning methods must undergo rigorous validation, testing, and potentially certification processes to ensure compliance with relevant regulations and standards, though arguably at this time no such formal standards and best practices exist that are vetted and validated by 3rd party entities. This is particularly crucial in high-stakes applications like medical AI and autonomous driving, where quality drops due to pruning-like optimizations can be life-threatening. Moreover, ethical considerations extend beyond safety to fairness and equality; recent work by (Tran et al. 2022) has revealed that pruning can disproportionately impact people of color, underscoring the need for comprehensive ethical evaluation in the pruning process.\n\n\nTran, Cuong, Ferdinando Fioretto, Jung-Eun Kim, and Rakshit Naidu. 2022. “Pruning Has a Disparate Impact on Model Accuracy.” Adv Neural Inf Process Syst 35: 17652–64.\n\n\n\n\n\n\nFigure 9.8: Sparse weight matrix.\n\n\n\n\n\n\n\n\n\nExercise 9.1: Pruning\n\n\n\n\n\nImagine your neural network is a giant, overgrown bush. Pruning is like strategically trimming away branches to make it stronger and more efficient! In the Colab, you’ll learn how to do this trimming in TensorFlow. Understanding these concepts will give you the foundation to see how pruning makes models small enough to run on your phone!\n\n\n\n\n\n\n\n9.2.2 Model Compression\nModel compression techniques are crucial for deploying deep learning models on resource-constrained devices. These techniques aim to create smaller, more efficient models that preserve the predictive performance of the original models.\n\nKnowledge Distillation\nOne popular technique is knowledge distillation (KD), which transfers knowledge from a large, complex “teacher” model to a smaller “student” model. The key idea is to train the student model to mimic the teacher’s outputs. The concept of KD was first popularized by Hinton (2005).\n\nHinton, Geoffrey. 2005. “Van Nostrand’s Scientific Encyclopedia.” Wiley. https://doi.org/10.1002/0471743984.vse0673.\n\nOverview and Benefits\nKnowledge distillation involves transferring knowledge from a large, complex teacher model to a smaller student model. The core idea is to use the teacher’s outputs, known as soft targets, to guide the training of the student model. Unlike traditional “hard targets” (the true labels), soft targets are the probability distributions over classes that the teacher model predicts. These distributions provide richer information about the relationships between classes, which can help the student model learn more effectively.\nYou have learned that the softmax function converts a model’s raw outputs into a probability distribution over classes. A key technique in KD is temperature scaling, which is applied to the softmax function of the teacher model’s outputs. By introducing a temperature parameter, the distribution can be adjusted: a higher temperature produces softer probabilities, meaning the differences between class probabilities become less extreme. This softening effect results in a more uniform distribution, where the model’s confidence in the most likely class is reduced, and other classes have higher, non-zero probabilities. This is valuable for the student model because it allows it to learn not just from the most likely class but from the relative probabilities of all classes, capturing subtle patterns that might be missed if trained only on hard targets. Thus, temperature scaling facilitates the transfer of more nuanced knowledge from the teacher to the student model.\nThe loss function in knowledge distillation typically combines two components: a distillation loss and a classification loss. The distillation loss, often calculated using Kullback-Leibler (KL) divergence, measures the difference between the soft targets produced by the teacher model and the outputs of the student model, encouraging the student to mimic the teacher’s predictions. Meanwhile, the classification loss ensures that the student model correctly predicts the true labels based on the original data. Together, these two components help the student model retain the knowledge of the teacher while adhering to the ground truth labels.\nThese components, when adeptly configured and harmonized, enable the student model to assimilate the teacher model’s knowledge, crafting a pathway towards efficient and robust smaller models that retain the predictive prowess of their larger counterparts. Figure 9.9 visualizes the training procedure of knowledge distillation. Note how the logits or soft labels of the teacher model are used to provide a distillation loss for the student model to learn from.\n\n\n\n\n\n\nFigure 9.9: Knowledge distillation training process. Source: IntelLabs (2023).\n\n\nIntelLabs. 2023. “Knowledge Distillation - Neural Network Distiller.” https://intellabs.github.io/distiller/knowledge_distillation.html.\n\n\n\n\nChallenges\nHowever, KD has a unique set of challenges and considerations that researchers and practitioners must attentively address. One of the challenges is in the meticulous tuning of hyperparameters, such as the temperature parameter in the softmax function and the weighting between the distillation and classification loss in the objective function. Striking a balance that effectively leverages the softened outputs of the teacher model while maintaining fidelity to the true data labels is non-trivial and can significantly impact the student model’s performance and generalization capabilities.\nFurthermore, the architecture of the student model itself poses a considerable challenge. Designing a model that is compact to meet computational and memory constraints, while still being capable of assimilating the essential knowledge from the teacher model, demands a nuanced understanding of model capacity and the inherent trade-offs involved in compression. The student model must be carefully architected to navigate the dichotomy of size and performance, ensuring that the distilled knowledge is meaningfully captured and utilized. Moreover, the choice of teacher model, which inherently influences the quality and nature of the knowledge to be transferred, is important and it introduces an added layer of complexity to the KD process.\nThese challenges underscore the necessity for a thorough and nuanced approach to implementing KD, ensuring that the resultant student models are both efficient and effective in their operational contexts.\n\n\n\nLow-rank Matrix Factorization\nSimilar in approximation theme, low-rank matrix factorization (LRMF) is a mathematical technique used in linear algebra and data analysis to approximate a given matrix by decomposing it into two or more lower-dimensional matrices. The fundamental idea is to express a high-dimensional matrix as a product of lower-rank matrices, which can help reduce the complexity of data while preserving its essential structure. Mathematically, given a matrix \\(A \\in \\mathbb{R}^{m \\times n}\\), LRMF seeks matrices \\(U \\in \\mathbb{R}^{m \\times k}\\) and \\(V \\in \\mathbb{R}^{k \\times n}\\) such that \\(A \\approx UV\\), where \\(k\\) is the rank and is typically much smaller than \\(m\\) and \\(n\\).\n\nBackground and Benefits\nOne of the seminal works in the realm of matrix factorization, particularly in the context of recommendation systems, is the paper by Koren, Bell, and Volinsky (2009). The authors look into various factorization models, providing insights into their efficacy in capturing the underlying patterns in the data and enhancing predictive accuracy in collaborative filtering. LRMF has been widely applied in recommendation systems (such as Netflix, Facebook, etc.), where the user-item interaction matrix is factorized to capture latent factors corresponding to user preferences and item attributes.\n\nKoren, Yehuda, Robert Bell, and Chris Volinsky. 2009. “Matrix Factorization Techniques for Recommender Systems.” Computer 42 (8): 30–37. https://doi.org/10.1109/mc.2009.263.\nThe main advantage of low-rank matrix factorization lies in its ability to reduce data dimensionality as shown in Figure 9.10, where there are fewer parameters to store, making it computationally more efficient and reducing storage requirements at the cost of some additional compute. This can lead to faster computations and more compact data representations, which is especially valuable when dealing with large datasets. Additionally, it may aid in noise reduction and can reveal underlying patterns and relationships in the data.\nFigure 9.10 illustrates the decrease in parameterization enabled by low-rank matrix factorization. Observe how the matrix \\(M\\) can be approximated by the product of matrices \\(L_k\\) and \\(R_k^T\\). For intuition, most fully connected layers in networks are stored as a projection matrix \\(M\\), which requires \\(m \\times n\\) parameter to be loaded on computation. However, by decomposing and approximating it as the product of two lower rank matrices, we thus only need to store \\(m \\times k + k\\times n\\) parameters in terms of storage while incurring an additional compute cost of the matrix multiplication. So long as \\(k < n/2\\), this factorization has fewer parameters total to store while adding a computation of runtime \\(O(mkn)\\) (Gu 2023).\n\nGu, Ivy. 2023. “Deep Learning Model Compression (Ii) by Ivy Gu Medium.” https://ivygdy.medium.com/deep-learning-model-compression-ii-546352ea9453.\n\n\n\n\n\n\nFigure 9.10: Low matrix factorization. Source: The Clever Machine.\n\n\n\n\n\nChallenges\nBut practitioners and researchers encounter a spectrum of challenges and considerations that necessitate careful attention and strategic approaches. As with any lossy compression technique, we may lose information during this approximation process: choosing the correct rank that balances the information lost and the computational costs is tricky as well and adds an additional hyper-parameter to tune for.\nLow-rank matrix factorization is a valuable tool for dimensionality reduction and making compute fit onto edge devices but, like other techniques, needs to be carefully tuned to the model and task at hand. A key challenge resides in managing the computational complexity inherent to LRMF, especially when grappling with high-dimensional and large-scale data. The computational burden, particularly in the context of real-time applications and massive datasets, remains a significant hurdle for effectively using LRMF.\nMoreover, the conundrum of choosing the optimal rank \\(k\\), for the factorization introduces another layer of complexity. The selection of \\(k\\) inherently involves a trade-off between approximation accuracy and model simplicity, and identifying a rank that adeptly balances these conflicting objectives often demands a combination of domain expertise, empirical validation, and sometimes, heuristic approaches. The challenge is further amplified when the data encompasses noise or when the inherent low-rank structure is not pronounced, making the determination of a suitable \\(k\\) even more elusive.\nHandling missing or sparse data, a common occurrence in applications like recommendation systems, poses another substantial challenge. Traditional matrix factorization techniques, such as Singular Value Decomposition (SVD), are not directly applicable to matrices with missing entries, necessitating the development and application of specialized algorithms that can factorize incomplete matrices while mitigating the risks of overfitting to the observed entries. This often involves incorporating regularization terms or constraining the factorization in specific ways, which in turn introduces additional hyperparameters that need to be judiciously selected.\nFurthermore, in scenarios where data evolves or grows over time, developing LRMF models that can adapt to new data without necessitating a complete re-factorization is a critical yet challenging endeavor. Online and incremental matrix factorization algorithms seek to address this by enabling the update of factorized matrices as new data arrives, yet ensuring stability, accuracy, and computational efficiency in these dynamic settings remains an intricate task. This is particularly challenging in the space of TinyML, where edge redeployment for refreshed models can be quite challenging.\n\n\n\nTensor Decomposition\nYou have learned in Section 6.4.1 that tensors are flexible structures, commonly used by ML Frameworks, that can represent data in higher dimensions. Similar to low-rank matrix factorization, more complex models may store weights in higher dimensions, such as tensors. Tensor decomposition is the higher-dimensional analogue of matrix factorization, where a model tensor is decomposed into lower rank components (see Figure 9.11). These lower-rank components are easier to compute on and store but may suffer from the same issues mentioned above, such as information loss and the need for nuanced hyperparameter tuning. Mathematically, given a tensor \\(\\mathcal{A}\\), tensor decomposition seeks to represent \\(\\mathcal{A}\\) as a combination of simpler tensors, facilitating a compressed representation that approximates the original data while minimizing the loss of information.\nThe work of Tamara G. Kolda and Brett W. Bader, “Tensor Decompositions and Applications” (2009), stands out as a seminal paper in the field of tensor decompositions. The authors provide a comprehensive overview of various tensor decomposition methods, exploring their mathematical underpinnings, algorithms, and a wide array of applications, ranging from signal processing to data mining. Of course, the reason we are discussing it is because it has huge potential for system performance improvements, particularly in the space of TinyML, where throughput and memory footprint savings are crucial to feasibility of deployments.\n\n\n\n\n\n\nFigure 9.11: Tensor decomposition. Source: Xinyu (n.d.).\n\n\nXinyu, Chen. n.d.\n\n\n\n\n\n\n\n\nExercise 9.2: Scalable Model Compression with TensorFlow\n\n\n\n\n\nThis Colab dives into a technique for compressing models while maintaining high accuracy. The key idea is to train a model with an extra penalty term that encourages the model to be more compressible. Then, the model is encoded using a special coding scheme that aligns with this penalty. This approach allows you to achieve compressed models that perform just as well as the original models and is useful in deploying models to devices with limited resources like mobile phones and edge devices.\n\n\n\n\n\n\n\n9.2.3 Edge-Aware Model Design\nNow, we reach the other end of the hardware-software gradient, where we specifically make model architecture decisions directly given knowledge of the edge devices we wish to deploy on.\nAs covered in previous sections, edge devices are constrained specifically with limitations on memory and parallelizable computations: as such, if there are critical inference speed requirements, computations must be flexible enough to satisfy hardware constraints, something that can be designed at the model architecture level. Furthermore, trying to cram SOTA large ML models onto edge devices even after pruning and compression is generally infeasible purely due to size: the model complexity itself must be chosen with more nuance as to more feasibly fit the device. Edge ML developers have approached this architectural challenge both through designing bespoke edge ML model architectures and through device-aware neural architecture search (NAS), which can more systematically generate feasible on-device model architectures.\n\nModel Design Techniques\nOne edge friendly architecture design, commonly used in deep learning for image processing, is depthwise separable convolutions. It consists of two distinct steps: the first is the depthwise convolution, where each input channel is convolved independently with its own set of learnable filters, as shown in Figure 9.12. This step reduces computational complexity by a significant margin compared to standard convolutions, as it drastically reduces the number of parameters and computations involved. The second step is the pointwise convolution, which combines the output of the depthwise convolution channels through a 1x1 convolution, creating inter-channel interactions. This approach offers several advantages. Benefits include reduced model size, faster inference times, and often better generalization due to fewer parameters, making it suitable for mobile and embedded applications. However, depthwise separable convolutions may not capture complex spatial interactions as effectively as standard convolutions and might require more depth (layers) to achieve the same level of representational power, potentially leading to longer training times. Nonetheless, their efficiency in terms of parameters and computation makes them a popular choice in modern convolutional neural network architectures.\n\n\n\n\n\n\nFigure 9.12: Depthwise separable convolutions. Source: Hegde (2023).\n\n\nHegde, Sumant. 2023. “An Introduction to Separable Convolutions - Analytics Vidhya.” https://www.analyticsvidhya.com/blog/2021/11/an-introduction-to-separable-convolutions/.\n\n\n\n\nExample Model Architectures\nIn this vein, a number of recent architectures have been, from inception, specifically designed for maximizing accuracy on an edge deployment, notably SqueezeNet, MobileNet, and EfficientNet.\n\nSqueezeNet by Iandola et al. (2016) for instance, utilizes a compact architecture with 1x1 convolutions and fire modules to minimize the number of parameters while maintaining strong accuracy.\nMobileNet by Howard et al. (2017), on the other hand, employs the aforementioned depthwise separable convolutions to reduce both computation and model size.\nEfficientNet by Tan and Le (2023) takes a different approach by optimizing network scaling (i.e. varying the depth, width and resolution of a network) and compound scaling, a more nuanced variation network scaling, to achieve superior performance with fewer parameters.\n\n\nIandola, Forrest N, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer. 2016. “SqueezeNet: Alexnet-level Accuracy with 50x Fewer Parameters and 0.5 MB Model Size.” ArXiv Preprint abs/1602.07360. https://arxiv.org/abs/1602.07360.\n\nHoward, Andrew G., Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.” ArXiv Preprint. https://arxiv.org/abs/1704.04861.\n\nTan, Mingxing, and Quoc V. Le. 2023. “Demystifying Deep Learning.” Wiley. https://doi.org/10.1002/9781394205639.ch6.\nThese models are essential in the context of edge computing where limited processing power and memory require lightweight yet effective models that can efficiently perform tasks such as image recognition, object detection, and more. Their design principles showcase the importance of intentionally tailored model architecture for edge computing, where performance and efficiency must fit within constraints.\n\n\nStreamlining Model Architecture Search\nLastly, to address the challenge of finding efficient model architectures that are compatible with edge devices, researchers have developed systematized pipelines that streamline the search for performant designs. Two notable frameworks in this space are TinyNAS by J. Lin et al. (2020) and MorphNet by Gordon et al. (2018), which automate the process of optimizing neural network architectures for edge deployment.\n\nGordon, Ariel, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang, and Edward Choi. 2018. “MorphNet: Fast &Amp; Simple Resource-Constrained Structure Learning of Deep Networks.” In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1586–95. IEEE. https://doi.org/10.1109/cvpr.2018.00171.\nTinyNAS is an innovative neural architecture search framework introduced in the MCUNet paper, designed to efficiently discover lightweight neural network architectures for edge devices with limited computational resources. Leveraging reinforcement learning and a compact search space of micro neural modules, TinyNAS optimizes for both accuracy and latency, enabling the deployment of deep learning models on microcontrollers, IoT devices, and other resource-constrained platforms. Specifically, TinyNAS, in conjunction with a network optimizer TinyEngine, generates different search spaces by scaling the input resolution and the model width of a model, then collects the computation FLOPs distribution of satisfying networks within the search space to evaluate its priority. TinyNAS relies on the assumption that a search space that accommodates higher FLOPs under memory constraint can produce higher accuracy models, something that the authors verified in practice in their work. In empirical performance, TinyEngine reduced the peak memory usage of models by around 3.4 times and accelerated inference by 1.7 to 3.3 times compared to TFLite and CMSIS-NN.\nSimilarly, MorphNet is a neural network optimization framework designed to automatically reshape and morph the architecture of deep neural networks, optimizing them for specific deployment requirements. It achieves this through two steps: first, it leverages a set of customizable network morphing operations, such as widening or deepening layers, to dynamically adjust the network’s structure. These operations enable the network to adapt to various computational constraints, including model size, latency, and accuracy targets, which are extremely prevalent in edge computing usage. In the second step, MorphNet uses a reinforcement learning-based approach to search for the optimal permutation of morphing operations, effectively balancing the trade-off between model size and performance. This innovative method allows deep learning practitioners to automatically tailor neural network architectures to specific application and hardware requirements, ensuring efficient and effective deployment across various platforms.\nTinyNAS and MorphNet represent a few of the many significant advancements in the field of systematic neural network optimization, allowing architectures to be systematically chosen and generated to fit perfectly within problem constraints.\n\n\n\n\n\n\nExercise 9.3: Edge-Aware Model Design\n\n\n\n\n\nImagine you’re building a tiny robot that can identify different flowers. It needs to be smart, but also small and energy-efficient! In the “Edge-Aware Model Design” world, we learned about techniques like depthwise separable convolutions and architectures like SqueezeNet, MobileNet, and EfficientNet – all designed to pack intelligence into compact models. Now, let’s see these ideas in action with some xColabs:\nSqueezeNet in Action: Maybe you’d like a Colab showing how to train a SqueezeNet model on a flower image dataset. This would demonstrate its small size and how it learns to recognize patterns despite its efficiency.\n\nMobileNet Exploration: Ever wonder if those tiny image models are just as good as the big ones? Let’s find out! In this Colab, we’re pitting MobileNet, the lightweight champion, against a classic image classification model. We’ll race them for speed, measure their memory needs, and see who comes out on top for accuracy. Get ready for a battle of the image brains!", "crumbs": [ "Training", "9  Model Optimizations" @@ -961,7 +961,7 @@ "href": "contents/optimizations/optimizations.html#sec-model_ops_numerics", "title": "9  Model Optimizations", "section": "9.3 Efficient Numerics Representation", - "text": "9.3 Efficient Numerics Representation\nNumerics representation involves a myriad of considerations, including, but not limited to, the precision of numbers, their encoding formats, and the arithmetic operations facilitated. It invariably involves a rich array of different trade-offs, where practitioners are tasked with navigating between numerical accuracy and computational efficiency. For instance, while lower-precision numerics may offer the allure of reduced memory usage and expedited computations, they concurrently present challenges pertaining to numerical stability and potential degradation of model accuracy.\n\nMotivation\nThe imperative for efficient numerics representation arises, particularly as efficient model optimization alone falls short when adapting models for deployment on low-powered edge devices operating under stringent constraints.\nBeyond minimizing memory demands, the tremendous potential of efficient numerics representation lies in, but is not limited to, these fundamental ways. By diminishing computational intensity, efficient numerics can thereby amplify computational speed, allowing more complex models to compute on low-powered devices. Reducing the bit precision of weights and activations on heavily over-parameterized models enables condensation of model size for edge devices without significantly harming the model’s predictive accuracy. With the omnipresence of neural networks in models, efficient numerics has a unique advantage in leveraging the layered structure of NNs to vary numeric precision across layers, minimizing precision in resistant layers while preserving higher precision in sensitive layers.\nIn this section, we will dive into how practitioners can harness the principles of hardware-software co-design at the lowest levels of a model to facilitate compatibility with edge devices. Kicking off with an introduction to the numerics, we will examine its implications for device memory and computational complexity. Subsequently, we will embark on a discussion regarding the trade-offs entailed in adopting this strategy, followed by a deep dive into a paramount method of efficient numerics: quantization.\n\n\n9.3.1 The Basics\n\nTypes\nNumerical data, the bedrock upon which machine learning models stand, manifest in two primary forms. These are integers and floating point numbers.\nIntegers: Whole numbers, devoid of fractional components, integers (e.g., -3, 0, 42) are key in scenarios demanding discrete values. For instance, in ML, class labels in a classification task might be represented as integers, where “cat”, “dog”, and “bird” could be encoded as 0, 1, and 2 respectively.\nFloating-Point Numbers: Encompassing real numbers, floating-point numbers (e.g., -3.14, 0.01, 2.71828) afford the representation of values with fractional components. In ML model parameters, weights might be initialized with small floating-point values, such as 0.001 or -0.045, to commence the training process. Currently, there are 4 popular precision formats discussed below.\nVariable bit widths: Beyond the standard widths, research is ongoing into extremely low bit-width numerics, even down to binary or ternary representations. Extremely low bit-width operations can offer significant speedups and reduce power consumption even further. While challenges remain in maintaining model accuracy with such drastic quantization, advances continue to be made in this area.\n\n\nPrecision\nPrecision, delineating the exactness with which a number is represented, bifurcates typically into single, double, half and in recent years there have been a number of other precisions that have emerged to better support machine learning tasks efficiently on the underlying hardware.\nDouble Precision (Float64): Allocating 64 bits, double precision (e.g., 3.141592653589793) provides heightened accuracy, albeit demanding augmented memory and computational resources. In scientific computations, where precision is paramount, variables like π might be represented with Float64.\nSingle Precision (Float32): With 32 bits at its disposal, single precision (e.g., 3.1415927) strikes a balance between numerical accuracy and memory conservation. In ML, Float32 might be employed to store weights during training to maintain a reasonable level of precision.\nHalf Precision (Float16): Constrained to 16 bits, half precision (e.g., 3.14) curtails memory usage and can expedite computations, albeit sacrificing numerical accuracy and range. In ML, especially during inference on resource-constrained devices, Float16 might be utilized to reduce the model’s memory footprint.\nBfloat16: Brain Floating-Point Format or Bfloat16, also employs 16 bits but allocates them differently compared to FP16: 1 bit for the sign, 8 bits for the exponent (resulting in the same number range as in float32), and 7 bits for the fraction. This format, developed by Google, prioritizes a larger exponent range over precision, making it particularly useful in deep learning applications where the dynamic range is crucial.\nFigure 9.13 illustrates the differences between the three floating-point formats: Float32, Float16, and BFloat16.\n\n\n\n\n\n\nFigure 9.13: Three floating-point formats.\n\n\n\nInteger: Integer representations are made using 8, 4, and 2 bits. They are often used during the inference phase of neural networks, where the weights and activations of the model are quantized to these lower precisions. Integer representations are deterministic and offer significant speed and memory advantages over floating-point representations. For many inference tasks, especially on edge devices, the slight loss in accuracy due to quantization is often acceptable given the efficiency gains. An extreme form of integer numerics is for binary neural networks (BNNs), where weights and activations are constrained to one of two values: either +1 or -1.\nYou may refer back to Section 8.6.1 for a table comparison between the trade-offs of different numeric types.\n\n\nNumeric Encoding and Storage\nNumeric encoding, the art of transmuting numbers into a computer-amenable format, and their subsequent storage are critical for computational efficiency. For instance, floating-point numbers might be encoded using the IEEE 754 standard, which apportions bits among sign, exponent, and fraction components, thereby enabling the representation of a vast array of values with a single format. There are a few new IEEE floating point formats that have been defined specifically for AI workloads:\n\nbfloat16- A 16-bit floating point format introduced by Google. It has 8 bits for exponent, 7 bits for mantissa and 1 bit for sign. Offers a reduced precision compromise between 32-bit float and 8-bit integers. Supported on many hardware accelerators.\nposit - A configurable format that can represent different levels of precision based on exponent bits. Aims to be more efficient than IEEE 754 binary floats. Has adjustable dynamic range and precision.\nFlexpoint - A format introduced by Intel that can dynamically adjust precision across layers or within a layer. Allows tuning precision to accuracy and hardware requirements.\nBF16ALT - A proposed 16-bit format by ARM as an alternative to bfloat16. Uses additional bit in exponent to prevent overflow/underflow.\nTF32 - Introduced by Nvidia for Ampere GPUs. Uses 10 bits for exponent instead of 8 bits like FP32. Improves model training performance while maintaining accuracy.\nFP8 - 8-bit floating point format that keeps 6 bits for mantissa and 2 bits for exponent. Enables better dynamic range than integers.\n\nThe key goals of these new formats are to provide lower precision alternatives to 32-bit floats for better computational efficiency and performance on AI accelerators while maintaining model accuracy. They offer different tradeoffs in terms of precision, range and implementation cost/complexity.\n\n\n\n9.3.2 Efficiency Benefits\nAs you learned in Section 8.6.2, numerical efficiency matters for machine learning workloads for a number of reasons. Efficient numerics is not just about reducing the bit-width of numbers but understanding the trade-offs between accuracy and efficiency. As machine learning models become more pervasive, especially in real-world, resource-constrained environments, the focus on efficient numerics will continue to grow. By thoughtfully selecting and leveraging the appropriate numeric precision, one can achieve robust model performance while optimizing for speed, memory, and energy.\n\n\n9.3.3 Numeric Representation Nuances\nThere are a number of nuances with numerical representations for ML that require us to have an understanding of both the theoretical and practical aspects of numerics representation, as well as a keen awareness of the specific requirements and constraints of the application domain.\n\nMemory Usage\nThe memory footprint of ML models, particularly those of considerable complexity and depth, can be substantial, thereby posing a significant challenge in both training and deployment phases. For instance, a deep neural network with 100 million parameters, represented using Float32 (32 bits or 4 bytes per parameter), would necessitate approximately 400 MB of memory just for storing the model weights. This does not account for additional memory requirements during training for storing gradients, optimizer states, and forward pass caches, which can further amplify the memory usage, potentially straining the resources on certain hardware, especially edge devices with limited memory capacity.\nThe choice of numeric representation further impacts memory usage and computational efficiency. For example, using Float64 for model weights would double the memory requirements compared to Float32, and could potentially increase computational time as well. For a weight matrix with dimensions [1000, 1000], Float64 would consume approximately 8MB of memory, while Float32 would reduce this to about 4MB. Thus, selecting an appropriate numeric format is crucial for optimizing both memory and computational efficiency.\n\n\nComputational Complexity\nNumerical precision directly impacts computational complexity, influencing the time and resources required to perform arithmetic operations. For example, operations using Float64 generally consume more computational resources than their Float32 or Float16 counterparts (see Figure 9.14). In the realm of ML, where models might need to process millions of operations (e.g., multiplications and additions in matrix operations during forward and backward passes), even minor differences in the computational complexity per operation can aggregate into a substantial impact on training and inference times. As shown in Figure 9.15, quantized models can be many times faster than their unquantized versions.\n\n\n\n\n\n\nFigure 9.14: Energy use by quantized operations. Source: Mark Horowitz, Stanford University.\n\n\n\n\n\n\n\n\n\nFigure 9.15: Speed of three different models in normal and quantized form.\n\n\n\nIn addition to pure runtimes, there is also a concern over energy efficiency. Not all numerical computations are created equal from the underlying hardware standpoint. Some numerical operations are more energy efficient than others. For example, Figure 9.16 below shows that integer addition is much more energy efficient than integer multiplication.\n\n\n\n\n\n\nFigure 9.16: Energy use by quantized operations. Source: Isscc (2014).\n\n\nIsscc. 2014. “Computing’s Energy Problem (and What We Can Do about It).” https://ieeexplore.ieee.org/document/6757323.\n\n\n\n\nHardware Compatibility\nEnsuring compatibility and optimized performance across diverse hardware platforms is another challenge in numerics representation. Different hardware, such as CPUs, GPUs, TPUs, and FPGAs, have varying capabilities and optimizations for handling different numeric precisions. For example, certain GPUs might be optimized for Float32 computations, while others might provide accelerations for Float16. Developing and optimizing ML models that can leverage the specific numerical capabilities of different hardware, while ensuring that the model maintains its accuracy and robustness, requires careful consideration and potentially additional development and testing efforts.\n\n\nPrecision and Accuracy Trade-offs\nThe trade-off between numerical precision and model accuracy is a nuanced challenge in numerics representation. Utilizing lower-precision numerics, such as Float16, might conserve memory and expedite computations but can also introduce issues like quantization error and reduced numerical range. For instance, training a model with Float16 might introduce challenges in representing very small gradient values, potentially impacting the convergence and stability of the training process. Furthermore, in certain applications, such as scientific simulations or financial computations, where high precision is paramount, the use of lower-precision numerics might not be permissible due to the risk of accruing significant errors.\n\n\nTrade-off Examples\nTo understand and appreciate the nuances, let’s consider some use case examples. Through these we will realize that the choice of numeric representation is not merely a technical decision but a strategic one, influencing the model’s predictive acumen, its computational demands, and its deployability across diverse computational environments. In this section we will look at a couple of examples to better understand the trade-offs with numerics and how they tie to the real world.\n\nAutonomous Vehicles\nIn the domain of autonomous vehicles, ML models are employed to interpret sensor data and make real-time decisions. The models must process high-dimensional data from various sensors (e.g., LiDAR, cameras, radar) and execute numerous computations within a constrained time frame to ensure safe and responsive vehicle operation. So the trade-offs here would include:\n\nMemory Usage: Storing and processing high-resolution sensor data, especially in floating-point formats, can consume substantial memory.\nComputational Complexity: Real-time processing demands efficient computations, where higher-precision numerics might impede the timely execution of control actions.\n\n\n\nMobile Health Applications\nMobile health applications often use ML models for tasks like activity recognition, health monitoring, or predictive analytics, operating within the resource-constrained environment of mobile devices. The trade-offs here would include:\n\nPrecision and Accuracy Trade-offs: Employing lower-precision numerics to conserve resources might impact the accuracy of health predictions or anomaly detections, which could have significant implications for user health and safety.\nHardware Compatibility: Models need to be optimized for diverse mobile hardware, ensuring efficient operation across a wide range of devices with varying numerical computation capabilities.\n\n\n\nHigh-Frequency Trading (HFT) Systems\nHFT systems leverage ML models to make rapid trading decisions based on real-time market data. These systems demand extremely low-latency responses to capitalize on short-lived trading opportunities.\n\nComputational Complexity: The models must process and analyze vast streams of market data with minimal latency, where even slight delays, potentially introduced by higher-precision numerics, can result in missed opportunities.\nPrecision and Accuracy Trade-offs: Financial computations often demand high numerical precision to ensure accurate pricing and risk assessments, posing challenges in balancing computational efficiency and numerical accuracy.\n\n\n\nEdge-Based Surveillance Systems\nSurveillance systems deployed on edge devices, like security cameras, use ML models for tasks like object detection, activity recognition, and anomaly detection, often operating under stringent resource constraints.\n\nMemory Usage: Storing pre-trained models and processing video feeds in real-time demands efficient memory usage, which can be challenging with high-precision numerics.\nHardware Compatibility: Ensuring that models can operate efficiently on edge devices with varying hardware capabilities and optimizations for different numeric precisions is crucial for widespread deployment.\n\n\n\nScientific Simulations\nML models are increasingly being utilized in scientific simulations, such as climate modeling or molecular dynamics simulations, to improve predictive capabilities and reduce computational demands.\n\nPrecision and Accuracy Trade-offs: Scientific simulations often require high numerical precision to ensure accurate and reliable results, which can conflict with the desire to reduce computational demands via lower-precision numerics.\nComputational Complexity: The models must manage and process complex, high-dimensional simulation data efficiently to ensure timely results and enable large-scale or long-duration simulations.\n\nThese examples illustrate diverse scenarios where the challenges of numerics representation in ML models are prominently manifested. Each system presents a unique set of requirements and constraints, necessitating tailored strategies and solutions to navigate the challenges of memory usage, computational complexity, precision-accuracy trade-offs, and hardware compatibility.\n\n\n\n\n9.3.4 Quantization\nQuantization is prevalent in various scientific and technological domains, and it essentially involves the mapping or constraining of a continuous set or range into a discrete counterpart to minimize the number of bits required.\n\nInitial Breakdown\nWe begin our foray into quantization with a brief analysis of one important use for quantization.\nIn signal processing, the continuous sine wave (shown in Figure 9.17) can be quantized into discrete values through a process known as sampling. This is a fundamental concept in digital signal processing and is crucial for converting analog signals (like the continuous sine wave) into a digital form that can be processed by computers. The sine wave is a prevalent example due to its periodic and smooth nature, making it a useful tool for explaining concepts like frequency, amplitude, phase, and, of course, quantization.\n\n\n\n\n\n\nFigure 9.17: Sine Wave.\n\n\n\nIn the quantized version shown in Figure 9.18, the continuous sine wave (Figure 9.17) is sampled at regular intervals (in this case, every \\(\\frac{\\pi}{4}\\) radians), and only these sampled values are represented in the digital version of the signal. The step-wise lines between the points show one way to represent the quantized signal in a piecewise-constant form. This is a simplified example of how analog-to-digital conversion works, where a continuous signal is mapped to a discrete set of values, enabling it to be represented and processed digitally.\n\n\n\n\n\n\nFigure 9.18: Quantized Sine Wave.\n\n\n\nReturning to the context of Machine Learning (ML), quantization refers to the process of constraining the possible values that numerical parameters (such as weights and biases) can take to a discrete set, thereby reducing the precision of the parameters and consequently, the model’s memory footprint. When properly implemented, quantization can reduce model size by up to 4x and improve inference latency and throughput by up to 2-3x. Figure 9.19 illustrates the impact that quantization has on different models’ sizes: for example, an Image Classification model like ResNet-v2 can be compressed from 180MB down to 45MB with 8-bit quantization. There is typically less than 1% loss in model accuracy from well tuned quantization. Accuracy can often be recovered by re-training the quantized model with quantization-aware training techniques. Therefore, this technique has emerged to be very important in deploying ML models to resource-constrained environments, such as mobile devices, IoT devices, and edge computing platforms, where computational resources (memory and processing power) are limited.\n\n\n\n\n\n\nFigure 9.19: Effect of quantization on model sizes. Source: HarvardX.\n\n\n\nThere are several dimensions to quantization such as uniformity, stochasticity (or determinism), symmetry, granularity (across layers/channels/groups or even within channels), range calibration considerations (static vs dynamic), and fine-tuning methods (QAT, PTQ, ZSQ). We examine these below.\n\n\n\n9.3.5 Types\n\nUniform Quantization\nUniform quantization involves mapping continuous or high-precision values to a lower-precision representation using a uniform scale. This means that the interval between each possible quantized value is consistent. For example, if weights of a neural network layer are quantized to 8-bit integers (values between 0 and 255), a weight with a floating-point value of 0.56 might be mapped to an integer value of 143, assuming a linear mapping between the original and quantized scales. Due to its use of integer or fixed-point math pipelines, this form of quantization allows computation on the quantized domain without the need to dequantize beforehand.\nThe process for implementing uniform quantization starts with choosing a range of real numbers to be quantized. The next step is to select a quantization function and map the real values to the integers representable by the bit-width of the quantized representation. For instance, a popular choice for a quantization function is:\n\\[\nQ(r)=Int(r/S) - Z\n\\]\nwhere \\(Q\\) is the quantization operator, \\(r\\) is a real valued input (in our case, an activation or weight), \\(S\\) is a real valued scaling factor, and \\(Z\\) is an integer zero point. The Int function maps a real value to an integer value through a rounding operation. Through this function, we have effectively mapped real values \\(r\\) to some integer values, resulting in quantized levels which are uniformly spaced.\nWhen the need arises for practitioners to retrieve the original higher precision values, real values \\(r\\) can be recovered from quantized values through an operation known as dequantization. In the example above, this would mean performing the following operation on our quantized value:\n\\[\n\\bar{r} = S(Q(r) + Z)\n\\]\nAs discussed, some precision in the real value is lost by quantization. In this case, the recovered value \\(\\bar{r}\\) will not exactly match \\(r\\) due to the rounding operation. This is an important tradeoff to note; however, in many successful uses of quantization, the loss of precision can be negligible and the test accuracy remains high. Despite this, uniform quantization continues to be the current de-facto choice due to its simplicity and efficient mapping to hardware.\n\n\nNon-uniform Quantization\nNon-uniform quantization, on the other hand, does not maintain a consistent interval between quantized values. This approach might be used to allocate more possible discrete values in regions where the parameter values are more densely populated, thereby preserving more detail where it is most needed. For instance, in bell-shaped distributions of weights with long tails, a set of weights in a model predominantly lies within a certain range; thus, more quantization levels might be allocated to that range to preserve finer details, enabling us to better capture information. However, one major weakness of non-uniform quantization is that it requires dequantization before higher precision computations due to its non-uniformity, restricting its ability to accelerate computation compared to uniform quantization.\nTypically, a rule-based non-uniform quantization uses a logarithmic distribution of exponentially increasing steps and levels as opposed to linearly. Another popular branch lies in binary-code-based quantization where real number vectors are quantized into binary vectors with a scaling factor. Notably, there is no closed form solution for minimizing errors between the real value and non-uniformly quantized value, so most quantizations in this field rely on heuristic solutions. For instance, recent work by Xu et al. (2018) formulates non-uniform quantization as an optimization problem where the quantization steps/levels in quantizer \\(Q\\) are adjusted to minimize the difference between the original tensor and quantized counterpart.\n\nXu, Chen, Jianqiang Yao, Zhouchen Lin, Wenwu Ou, Yuanbin Cao, Zhirong Wang, and Hongbin Zha. 2018. “Alternating Multi-Bit Quantization for Recurrent Neural Networks.” In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=S19dR9x0b.\n\\[\n\\min_Q ||Q(r)-r||^2\n\\]\nFurthermore, learnable quantizers can be jointly trained with model parameters, and the quantization steps/levels are generally trained with iterative optimization or gradient descent. Additionally, clustering has been used to alleviate information loss from quantization. While capable of capturing higher levels of detail, non-uniform quantization schemes can be difficult to deploy efficiently on general computation hardware, making it less-preferred to methods which use uniform quantization.\n\n\n\n\n\n\nFigure 9.20: Quantization uniformity. Source: Gholami et al. (2021).\n\n\n\n\n\nStochastic Quantization\nUnlike the two previous approaches which generate deterministic mappings, there is some work exploring the idea of stochastic quantization for quantization-aware training and reduced precision training. This approach maps floating numbers up or down with a probability associated to the magnitude of the weight update. The hope generated by high level intuition is that such a probabilistic approach may allow a neural network to explore more, as compared to deterministic quantization. Supposedly, enabling a stochastic rounding may allow neural networks to escape local optimums, thereby updating its parameters. Below are two example stochastic mapping functions:\n\n\n\n\n\n\n\nFigure 9.21: Integer vs Binary quantization functions.\n\n\n\n\n\nZero Shot Quantization\nZero-shot quantization refers to the process of converting a full-precision deep learning model directly into a low-precision, quantized model without the need for any retraining or fine-tuning on the quantized model. The primary advantage of this approach is its efficiency, as it eliminates the often time-consuming and resource-intensive process of retraining a model post-quantization. By leveraging techniques that anticipate and minimize quantization errors, zero-shot quantization aims to maintain the model’s original accuracy even after reducing its numerical precision. It is particularly useful for Machine Learning as a Service (MLaaS) providers aiming to expedite the deployment of their customer’s workloads without having to access their datasets.\n\n\n\n9.3.6 Calibration\nCalibration is the process of selecting the most effective clipping range [\\(\\alpha\\), \\(\\beta\\)] for weights and activations to be quantized to. For example, consider quantizing activations that originally have a floating-point range between -6 and 6 to 8-bit integers. If you just take the minimum and maximum possible 8-bit integer values (-128 to 127) as your quantization range, it might not be the most effective. Instead, calibration would involve passing a representative dataset then use this observed range for quantization.\nThere are many calibration methods but a few commonly used include:\n\nMax: Use the maximum absolute value seen during calibration. However, this method is susceptible to outlier data. Notice how in Figure 9.22, we have an outlier cluster around 2.1, while the rest are clustered around smaller values.\nEntropy: Use KL divergence to minimize information loss between the original floating-point values and values that could be represented by the quantized format. This is the default method used by TensorRT.\nPercentile: Set the range to a percentile of the distribution of absolute values seen during calibration. For example, 99% calibration would clip 1% of the largest magnitude values.\n\n\n\n\n\n\n\nFigure 9.22: Input activations to layer 3 in ResNet50. Source: @Wu, Judd, and Isaev (2020).\n\n\n\nImportantly, the quality of calibration can make a difference between a quantized model that retains most of its accuracy and one that degrades significantly. Hence, it’s an essential step in the quantization process. When choosing a calibration range, there are two types: symmetric and asymmetric.\n\nSymmetric Quantization\nSymmetric quantization maps real values to a symmetrical clipping range centered around 0. This involves choosing a range [\\(\\alpha\\), \\(\\beta\\)] where \\(\\alpha = -\\beta\\). For example, one symmetrical range would be based on the min/max values of the real values such that:\n\\[\n\\alpha = \\beta = max(abs(r_{max}), abs(r_{min}))\n\\]\nSymmetric clipping ranges are the most widely adopted in practice as they have the advantage of easier implementation. In particular, the mapping of zero to zero in the clipping range (sometimes called “zeroing out of the zero point”) can lead to reduction in computational cost during inference (Wu, Judd, and Isaev 2020).\n\n\nAsymmetric Quantization\nAsymmetric quantization maps real values to an asymmetrical clipping range that isn’t necessarily centered around 0, as shown in Figure 9.23 on the right. It involves choosing a range [\\(\\alpha\\), \\(\\beta\\)] where \\(\\alpha \\neq -\\beta\\). For example, selecting a range based on the minimum and maximum real values, or where \\(\\alpha = r_{min}\\) and \\(\\beta = r_{max}\\), creates an asymmetric range. Typically, asymmetric quantization produces tighter clipping ranges compared to symmetric quantization, which is important when target weights and activations are imbalanced, e.g., the activation after the ReLU always has non-negative values. Despite producing tighter clipping ranges, asymmetric quantization is less preferred to symmetric quantization as it doesn’t always zero out the real value zero.\n\n\n\n\n\n\nFigure 9.23: Quantization (a)symmetry. Source: Gholami et al. (2021).\n\n\n\n\n\nGranularity\nUpon deciding the type of clipping range, it is essential to tighten the range to allow a model to retain as much of its accuracy as possible. We’ll be taking a look at convolutional neural networks as our way of exploring methods that fine tune the granularity of clipping ranges for quantization. The input activation of a layer in our CNN undergoes convolution with multiple convolutional filters. Every convolutional filter can possess a unique range of values. Notice how in Figure 9.24, the range for Filter 1 is much smaller than that for Filter 3. Consequently, one distinguishing feature of quantization approaches is the precision with which the clipping range [α,β] is determined for the weights.\n\n\n\n\n\n\nFigure 9.24: Quantization granularity: variable ranges. Source: Gholami et al. (2021).\n\n\n\n\nLayerwise Quantization: This approach determines the clipping range by considering all of the weights in the convolutional filters of a layer. Then, the same clipping range is used for all convolutional filters. It’s the simplest to implement, and, as such, it often results in sub-optimal accuracy due the wide variety of differing ranges between filters. For example, a convolutional kernel with a narrower range of parameters loses its quantization resolution due to another kernel in the same layer having a wider range.\nGroupwise Quantization: This approach groups different channels inside a layer to calculate the clipping range. This method can be helpful when the distribution of parameters across a single convolution/activation varies a lot. In practice, this method was useful in Q-BERT (Shen et al. 2020) for quantizing Transformer (Vaswani et al. 2017) models that consist of fully-connected attention layers. The downside with this approach comes with the extra cost of accounting for different scaling factors.\nChannelwise Quantization: This popular method uses a fixed range for each convolutional filter that is independent of other channels. Because each channel is assigned a dedicated scaling factor, this method ensures a higher quantization resolution and often results in higher accuracy.\nSub-channelwise Quantization: Taking channelwise quantization to the extreme, this method determines the clipping range with respect to any groups of parameters in a convolution or fully-connected layer. It may result in considerable overhead since different scaling factors need to be taken into account when processing a single convolution or fully-connected layer.\n\n\nShen, Sheng, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoney, and Kurt Keutzer. 2020. “Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT.” Proceedings of the AAAI Conference on Artificial Intelligence 34 (05): 8815–21. https://doi.org/10.1609/aaai.v34i05.6409.\n\nVaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” Adv Neural Inf Process Syst 30.\nOf these, channelwise quantization is the current standard used for quantizing convolutional kernels, since it enables the adjustment of clipping ranges for each individual kernel with negligible overhead.\n\n\nStatic and Dynamic Quantization\nAfter determining the type and granularity of the clipping range, practitioners must decide when ranges are determined in their range calibration algorithms. There are two approaches to quantizing activations: static quantization and dynamic quantization.\nStatic quantization is the most frequently used approach. In this, the clipping range is pre-calculated and static during inference. It does not add any computational overhead, but, consequently, results in lower accuracy as compared to dynamic quantization. A popular method of implementing this is to run a series of calibration inputs to compute the typical range of activations (Jacob et al. 2018; Yao et al. 2021).\n\nJacob, Benoit, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. 2018. “Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2704–13.\n\nYao, Zhewei, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, et al. 2021. “Hawq-V3: Dyadic Neural Network Quantization.” In International Conference on Machine Learning, 11875–86. PMLR.\nDynamic quantization is an alternative approach which dynamically calculates the range for each activation map during runtime. The approach requires real-time computations which might have a very high overhead. By doing this, dynamic quantization often achieves the highest accuracy as the range is calculated specifically for each input.\nBetween the two, calculating the range dynamically usually is very costly, so most practitioners will often use static quantization instead.\n\n\n\n9.3.7 Techniques\nThe two prevailing techniques for quantizing models are Post Training Quantization and Quantization-Aware Training.\nPost Training Quantization: Post-training quantization (PTQ) is a quantization technique where the model is quantized after it has been trained. The model is trained in floating point and then weights and activations are quantized as a post-processing step. This is the simplest approach and does not require access to the training data. Unlike Quantization-Aware Training (QAT), PTQ sets weight and activation quantization parameters directly, making it low-overhead and suitable for limited or unlabeled data situations. However, not readjusting the weights after quantizing, especially in low-precision quantization can lead to very different behavior and thus lower accuracy. To tackle this, techniques like bias correction, equalizing weight ranges, and adaptive rounding methods have been developed. PTQ can also be applied in zero-shot scenarios, where no training or testing data are available. This method has been made even more efficient to benefit compute- and memory- intensive large language models. Recently, SmoothQuant, a training-free, accuracy-preserving, and general-purpose PTQ solution which enables 8-bit weight, 8-bit activation quantization for LLMs, has been developed, demonstrating up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy (Xiao et al. 2022).\nIn PTQ, a pretrained model undergoes a calibration process, as shown in Figure 9.25. Calibration involves using a separate dataset known as calibration data, a specific subset of the training data reserved for quantization to help find the appropriate clipping ranges and scaling factors.\n\n\n\n\n\n\nFigure 9.25: Post-Training Quantization and calibration. Source: Gholami et al. (2021).\n\n\n\nQuantization-Aware Training: Quantization-aware training (QAT) is a fine-tuning of the PTQ model. The model is trained aware of quantization, allowing it to adjust for quantization effects. This produces better accuracy with quantized inference. Quantizing a trained neural network model with methods such as PTQ introduces perturbations that can deviate the model from its original convergence point. For instance, Krishnamoorthi showed that even with per-channel quantization, networks like MobileNet do not reach baseline accuracy with int8 Post Training Quantization (PTQ) and require Quantization-Aware Training (QAT) (Krishnamoorthi 2018).To address this, QAT retrains the model with quantized parameters, employing forward and backward passes in floating point but quantizing parameters after each gradient update. Handling the non-differentiable quantization operator is crucial; a widely used method is the Straight Through Estimator (STE), approximating the rounding operation as an identity function. While other methods and variations exist, STE remains the most commonly used due to its practical effectiveness. In QAT, a pretrained model is quantized and then finetuned using training data to adjust parameters and recover accuracy degradation, as shown in Figure 9.26. The calibration process is often conducted in parallel with the finetuning process for QAT.\n\n\n\n\n\n\nFigure 9.26: Quantization-Aware Training. Source: Gholami et al. (2021).\n\n\nGholami, Dong Kim, Mahoney Yao, and Keutzer. 2021. “A Survey of Quantization Methods for Efficient Neural Network Inference).” ArXiv Preprint. https://arxiv.org/abs/2103.13630.\n\n\nQuantization-Aware Training serves as a natural extension of Post-Training Quantization. Following the initial quantization performed by PTQ, QAT is used to further refine and fine-tune the quantized parameters - see how in Figure 9.27, the PTQ model undergoes an additional step, QAT. It involves a retraining process where the model is exposed to additional training iterations using the original data. This dynamic training approach allows the model to adapt and adjust its parameters, compensating for the performance degradation caused by quantization.\n\n\n\n\n\n\nFigure 9.27: PTQ and QAT. Source: “The Ultimate Guide to Deep Learning Model Quantization and Quantization-Aware Training” (n.d.).\n\n\n“The Ultimate Guide to Deep Learning Model Quantization and Quantization-Aware Training.” n.d. https://deci.ai/quantization-and-quantization-aware-training/.\n\n\nFigure 9.28 shows the relative accuracy of different models after PTQ and QAT. In almost all cases, QAT yields a better accuracy than PTQ. Consider for example EfficientNet b0. After PTQ, the accuracy drops from 76.85% to 72.06%. But when we apply QAT, the accuracy rebounds to 76.95% (with even a slight improvement over the original accuracy).\n\n\n\n\n\n\nFigure 9.28: Relative accuracies of PTQ and QAT. Source: Wu, Judd, and Isaev (2020).\n\n\n\n\n\n\n\n\n\n\n\n\nAspect\nPost Training Quantization\nQuantization-Aware Training\nDynamic Quantization\n\n\n\n\nPros\n\n\n\n\n\nSimplicity\n✓\n✗\n✗\n\n\nAccuracy Preservation\n✗\n✓\n✓\n\n\nAdaptability\n✗\n✗\n✓\n\n\nOptimized Performance\n✗\n✓\nPotentially\n\n\nCons\n\n\n\n\n\nAccuracy Degradation\n✓\n✗\nPotentially\n\n\nComputational Overhead\n✗\n✓\n✓\n\n\nImplementation Complexity\n✗\n✓\n✓\n\n\nTradeoffs\n\n\n\n\n\nSpeed vs. Accuracy\n✓\n✗\n✗\n\n\nAccuracy vs. Cost\n✗\n✓\n✗\n\n\nAdaptability vs. Overhead\n✗\n✗\n✓\n\n\n\n\n\n9.3.8 Weights vs. Activations\nWeight Quantization: Involves converting the continuous or high-precision weights of a model to lower-precision, such as converting Float32 weights to quantized INT8 (integer) weights - in Figure 9.29, weight quantization is taking place in the second step (red squares) when we multiply the inputs. This reduces the model size, thereby reducing the memory required to store the model and the computational resources needed to perform inference. For example, consider a weight matrix in a neural network layer with Float32 weights as [0.215, -1.432, 0.902, …]. Through weight quantization, these might be mapped to INT8 values like [27, -183, 115, …], significantly reducing the memory required to store them.\n\n\n\n\n\n\nFigure 9.29: Weight and activation quantization. Source: HarvardX.\n\n\n\nActivation Quantization: Involves quantizing the activation values (outputs of layers) during model inference. This can reduce the computational resources required during inference, but it introduces additional challenges in maintaining model accuracy due to the reduced precision of intermediate computations. For example, in a convolutional neural network (CNN), the activation maps (feature maps) produced by convolutional layers, originally in Float32, might be quantized to INT8 during inference to accelerate computation, especially on hardware optimized for integer arithmetic. Additionally, recent work has explored the use of Activation-aware Weight Quantization for LLM compression and acceleration, which involves protecting only 1% of the most important salient weights by observing the activations not weights (Lin et al. 2023).\n\n\n9.3.9 Trade-offs\nQuantization invariably introduces a trade-off between model size/performance and accuracy. While it significantly reduces the memory footprint and can accelerate inference, especially on hardware optimized for low-precision arithmetic, the reduced precision can degrade model accuracy.\nModel Size: A model with weights represented as Float32 being quantized to INT8 can theoretically reduce the model size by a factor of 4, enabling it to be deployed on devices with limited memory. The model size of large language models is developing at a faster pace than the GPU memory in recent years, leading to a big gap between the supply and demand for memory. Figure 9.30 illustrates the recent trend of the widening gap between model size (red line) and accelerator memory (yellow line). Quantization and model compression techniques can help bridge the gap\n\n\n\n\n\n\nFigure 9.30: Model size vs. accelerator memory. Source: Xiao et al. (2022).\n\n\nXiao, Seznec Lin, Demouth Wu, and Han. 2022. “SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.” ArXiv Preprint. https://arxiv.org/abs/2211.10438.\n\n\nInference Speed: Quantization can also accelerate inference, as lower-precision arithmetic is computationally less expensive. For example, certain hardware accelerators, like Google’s Edge TPU, are optimized for INT8 arithmetic and can perform inference significantly faster with INT8 quantized models compared to their floating-point counterparts. The reduction in memory from quantization helps reduce the amount of data transmission, saving up memory and speeding the process. Figure 9.31 compares the increase in throughput and the reduction in bandwidth memory for different data type on the NVIDIA Turing GPU.\n\n\n\n\n\n\nFigure 9.31: Benefits of lower precision data types. Source: Wu, Judd, and Isaev (2020).\n\n\nWu, Zhang Judd, and Micikevicius Isaev. 2020. “Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation).” ArXiv Preprint. https://arxiv.org/abs/2004.09602.\n\n\nAccuracy: The reduction in numerical precision post-quantization can lead to a degradation in model accuracy, which might be acceptable in certain applications (e.g., image classification) but not in others (e.g., medical diagnosis). Therefore, post-quantization, the model typically requires re-calibration or fine-tuning to mitigate accuracy loss. Furthermore, recent work has explored the use of Activation-aware Weight Quantization (Lin et al. 2023) which is based on the observation that protecting only 1% of salient weights can greatly reduce quantization error.\n\n\n9.3.10 Quantization and Pruning\nPruning and quantization work well together, and it’s been found that pruning doesn’t hinder quantization. In fact, pruning can help reduce quantization error. Intuitively, this is due to pruning reducing the number of weights to quantize, thereby reducing the accumulated error from quantization. For example, an unpruned AlexNet has 60 million weights to quantize whereas a pruned AlexNet only has 6.7 million weights to quantize. This significant drop in weights helps reduce the error between quantizing the unpruned AlexNet vs. the pruned AlexNet. Furthermore, recent work has found that quantization-aware pruning generates more computationally efficient models than either pruning or quantization alone; It typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization (Hawks et al. 2021).\n\n\n\n\n\n\nFigure 9.32: Accuracy vs. compression rate under different compression methods. Source: Han, Mao, and Dally (2015).\n\n\nHan, Song, Huizi Mao, and William J Dally. 2015. “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.” arXiv Preprint arXiv:1510.00149.\n\n\n\n\n9.3.11 Edge-aware Quantization\nQuantization not only reduces model size but also enables faster computations and draws less power, making it vital to edge development. Edge devices typically have tight resource constraints with compute, memory, and power, which are impossible to meet for many of the deep NN models of today. Furthermore, edge processors do not support floating point operations, making integer quantization particularly important for chips like GAP-8, a RISC-V SoC for edge inference with a dedicated CNN accelerator, which only support integer arithmetic.\nOne hardware platform utilizing quantization is the ARM Cortex-M group of 32-bit RISC ARM processor cores. They leverage fixed-point quantization with power of two scaling factors so that quantization and dequantization can be efficiently done by bit shifting. Additionally, Google Edge TPUs, Google’s emerging solution for running inference at the edge, is designed for small, low-powered devices and can only support 8-bit arithmetic. Many complex neural network models that could only be deployed on servers due to their high computational needs can now be run on edge devices thanks to recent advancements (e.g. quantization methods) in edge computing field.\nIn addition to being an indispensable technique for many edge processors, quantization has also brought noteworthy improvements to non-edge processors such as encouraging such processors to meet the Service Level Agreement (SLA) requirements such as 99th percentile latency.\nThus, quantization combined with efficient low-precision logic and dedicated deep learning accelerators, has been one crucial driving force for the evolution of such edge processors.\nVideo 9.1 is a lecture on quantization and the different quantization methods.\n\n\n\n\n\n\nVideo 9.1: Quantization", + "text": "9.3 Efficient Numerics Representation\nNumerics representation involves a myriad of considerations, including, but not limited to, the precision of numbers, their encoding formats, and the arithmetic operations facilitated. It invariably involves a rich array of different trade-offs, where practitioners are tasked with navigating between numerical accuracy and computational efficiency. For instance, while lower-precision numerics may offer the allure of reduced memory usage and expedited computations, they concurrently present challenges pertaining to numerical stability and potential degradation of model accuracy.\n\nMotivation\nThe imperative for efficient numerics representation arises, particularly as efficient model optimization alone falls short when adapting models for deployment on low-powered edge devices operating under stringent constraints.\nBeyond minimizing memory demands, the tremendous potential of efficient numerics representation lies in, but is not limited to, these fundamental ways. By diminishing computational intensity, efficient numerics can thereby amplify computational speed, allowing more complex models to compute on low-powered devices. Reducing the bit precision of weights and activations on heavily over-parameterized models enables condensation of model size for edge devices without significantly harming the model’s predictive accuracy. With the omnipresence of neural networks in models, efficient numerics has a unique advantage in leveraging the layered structure of NNs to vary numeric precision across layers, minimizing precision in resistant layers while preserving higher precision in sensitive layers.\nIn this section, we will dive into how practitioners can harness the principles of hardware-software co-design at the lowest levels of a model to facilitate compatibility with edge devices. Kicking off with an introduction to the numerics, we will examine its implications for device memory and computational complexity. Subsequently, we will embark on a discussion regarding the trade-offs entailed in adopting this strategy, followed by a deep dive into a paramount method of efficient numerics: quantization.\n\n\n9.3.1 The Basics\n\nTypes\nNumerical data, the bedrock upon which machine learning models stand, manifest in two primary forms. These are integers and floating point numbers.\nIntegers: Whole numbers, devoid of fractional components, integers (e.g., -3, 0, 42) are key in scenarios demanding discrete values. For instance, in ML, class labels in a classification task might be represented as integers, where “cat”, “dog”, and “bird” could be encoded as 0, 1, and 2 respectively.\nFloating-Point Numbers: Encompassing real numbers, floating-point numbers (e.g., -3.14, 0.01, 2.71828) afford the representation of values with fractional components. In ML model parameters, weights might be initialized with small floating-point values, such as 0.001 or -0.045, to commence the training process. Currently, there are 4 popular precision formats discussed below.\nVariable bit widths: Beyond the standard widths, research is ongoing into extremely low bit-width numerics, even down to binary or ternary representations. Extremely low bit-width operations can offer significant speedups and reduce power consumption even further. While challenges remain in maintaining model accuracy with such drastic quantization, advances continue to be made in this area.\n\n\nPrecision\nPrecision, delineating the exactness with which a number is represented, bifurcates typically into single, double, half and in recent years there have been a number of other precisions that have emerged to better support machine learning tasks efficiently on the underlying hardware.\nDouble Precision (Float64): Allocating 64 bits, double precision (e.g., 3.141592653589793) provides heightened accuracy, albeit demanding augmented memory and computational resources. In scientific computations, where precision is paramount, variables like π might be represented with Float64.\nSingle Precision (Float32): With 32 bits at its disposal, single precision (e.g., 3.1415927) strikes a balance between numerical accuracy and memory conservation. In ML, Float32 might be employed to store weights during training to maintain a reasonable level of precision.\nHalf Precision (Float16): Constrained to 16 bits, half precision (e.g., 3.14) curtails memory usage and can expedite computations, albeit sacrificing numerical accuracy and range. In ML, especially during inference on resource-constrained devices, Float16 might be utilized to reduce the model’s memory footprint.\nBfloat16: Brain Floating-Point Format or Bfloat16, also employs 16 bits but allocates them differently compared to FP16: 1 bit for the sign, 8 bits for the exponent (resulting in the same number range as in float32), and 7 bits for the fraction. This format, developed by Google, prioritizes a larger exponent range over precision, making it particularly useful in deep learning applications where the dynamic range is crucial.\nFigure 9.13 illustrates the differences between the three floating-point formats: Float32, Float16, and BFloat16.\n\n\n\n\n\n\nFigure 9.13: Three floating-point formats.\n\n\n\nInteger: Integer representations are made using 8, 4, and 2 bits. They are often used during the inference phase of neural networks, where the weights and activations of the model are quantized to these lower precisions. Integer representations are deterministic and offer significant speed and memory advantages over floating-point representations. For many inference tasks, especially on edge devices, the slight loss in accuracy due to quantization is often acceptable given the efficiency gains. An extreme form of integer numerics is for binary neural networks (BNNs), where weights and activations are constrained to one of two values: either +1 or -1.\nYou may refer back to Section 8.6.1 for a table comparison between the trade-offs of different numeric types.\n\n\nNumeric Encoding and Storage\nNumeric encoding, the art of transmuting numbers into a computer-amenable format, and their subsequent storage are critical for computational efficiency. For instance, floating-point numbers might be encoded using the IEEE 754 standard, which apportions bits among sign, exponent, and fraction components, thereby enabling the representation of a vast array of values with a single format. There are a few new IEEE floating point formats that have been defined specifically for AI workloads:\n\nbfloat16- A 16-bit floating point format introduced by Google. It has 8 bits for exponent, 7 bits for mantissa and 1 bit for sign. Offers a reduced precision compromise between 32-bit float and 8-bit integers. Supported on many hardware accelerators.\nposit - A configurable format that can represent different levels of precision based on exponent bits. It is more efficient than IEEE 754 binary floats. Has adjustable dynamic range and precision.\nFlexpoint - A format introduced by Intel that can dynamically adjust precision across layers or within a layer. Allows tuning precision to accuracy and hardware requirements.\nBF16ALT - A proposed 16-bit format by ARM as an alternative to bfloat16. Uses additional bit in exponent to prevent overflow/underflow.\nTF32 - Introduced by Nvidia for Ampere GPUs. Uses 10 bits for exponent instead of 8 bits like FP32. Improves model training performance while maintaining accuracy.\nFP8 - 8-bit floating point format that keeps 6 bits for mantissa and 2 bits for exponent. Enables better dynamic range than integers.\n\nThe key goals of these new formats are to provide lower precision alternatives to 32-bit floats for better computational efficiency and performance on AI accelerators while maintaining model accuracy. They offer different tradeoffs in terms of precision, range and implementation cost/complexity.\n\n\n\n9.3.2 Efficiency Benefits\nAs you learned in Section 8.6.2, numerical efficiency matters for machine learning workloads for a number of reasons. Efficient numerics is not just about reducing the bit-width of numbers but understanding the trade-offs between accuracy and efficiency. As machine learning models become more pervasive, especially in real-world, resource-constrained environments, the focus on efficient numerics will continue to grow. By thoughtfully selecting and leveraging the appropriate numeric precision, one can achieve robust model performance while optimizing for speed, memory, and energy.\n\n\n9.3.3 Numeric Representation Nuances\nThere are a number of nuances with numerical representations for ML that require us to have an understanding of both the theoretical and practical aspects of numerics representation, as well as a keen awareness of the specific requirements and constraints of the application domain.\n\nMemory Usage\nThe memory footprint of ML models, particularly those of considerable complexity and depth, can be substantial, thereby posing a significant challenge in both training and deployment phases. For instance, a deep neural network with 100 million parameters, represented using Float32 (32 bits or 4 bytes per parameter), would necessitate approximately 400 MB of memory just for storing the model weights. This does not account for additional memory requirements during training for storing gradients, optimizer states, and forward pass caches, which can further amplify the memory usage, potentially straining the resources on certain hardware, especially edge devices with limited memory capacity.\nThe choice of numeric representation further impacts memory usage and computational efficiency. For example, using Float64 for model weights would double the memory requirements compared to Float32, and could potentially increase computational time as well. For a weight matrix with dimensions [1000, 1000], Float64 would consume approximately 8MB of memory, while Float32 would reduce this to about 4MB. Thus, selecting an appropriate numeric format is crucial for optimizing both memory and computational efficiency.\n\n\nComputational Complexity\nNumerical precision directly impacts computational complexity, influencing the time and resources required to perform arithmetic operations. For example, operations using Float64 generally consume more computational resources than their Float32 or Float16 counterparts (see Figure 9.14). In the realm of ML, where models might need to process millions of operations (e.g., multiplications and additions in matrix operations during forward and backward passes), even minor differences in the computational complexity per operation can aggregate into a substantial impact on training and inference times. As shown in Figure 9.15, quantized models can be many times faster than their unquantized versions.\n\n\n\n\n\n\nFigure 9.14: Energy use by quantized operations. Source: Mark Horowitz, Stanford University.\n\n\n\n\n\n\n\n\n\nFigure 9.15: Speed of three different models in normal and quantized form.\n\n\n\nIn addition to pure runtimes, there is also a concern over energy efficiency. Not all numerical computations are created equal from the underlying hardware standpoint. Some numerical operations are more energy efficient than others. For example, Figure 9.16 below shows that integer addition is much more energy efficient than integer multiplication.\n\n\n\n\n\n\nFigure 9.16: Energy use by quantized operations. Source: Isscc (2014).\n\n\nIsscc. 2014. “Computing’s Energy Problem (and What We Can Do about It).” https://ieeexplore.ieee.org/document/6757323.\n\n\n\n\nHardware Compatibility\nEnsuring compatibility and optimized performance across diverse hardware platforms is another challenge in numerics representation. Different hardware, such as CPUs, GPUs, TPUs, and FPGAs, have varying capabilities and optimizations for handling different numeric precisions. For example, certain GPUs might be optimized for Float32 computations, while others might provide accelerations for Float16. Developing and optimizing ML models that can leverage the specific numerical capabilities of different hardware, while ensuring that the model maintains its accuracy and robustness, requires careful consideration and potentially additional development and testing efforts.\n\n\nPrecision and Accuracy Trade-offs\nThe trade-off between numerical precision and model accuracy is a nuanced challenge in numerics representation. Utilizing lower-precision numerics, such as Float16, might conserve memory and expedite computations but can also introduce issues like quantization error and reduced numerical range. For instance, training a model with Float16 might introduce challenges in representing very small gradient values, potentially impacting the convergence and stability of the training process. Furthermore, in certain applications, such as scientific simulations or financial computations, where high precision is paramount, the use of lower-precision numerics might not be permissible due to the risk of accruing significant errors.\n\n\nTrade-off Examples\nTo understand and appreciate the nuances, let’s consider some use case examples. Through these we will realize that the choice of numeric representation is not merely a technical decision but a strategic one, influencing the model’s predictive acumen, its computational demands, and its deployability across diverse computational environments. In this section we will look at a couple of examples to better understand the trade-offs with numerics and how they tie to the real world.\n\nAutonomous Vehicles\nIn the domain of autonomous vehicles, ML models are employed to interpret sensor data and make real-time decisions. The models must process high-dimensional data from various sensors (e.g., LiDAR, cameras, radar) and execute numerous computations within a constrained time frame to ensure safe and responsive vehicle operation. So the trade-offs here would include:\n\nMemory Usage: Storing and processing high-resolution sensor data, especially in floating-point formats, can consume substantial memory.\nComputational Complexity: Real-time processing demands efficient computations, where higher-precision numerics might impede the timely execution of control actions.\n\n\n\nMobile Health Applications\nMobile health applications often use ML models for tasks like activity recognition, health monitoring, or predictive analytics, operating within the resource-constrained environment of mobile devices. The trade-offs here would include:\n\nPrecision and Accuracy Trade-offs: Employing lower-precision numerics to conserve resources might impact the accuracy of health predictions or anomaly detections, which could have significant implications for user health and safety.\nHardware Compatibility: Models need to be optimized for diverse mobile hardware, ensuring efficient operation across a wide range of devices with varying numerical computation capabilities.\n\n\n\nHigh-Frequency Trading (HFT) Systems\nHFT systems leverage ML models to make rapid trading decisions based on real-time market data. These systems demand extremely low-latency responses to capitalize on short-lived trading opportunities.\n\nComputational Complexity: The models must process and analyze vast streams of market data with minimal latency, where even slight delays, potentially introduced by higher-precision numerics, can result in missed opportunities.\nPrecision and Accuracy Trade-offs: Financial computations often demand high numerical precision to ensure accurate pricing and risk assessments, posing challenges in balancing computational efficiency and numerical accuracy.\n\n\n\nEdge-Based Surveillance Systems\nSurveillance systems deployed on edge devices, like security cameras, use ML models for tasks like object detection, activity recognition, and anomaly detection, often operating under stringent resource constraints.\n\nMemory Usage: Storing pre-trained models and processing video feeds in real-time demands efficient memory usage, which can be challenging with high-precision numerics.\nHardware Compatibility: Ensuring that models can operate efficiently on edge devices with varying hardware capabilities and optimizations for different numeric precisions is crucial for widespread deployment.\n\n\n\nScientific Simulations\nML models are increasingly being utilized in scientific simulations, such as climate modeling or molecular dynamics simulations, to improve predictive capabilities and reduce computational demands.\n\nPrecision and Accuracy Trade-offs: Scientific simulations often require high numerical precision to ensure accurate and reliable results, which can conflict with the desire to reduce computational demands via lower-precision numerics.\nComputational Complexity: The models must manage and process complex, high-dimensional simulation data efficiently to ensure timely results and enable large-scale or long-duration simulations.\n\nThese examples illustrate diverse scenarios where the challenges of numerics representation in ML models are prominently manifested. Each system presents a unique set of requirements and constraints, necessitating tailored strategies and solutions to navigate the challenges of memory usage, computational complexity, precision-accuracy trade-offs, and hardware compatibility.\n\n\n\n\n9.3.4 Quantization\nQuantization is prevalent in various scientific and technological domains, and it essentially involves the mapping or constraining of a continuous set or range into a discrete counterpart to minimize the number of bits required.\n\nInitial Breakdown\nWe begin our foray into quantization with a brief analysis of one important use for quantization.\nIn signal processing, the continuous sine wave (shown in Figure 9.17) can be quantized into discrete values through a process known as sampling. This is a fundamental concept in digital signal processing and is crucial for converting analog signals (like the continuous sine wave) into a digital form that can be processed by computers. The sine wave is a prevalent example due to its periodic and smooth nature, making it a useful tool for explaining concepts like frequency, amplitude, phase, and, of course, quantization.\n\n\n\n\n\n\nFigure 9.17: Sine Wave.\n\n\n\nIn the quantized version shown in Figure 9.18, the continuous sine wave (Figure 9.17) is sampled at regular intervals (in this case, every \\(\\frac{\\pi}{4}\\) radians), and only these sampled values are represented in the digital version of the signal. The step-wise lines between the points show one way to represent the quantized signal in a piecewise-constant form. This is a simplified example of how analog-to-digital conversion works, where a continuous signal is mapped to a discrete set of values, enabling it to be represented and processed digitally.\n\n\n\n\n\n\nFigure 9.18: Quantized Sine Wave.\n\n\n\nReturning to the context of Machine Learning (ML), quantization refers to the process of constraining the possible values that numerical parameters (such as weights and biases) can take to a discrete set, thereby reducing the precision of the parameters and consequently, the model’s memory footprint. When properly implemented, quantization can reduce model size by up to 4x and improve inference latency and throughput by up to 2-3x. Figure 9.19 illustrates the impact that quantization has on different models’ sizes: for example, an Image Classification model like ResNet-v2 can be compressed from 180MB down to 45MB with 8-bit quantization. There is typically less than 1% loss in model accuracy from well tuned quantization. Accuracy can often be recovered by re-training the quantized model with quantization-aware training techniques. Therefore, this technique has emerged to be very important in deploying ML models to resource-constrained environments, such as mobile devices, IoT devices, and edge computing platforms, where computational resources (memory and processing power) are limited.\n\n\n\n\n\n\nFigure 9.19: Effect of quantization on model sizes. Source: HarvardX.\n\n\n\nThere are several dimensions to quantization such as uniformity, stochasticity (or determinism), symmetry, granularity (across layers/channels/groups or even within channels), range calibration considerations (static vs dynamic), and fine-tuning methods (QAT, PTQ, ZSQ). We examine these below.\n\n\n\n9.3.5 Types\n\nUniform Quantization\nUniform quantization involves mapping continuous or high-precision values to a lower-precision representation using a uniform scale. This means that the interval between each possible quantized value is consistent. For example, if weights of a neural network layer are quantized to 8-bit integers (values between 0 and 255), a weight with a floating-point value of 0.56 might be mapped to an integer value of 143, assuming a linear mapping between the original and quantized scales. Due to its use of integer or fixed-point math pipelines, this form of quantization allows computation on the quantized domain without the need to dequantize beforehand.\nThe process for implementing uniform quantization starts with choosing a range of real numbers to be quantized. The next step is to select a quantization function and map the real values to the integers representable by the bit-width of the quantized representation. For instance, a popular choice for a quantization function is:\n\\[\nQ(r)=Int(r/S) - Z\n\\]\nwhere \\(Q\\) is the quantization operator, \\(r\\) is a real valued input (in our case, an activation or weight), \\(S\\) is a real valued scaling factor, and \\(Z\\) is an integer zero point. The Int function maps a real value to an integer value through a rounding operation. Through this function, we have effectively mapped real values \\(r\\) to some integer values, resulting in quantized levels which are uniformly spaced.\nWhen the need arises for practitioners to retrieve the original higher precision values, real values \\(r\\) can be recovered from quantized values through an operation known as dequantization. In the example above, this would mean performing the following operation on our quantized value:\n\\[\n\\bar{r} = S(Q(r) + Z)\n\\]\nAs discussed, some precision in the real value is lost by quantization. In this case, the recovered value \\(\\bar{r}\\) will not exactly match \\(r\\) due to the rounding operation. This is an important tradeoff to note; however, in many successful uses of quantization, the loss of precision can be negligible and the test accuracy remains high. Despite this, uniform quantization continues to be the current de-facto choice due to its simplicity and efficient mapping to hardware.\n\n\nNon-uniform Quantization\nNon-uniform quantization, on the other hand, does not maintain a consistent interval between quantized values. This approach might be used to allocate more possible discrete values in regions where the parameter values are more densely populated, thereby preserving more detail where it is most needed. For instance, in bell-shaped distributions of weights with long tails, a set of weights in a model predominantly lies within a certain range; thus, more quantization levels might be allocated to that range to preserve finer details, enabling us to better capture information. However, one major weakness of non-uniform quantization is that it requires dequantization before higher precision computations due to its non-uniformity, restricting its ability to accelerate computation compared to uniform quantization.\nTypically, a rule-based non-uniform quantization uses a logarithmic distribution of exponentially increasing steps and levels as opposed to linearly. Another popular branch lies in binary-code-based quantization where real number vectors are quantized into binary vectors with a scaling factor. Notably, there is no closed form solution for minimizing errors between the real value and non-uniformly quantized value, so most quantizations in this field rely on heuristic solutions. For instance, recent work by Xu et al. (2018) formulates non-uniform quantization as an optimization problem where the quantization steps/levels in quantizer \\(Q\\) are adjusted to minimize the difference between the original tensor and quantized counterpart.\n\nXu, Chen, Jianqiang Yao, Zhouchen Lin, Wenwu Ou, Yuanbin Cao, Zhirong Wang, and Hongbin Zha. 2018. “Alternating Multi-Bit Quantization for Recurrent Neural Networks.” In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https://openreview.net/forum?id=S19dR9x0b.\n\\[\n\\min_Q ||Q(r)-r||^2\n\\]\nFurthermore, learnable quantizers can be jointly trained with model parameters, and the quantization steps/levels are generally trained with iterative optimization or gradient descent. Additionally, clustering has been used to alleviate information loss from quantization. While capable of capturing higher levels of detail, non-uniform quantization schemes can be difficult to deploy efficiently on general computation hardware, making it less-preferred to methods which use uniform quantization.\n\n\n\n\n\n\nFigure 9.20: Quantization uniformity. Source: Gholami et al. (2021).\n\n\n\n\n\nStochastic Quantization\nUnlike the two previous approaches which generate deterministic mappings, there is some work exploring the idea of stochastic quantization for quantization-aware training and reduced precision training. This approach maps floating numbers up or down with a probability associated to the magnitude of the weight update. The hope generated by high level intuition is that such a probabilistic approach may allow a neural network to explore more, as compared to deterministic quantization. Supposedly, enabling a stochastic rounding may allow neural networks to escape local optimums, thereby updating its parameters. Below are two example stochastic mapping functions:\n\n\n\n\n\n\n\nFigure 9.21: Integer vs Binary quantization functions.\n\n\n\n\n\nZero Shot Quantization\nZero-shot quantization refers to the process of converting a full-precision deep learning model directly into a low-precision, quantized model without the need for any retraining or fine-tuning on the quantized model. The primary advantage of this approach is its efficiency, as it eliminates the often time-consuming and resource-intensive process of retraining a model post-quantization. By leveraging techniques that anticipate and minimize quantization errors, zero-shot quantization maintains the model’s original accuracy even after reducing its numerical precision. It is particularly useful for Machine Learning as a Service (MLaaS) providers aiming to expedite the deployment of their customer’s workloads without having to access their datasets.\n\n\n\n9.3.6 Calibration\nCalibration is the process of selecting the most effective clipping range [\\(\\alpha\\), \\(\\beta\\)] for weights and activations to be quantized to. For example, consider quantizing activations that originally have a floating-point range between -6 and 6 to 8-bit integers. If you just take the minimum and maximum possible 8-bit integer values (-128 to 127) as your quantization range, it might not be the most effective. Instead, calibration would involve passing a representative dataset then use this observed range for quantization.\nThere are many calibration methods but a few commonly used include:\n\nMax: Use the maximum absolute value seen during calibration. However, this method is susceptible to outlier data. Notice how in Figure 9.22, we have an outlier cluster around 2.1, while the rest are clustered around smaller values.\nEntropy: Use KL divergence to minimize information loss between the original floating-point values and values that could be represented by the quantized format. This is the default method used by TensorRT.\nPercentile: Set the range to a percentile of the distribution of absolute values seen during calibration. For example, 99% calibration would clip 1% of the largest magnitude values.\n\n\n\n\n\n\n\nFigure 9.22: Input activations to layer 3 in ResNet50. Source: @Wu, Judd, and Isaev (2020).\n\n\n\nImportantly, the quality of calibration can make a difference between a quantized model that retains most of its accuracy and one that degrades significantly. Hence, it’s an essential step in the quantization process. When choosing a calibration range, there are two types: symmetric and asymmetric.\n\nSymmetric Quantization\nSymmetric quantization maps real values to a symmetrical clipping range centered around 0. This involves choosing a range [\\(\\alpha\\), \\(\\beta\\)] where \\(\\alpha = -\\beta\\). For example, one symmetrical range would be based on the min/max values of the real values such that:\n\\[\n\\alpha = \\beta = max(abs(r_{max}), abs(r_{min}))\n\\]\nSymmetric clipping ranges are the most widely adopted in practice as they have the advantage of easier implementation. In particular, the mapping of zero to zero in the clipping range (sometimes called “zeroing out of the zero point”) can lead to reduction in computational cost during inference (Wu, Judd, and Isaev 2020).\n\n\nAsymmetric Quantization\nAsymmetric quantization maps real values to an asymmetrical clipping range that isn’t necessarily centered around 0, as shown in Figure 9.23 on the right. It involves choosing a range [\\(\\alpha\\), \\(\\beta\\)] where \\(\\alpha \\neq -\\beta\\). For example, selecting a range based on the minimum and maximum real values, or where \\(\\alpha = r_{min}\\) and \\(\\beta = r_{max}\\), creates an asymmetric range. Typically, asymmetric quantization produces tighter clipping ranges compared to symmetric quantization, which is important when target weights and activations are imbalanced, e.g., the activation after the ReLU always has non-negative values. Despite producing tighter clipping ranges, asymmetric quantization is less preferred to symmetric quantization as it doesn’t always zero out the real value zero.\n\n\n\n\n\n\nFigure 9.23: Quantization (a)symmetry. Source: Gholami et al. (2021).\n\n\n\n\n\nGranularity\nUpon deciding the type of clipping range, it is essential to tighten the range to allow a model to retain as much of its accuracy as possible. We’ll be taking a look at convolutional neural networks as our way of exploring methods that fine tune the granularity of clipping ranges for quantization. The input activation of a layer in our CNN undergoes convolution with multiple convolutional filters. Every convolutional filter can possess a unique range of values. Notice how in Figure 9.24, the range for Filter 1 is much smaller than that for Filter 3. Consequently, one distinguishing feature of quantization approaches is the precision with which the clipping range [α,β] is determined for the weights.\n\n\n\n\n\n\nFigure 9.24: Quantization granularity: variable ranges. Source: Gholami et al. (2021).\n\n\n\n\nLayerwise Quantization: This approach determines the clipping range by considering all of the weights in the convolutional filters of a layer. Then, the same clipping range is used for all convolutional filters. It’s the simplest to implement, and, as such, it often results in sub-optimal accuracy due the wide variety of differing ranges between filters. For example, a convolutional kernel with a narrower range of parameters loses its quantization resolution due to another kernel in the same layer having a wider range.\nGroupwise Quantization: This approach groups different channels inside a layer to calculate the clipping range. This method can be helpful when the distribution of parameters across a single convolution/activation varies a lot. In practice, this method was useful in Q-BERT (Shen et al. 2020) for quantizing Transformer (Vaswani et al. 2017) models that consist of fully-connected attention layers. The downside with this approach comes with the extra cost of accounting for different scaling factors.\nChannelwise Quantization: This popular method uses a fixed range for each convolutional filter that is independent of other channels. Because each channel is assigned a dedicated scaling factor, this method ensures a higher quantization resolution and often results in higher accuracy.\nSub-channelwise Quantization: Taking channelwise quantization to the extreme, this method determines the clipping range with respect to any groups of parameters in a convolution or fully-connected layer. It may result in considerable overhead since different scaling factors need to be taken into account when processing a single convolution or fully-connected layer.\n\n\nShen, Sheng, Zhen Dong, Jiayu Ye, Linjian Ma, Zhewei Yao, Amir Gholami, Michael W. Mahoney, and Kurt Keutzer. 2020. “Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT.” Proceedings of the AAAI Conference on Artificial Intelligence 34 (05): 8815–21. https://doi.org/10.1609/aaai.v34i05.6409.\n\nVaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” Adv Neural Inf Process Syst 30.\nOf these, channelwise quantization is the current standard used for quantizing convolutional kernels, since it enables the adjustment of clipping ranges for each individual kernel with negligible overhead.\n\n\nStatic and Dynamic Quantization\nAfter determining the type and granularity of the clipping range, practitioners must decide when ranges are determined in their range calibration algorithms. There are two approaches to quantizing activations: static quantization and dynamic quantization.\nStatic quantization is the most frequently used approach. In this, the clipping range is pre-calculated and static during inference. It does not add any computational overhead, but, consequently, results in lower accuracy as compared to dynamic quantization. A popular method of implementing this is to run a series of calibration inputs to compute the typical range of activations (Jacob et al. 2018; Yao et al. 2021).\n\nJacob, Benoit, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. 2018. “Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2704–13.\n\nYao, Zhewei, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, et al. 2021. “Hawq-V3: Dyadic Neural Network Quantization.” In International Conference on Machine Learning, 11875–86. PMLR.\nDynamic quantization is an alternative approach which dynamically calculates the range for each activation map during runtime. The approach requires real-time computations which might have a very high overhead. By doing this, dynamic quantization often achieves the highest accuracy as the range is calculated specifically for each input.\nBetween the two, calculating the range dynamically usually is very costly, so most practitioners will often use static quantization instead.\n\n\n\n9.3.7 Techniques\nThe two prevailing techniques for quantizing models are Post Training Quantization and Quantization-Aware Training.\nPost Training Quantization: Post-training quantization (PTQ) is a quantization technique where the model is quantized after it has been trained. The model is trained in floating point and then weights and activations are quantized as a post-processing step. This is the simplest approach and does not require access to the training data. Unlike Quantization-Aware Training (QAT), PTQ sets weight and activation quantization parameters directly, making it low-overhead and suitable for limited or unlabeled data situations. However, not readjusting the weights after quantizing, especially in low-precision quantization can lead to very different behavior and thus lower accuracy. To tackle this, techniques like bias correction, equalizing weight ranges, and adaptive rounding methods have been developed. PTQ can also be applied in zero-shot scenarios, where no training or testing data are available. This method has been made even more efficient to benefit compute- and memory- intensive large language models. Recently, SmoothQuant, a training-free, accuracy-preserving, and general-purpose PTQ solution which enables 8-bit weight, 8-bit activation quantization for LLMs, has been developed, demonstrating up to 1.56x speedup and 2x memory reduction for LLMs with negligible loss in accuracy (Xiao et al. 2022).\nIn PTQ, a pretrained model undergoes a calibration process, as shown in Figure 9.25. Calibration involves using a separate dataset known as calibration data, a specific subset of the training data reserved for quantization to help find the appropriate clipping ranges and scaling factors.\n\n\n\n\n\n\nFigure 9.25: Post-Training Quantization and calibration. Source: Gholami et al. (2021).\n\n\n\nQuantization-Aware Training: Quantization-aware training (QAT) is a fine-tuning of the PTQ model. The model is trained aware of quantization, allowing it to adjust for quantization effects. This produces better accuracy with quantized inference. Quantizing a trained neural network model with methods such as PTQ introduces perturbations that can deviate the model from its original convergence point. For instance, Krishnamoorthi showed that even with per-channel quantization, networks like MobileNet do not reach baseline accuracy with int8 Post Training Quantization (PTQ) and require Quantization-Aware Training (QAT) (Krishnamoorthi 2018).To address this, QAT retrains the model with quantized parameters, employing forward and backward passes in floating point but quantizing parameters after each gradient update. Handling the non-differentiable quantization operator is crucial; a widely used method is the Straight Through Estimator (STE), approximating the rounding operation as an identity function. While other methods and variations exist, STE remains the most commonly used due to its practical effectiveness. In QAT, a pretrained model is quantized and then finetuned using training data to adjust parameters and recover accuracy degradation, as shown in Figure 9.26. The calibration process is often conducted in parallel with the finetuning process for QAT.\n\n\n\n\n\n\nFigure 9.26: Quantization-Aware Training. Source: Gholami et al. (2021).\n\n\nGholami, Dong Kim, Mahoney Yao, and Keutzer. 2021. “A Survey of Quantization Methods for Efficient Neural Network Inference).” ArXiv Preprint. https://arxiv.org/abs/2103.13630.\n\n\nQuantization-Aware Training serves as a natural extension of Post-Training Quantization. Following the initial quantization performed by PTQ, QAT is used to further refine and fine-tune the quantized parameters - see how in Figure 9.27, the PTQ model undergoes an additional step, QAT. It involves a retraining process where the model is exposed to additional training iterations using the original data. This dynamic training approach allows the model to adapt and adjust its parameters, compensating for the performance degradation caused by quantization.\n\n\n\n\n\n\nFigure 9.27: PTQ and QAT. Source: “The Ultimate Guide to Deep Learning Model Quantization and Quantization-Aware Training” (n.d.).\n\n\n“The Ultimate Guide to Deep Learning Model Quantization and Quantization-Aware Training.” n.d. https://deci.ai/quantization-and-quantization-aware-training/.\n\n\nFigure 9.28 shows the relative accuracy of different models after PTQ and QAT. In almost all cases, QAT yields a better accuracy than PTQ. Consider for example EfficientNet b0. After PTQ, the accuracy drops from 76.85% to 72.06%. But when we apply QAT, the accuracy rebounds to 76.95% (with even a slight improvement over the original accuracy).\n\n\n\n\n\n\nFigure 9.28: Relative accuracies of PTQ and QAT. Source: Wu, Judd, and Isaev (2020).\n\n\n\n\n\n\n\n\n\n\n\n\nAspect\nPost Training Quantization\nQuantization-Aware Training\nDynamic Quantization\n\n\n\n\nPros\n\n\n\n\n\nSimplicity\n✓\n✗\n✗\n\n\nAccuracy Preservation\n✗\n✓\n✓\n\n\nAdaptability\n✗\n✗\n✓\n\n\nOptimized Performance\n✗\n✓\nPotentially\n\n\nCons\n\n\n\n\n\nAccuracy Degradation\n✓\n✗\nPotentially\n\n\nComputational Overhead\n✗\n✓\n✓\n\n\nImplementation Complexity\n✗\n✓\n✓\n\n\nTradeoffs\n\n\n\n\n\nSpeed vs. Accuracy\n✓\n✗\n✗\n\n\nAccuracy vs. Cost\n✗\n✓\n✗\n\n\nAdaptability vs. Overhead\n✗\n✗\n✓\n\n\n\n\n\n9.3.8 Weights vs. Activations\nWeight Quantization: Involves converting the continuous or high-precision weights of a model to lower-precision, such as converting Float32 weights to quantized INT8 (integer) weights - in Figure 9.29, weight quantization is taking place in the second step (red squares) when we multiply the inputs. This reduces the model size, thereby reducing the memory required to store the model and the computational resources needed to perform inference. For example, consider a weight matrix in a neural network layer with Float32 weights as [0.215, -1.432, 0.902, …]. Through weight quantization, these might be mapped to INT8 values like [27, -183, 115, …], significantly reducing the memory required to store them.\n\n\n\n\n\n\nFigure 9.29: Weight and activation quantization. Source: HarvardX.\n\n\n\nActivation Quantization: Involves quantizing the activation values (outputs of layers) during model inference. This can reduce the computational resources required during inference, but it introduces additional challenges in maintaining model accuracy due to the reduced precision of intermediate computations. For example, in a convolutional neural network (CNN), the activation maps (feature maps) produced by convolutional layers, originally in Float32, might be quantized to INT8 during inference to accelerate computation, especially on hardware optimized for integer arithmetic. Additionally, recent work has explored the use of Activation-aware Weight Quantization for LLM compression and acceleration, which involves protecting only 1% of the most important salient weights by observing the activations not weights (Lin et al. 2023).\n\n\n9.3.9 Trade-offs\nQuantization invariably introduces a trade-off between model size/performance and accuracy. While it significantly reduces the memory footprint and can accelerate inference, especially on hardware optimized for low-precision arithmetic, the reduced precision can degrade model accuracy.\nModel Size: A model with weights represented as Float32 being quantized to INT8 can theoretically reduce the model size by a factor of 4, enabling it to be deployed on devices with limited memory. The model size of large language models is developing at a faster pace than the GPU memory in recent years, leading to a big gap between the supply and demand for memory. Figure 9.30 illustrates the recent trend of the widening gap between model size (red line) and accelerator memory (yellow line). Quantization and model compression techniques can help bridge the gap\n\n\n\n\n\n\nFigure 9.30: Model size vs. accelerator memory. Source: Xiao et al. (2022).\n\n\nXiao, Seznec Lin, Demouth Wu, and Han. 2022. “SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.” ArXiv Preprint. https://arxiv.org/abs/2211.10438.\n\n\nInference Speed: Quantization can also accelerate inference, as lower-precision arithmetic is computationally less expensive. For example, certain hardware accelerators, like Google’s Edge TPU, are optimized for INT8 arithmetic and can perform inference significantly faster with INT8 quantized models compared to their floating-point counterparts. The reduction in memory from quantization helps reduce the amount of data transmission, saving up memory and speeding the process. Figure 9.31 compares the increase in throughput and the reduction in bandwidth memory for different data type on the NVIDIA Turing GPU.\n\n\n\n\n\n\nFigure 9.31: Benefits of lower precision data types. Source: Wu, Judd, and Isaev (2020).\n\n\nWu, Zhang Judd, and Micikevicius Isaev. 2020. “Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation).” ArXiv Preprint. https://arxiv.org/abs/2004.09602.\n\n\nAccuracy: The reduction in numerical precision post-quantization can lead to a degradation in model accuracy, which might be acceptable in certain applications (e.g., image classification) but not in others (e.g., medical diagnosis). Therefore, post-quantization, the model typically requires re-calibration or fine-tuning to mitigate accuracy loss. Furthermore, recent work has explored the use of Activation-aware Weight Quantization (Lin et al. 2023) which is based on the observation that protecting only 1% of salient weights can greatly reduce quantization error.\n\n\n9.3.10 Quantization and Pruning\nPruning and quantization work well together, and it’s been found that pruning doesn’t hinder quantization. In fact, pruning can help reduce quantization error. Intuitively, this is due to pruning reducing the number of weights to quantize, thereby reducing the accumulated error from quantization. For example, an unpruned AlexNet has 60 million weights to quantize whereas a pruned AlexNet only has 6.7 million weights to quantize. This significant drop in weights helps reduce the error between quantizing the unpruned AlexNet vs. the pruned AlexNet. Furthermore, recent work has found that quantization-aware pruning generates more computationally efficient models than either pruning or quantization alone; It typically performs similar to or better in terms of computational efficiency compared to other neural architecture search techniques like Bayesian optimization (Hawks et al. 2021).\n\n\n\n\n\n\nFigure 9.32: Accuracy vs. compression rate under different compression methods. Source: Han, Mao, and Dally (2015).\n\n\nHan, Song, Huizi Mao, and William J Dally. 2015. “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.” arXiv Preprint arXiv:1510.00149.\n\n\n\n\n9.3.11 Edge-aware Quantization\nQuantization not only reduces model size but also enables faster computations and draws less power, making it vital to edge development. Edge devices typically have tight resource constraints with compute, memory, and power, which are impossible to meet for many of the deep NN models of today. Furthermore, edge processors do not support floating point operations, making integer quantization particularly important for chips like GAP-8, a RISC-V SoC for edge inference with a dedicated CNN accelerator, which only support integer arithmetic.\nOne hardware platform utilizing quantization is the ARM Cortex-M group of 32-bit RISC ARM processor cores. They leverage fixed-point quantization with power of two scaling factors so that quantization and dequantization can be efficiently done by bit shifting. Additionally, Google Edge TPUs, Google’s emerging solution for running inference at the edge, is designed for small, low-powered devices and can only support 8-bit arithmetic. Many complex neural network models that could only be deployed on servers due to their high computational needs can now be run on edge devices thanks to recent advancements (e.g. quantization methods) in edge computing field.\nIn addition to being an indispensable technique for many edge processors, quantization has also brought noteworthy improvements to non-edge processors such as encouraging such processors to meet the Service Level Agreement (SLA) requirements such as 99th percentile latency.\nThus, quantization combined with efficient low-precision logic and dedicated deep learning accelerators, has been one crucial driving force for the evolution of such edge processors.\nVideo 9.1 is a lecture on quantization and the different quantization methods.\n\n\n\n\n\n\nVideo 9.1: Quantization", "crumbs": [ "Training", "9  Model Optimizations" @@ -1016,7 +1016,7 @@ "href": "contents/hw_acceleration/hw_acceleration.html", "title": "10  AI Acceleration", "section": "", - "text": "10.1 Introduction\nMachine learning has emerged as a transformative technology across many industries, enabling systems to learn and improve from data. There is a growing demand for embedded ML solutions to deploy machine learning capabilities in real-world environments - where models are built into edge devices like smartphones, home appliances, and autonomous vehicles. However, these edge devices have limited computing resources compared to data center servers.\nSpecialized hardware acceleration enables high-performance machine learning on resource-constrained edge devices. Hardware acceleration refers to using custom silicon chips and architectures to offload compute-intensive ML operations from the main processor. In neural networks, the most intensive computations are the matrix multiplications during inference. Hardware accelerators can optimize these matrix operations, providing 10-100x speedups over general-purpose CPUs. This acceleration unlocks the ability to run advanced neural network models on devices with size, weight, and power constraints in real-time.\nThis chapter overviews hardware acceleration techniques for embedded machine learning and their design tradeoffs. Its goal is to equip readers with an essential background in embedded ML acceleration. This will enable informed hardware selection and software optimization to develop high-performance machine learning capabilities on edge devices.", + "text": "10.1 Introduction\nYou’ve probably noticed the growing demand for embedding machine learning into everyday devices—like the smartphones in our pockets, smart home appliances, and even autonomous vehicles. Bringing ML capabilities into these real-world environments is exciting, but it comes with its own set of challenges. Unlike powerful data center servers, these edge devices have limited computing resources, making it tricky to run complex models effectively.\nSpecialized hardware acceleration is the key to making high-performance machine learning possible on resource-limited edge devices. When we talk about hardware acceleration, we’re referring to the use of custom chips and architectures designed to handle the heavy lifting of ML operations, taking the burden off the main processor. In neural networks, some of the most demanding tasks involve matrix multiplications during inference. Hardware accelerators are built to optimize these operations, often delivering 10-100x speedups compared to general-purpose CPUs. This kind of acceleration is what makes it feasible to run advanced neural network models on devices that are constrained by size, weight, and power— and to do it all in real-time.\nIn this chapter, we’ll take a closer look at the different hardware acceleration techniques available for embedded machine learning and the tradeoffs that come with each option. The goal is to give you a solid understanding of how these techniques work, so you can make informed decisions when it comes to choosing the right hardware and optimizing your software. By the end, you’ll be well-equipped to develop high-performance machine learning capabilities on edge devices, even with their constraints.", "crumbs": [ "Training", "10  AI Acceleration" @@ -1027,7 +1027,7 @@ "href": "contents/hw_acceleration/hw_acceleration.html#background-and-basics", "title": "10  AI Acceleration", "section": "10.2 Background and Basics", - "text": "10.2 Background and Basics\n\n10.2.1 Historical Background\nThe origins of hardware acceleration date back to the 1960s, with the advent of floating point math co-processors to offload calculations from the main CPU. One early example was the Intel 8087 chip released in 1980 to accelerate floating point operations for the 8086 processor. This established the practice of using specialized processors to handle math-intensive workloads efficiently.\nIn the 1990s, the first graphics processing units (GPUs) emerged to process graphics pipelines for rendering and gaming rapidly. Nvidia’s GeForce 256 in 1999 was one of the earliest programmable GPUs capable of running custom software algorithms. GPUs exemplify domain-specific fixed-function accelerators and evolve into parallel programmable accelerators.\nIn the 2000s, GPUs were applied to general-purpose computing under GPGPU. Their high memory bandwidth and computational throughput made them well-suited for math-intensive workloads. This included breakthroughs in using GPUs to accelerate training of deep learning models such as AlexNet in 2012.\nIn recent years, Google’s Tensor Processing Units (TPUs) represent customized ASICs specifically architected for matrix multiplication in deep learning. During inference, their optimized tensor cores achieve higher TeraOPS/watt than CPUs or GPUs. Ongoing innovation includes model compression techniques like pruning and quantization to fit larger neural networks on edge devices.\nThis evolution demonstrates how hardware acceleration has focused on solving compute-intensive bottlenecks, from floating point math to graphics to matrix multiplication for ML. Understanding this history provides a crucial context for specialized AI accelerators today.\n\n\n10.2.2 The Need for Acceleration\nThe evolution of hardware acceleration is closely tied to the broader history of computing. In the early decades, chip design was governed by Moore’s Law and Dennard Scaling, which observed that the number of transistors on an integrated circuit doubled yearly, and their performance (speed) increased as transistors became smaller. At the same time, power density (power per unit area) remains constant. These two laws were held through the single-core era. Figure 10.1 shows the trends of different microprocessor metrics. As the figure denotes, Dennard Scaling fails around the mid-2000s; notice how the clock speed (frequency) remains almost constant even as the number of transistors keeps increasing.\nHowever, as Patterson and Hennessy (2016) describes, technological constraints eventually forced a transition to the multicore era, with chips containing multiple processing cores to deliver performance gains. Power limitations prevented further scaling, which led to “dark silicon” (Dark Silicon), where not all chip areas could be simultaneously active (Xiu 2019).\n\nPatterson, David A, and John L Hennessy. 2016. Computer Organization and Design ARM Edition: The Hardware Software Interface. Morgan kaufmann.\n\nXiu, Liming. 2019. “Time Moore: Exploiting Moore’s Law from the Perspective of Time.” IEEE Solid-State Circuits Mag. 11 (1): 39–55. https://doi.org/10.1109/mssc.2018.2882285.\nThe concept of dark silicon emerged as a consequence of these constraints. “Dark silicon” refers to portions of the chip that cannot be powered simultaneously due to thermal and power limitations. Essentially, as the density of transistors increased, the proportion of the chip that could be actively used without overheating or exceeding power budgets shrank.\nThis phenomenon meant that while chips had more transistors, not all could be operational simultaneously, limiting potential performance gains. This power crisis necessitated a shift to the accelerator era, with specialized hardware units tailored for specific tasks to maximize efficiency. The explosion in AI workloads further drove demand for customized accelerators. Enabling factors included new programming languages, software tools, and manufacturing advances.\n\n\n\n\n\n\nFigure 10.1: Microprocessor trends. Source: Karl Rupp.\n\n\n\nFundamentally, hardware accelerators are evaluated on performance, power, and silicon area (PPA)—the nature of the target application—whether memory-bound or compute-bound—heavily influences the design. For example, memory-bound workloads demand high bandwidth and low latency access, while compute-bound applications require maximal computational throughput.\n\n\n10.2.3 General Principles\nThe design of specialized hardware accelerators involves navigating complex tradeoffs between performance, power efficiency, silicon area, and workload-specific optimizations. This section outlines core considerations and methodologies for achieving an optimal balance based on application requirements and hardware constraints.\n\nPerformance Within Power Budgets\nPerformance refers to the throughput of computational work per unit of time, commonly measured in floating point operations per second (FLOPS) or frames per second (FPS). Higher performance enables completing more work, but power consumption rises with activity.\nHardware accelerators aim to maximize performance within set power budgets. This requires careful balancing of parallelism, the chip’s clock frequency, the operating voltage, workload optimization, and other techniques to maximize operations per watt.\n\nPerformance = Throughput * Efficiency\nThroughput ~= Parallelism * Clock Frequency\nEfficiency = Operations / Watt\n\nFor example, GPUs achieve high throughput via massively parallel architectures. However, their efficiency is lower than that of customized application-specific integrated circuits (ASICs) like Google’s TPU, which optimize for a specific workload.\n\n\nManaging Silicon Area and Costs\nChip area directly impacts manufacturing cost. Larger die sizes require more materials, lower yields, and higher defect rates. Multi-die packages help scale designs but add packaging complexity. Silicon area depends on:\n\nComputational resources - e.g., number of cores, memory, caches\nManufacturing process node - smaller transistors enable higher density\nProgramming model - programmed accelerators require more flexibility\n\nAccelerator design involves squeezing maximum performance within area constraints. Techniques like pruning and compression help fit larger models on the chip.\n\n\nWorkload-Specific Optimizations\nThe target workload dictates optimal accelerator architectures. Some of the key considerations include:\n\nMemory vs Compute boundedness: Memory-bound workloads require more memory bandwidth, while compute-bound apps need arithmetic throughput.\nData locality: Data movement should be minimized for efficiency. Near-compute memory helps.\nBit-level operations: Low precision datatypes like INT8/INT4 optimize compute density.\nData parallelism: Multiple replicated compute units allow parallel execution.\nPipelining: Overlapped execution of operations increases throughput.\n\nUnderstanding workload characteristics enables customized acceleration. For example, convolutional neural networks use sliding window operations optimally mapped to spatial arrays of processing elements.\nBy navigating these architectural tradeoffs, hardware accelerators can deliver massive performance gains and enable emerging applications in AI, graphics, scientific computing, and other domains.\n\n\nSustainable Hardware Design\nIn recent years, AI sustainability has become a pressing concern driven by two key factors - the exploding scale of AI workloads and their associated energy consumption.\nFirst, the size of AI models and datasets has rapidly grown. For example, based on OpenAI’s AI computing trends, the amount of computing used to train state-of-the-art models doubles every 3.5 months. This exponential growth requires massive computational resources in data centers.\nSecond, the energy usage of AI training and inference presents sustainability challenges. Data centers running AI applications consume substantial energy, contributing to high carbon emissions. It’s estimated that training a large AI model can have a carbon footprint of 626,000 pounds of CO2 equivalent, almost 5 times the lifetime emissions of an average car.\nAs a result, AI research and practice must prioritize energy efficiency and carbon impact alongside accuracy. There is an increasing focus on model efficiency, data center design, hardware optimization, and other solutions to improve sustainability. Striking a balance between AI progress and environmental responsibility has emerged as a key consideration and an area of active research across the field.\nThe scale of AI systems is expected to keep growing. Developing sustainable AI is crucial for managing the environmental footprint and enabling widespread beneficial deployment of this transformative technology.\nWe will learn about Sustainable AI in a later chapter, where we will discuss it in more detail.", + "text": "10.2 Background and Basics\n\n10.2.1 Historical Background\nThe origins of hardware acceleration date back to the 1960s, with the advent of floating point math co-processors to offload calculations from the main CPU. One early example was the Intel 8087 chip released in 1980 to accelerate floating point operations for the 8086 processor. This established the practice of using specialized processors to handle math-intensive workloads efficiently.\nIn the 1990s, the first graphics processing units (GPUs) emerged to process graphics pipelines for rendering and gaming rapidly. Nvidia’s GeForce 256 in 1999 was one of the earliest programmable GPUs capable of running custom software algorithms. GPUs exemplify domain-specific fixed-function accelerators and evolve into parallel programmable accelerators.\nIn the 2000s, GPUs were applied to general-purpose computing under GPGPU. Their high memory bandwidth and computational throughput made them well-suited for math-intensive workloads. This included breakthroughs in using GPUs to accelerate training of deep learning models such as AlexNet in 2012.\nIn recent years, Google’s Tensor Processing Units (TPUs) represent customized ASICs specifically architected for matrix multiplication in deep learning. During inference, their optimized tensor cores achieve higher TeraOPS/watt than CPUs or GPUs. Ongoing innovation includes model compression techniques like pruning and quantization to fit larger neural networks on edge devices.\nThis evolution demonstrates how hardware acceleration has focused on solving compute-intensive bottlenecks, from floating point math to graphics to matrix multiplication for ML. Understanding this history provides a crucial context for specialized AI accelerators today.\n\n\n10.2.2 The Need for Acceleration\nThe evolution of hardware acceleration is closely tied to the broader history of computing. Central to this history is the role of transistors, the fundamental building blocks of modern electronics. Transistors act as tiny switches that can turn on or off, enabling the complex computations that drive everything from simple calculators to advanced machine learning models. In the early decades, chip design was governed by Moore’s Law, which predicted that the number of transistors on an integrated circuit would double approximately every two years, and Dennard Scaling, which observed that as transistors became smaller, their performance (speed) increased, while power density (power per unit area) remained constant. These two laws were held through the single-core era. Figure 10.1 shows the trends of different microprocessor metrics. As the figure denotes, Dennard Scaling fails around the mid-2000s; notice how the clock speed (frequency) remains almost constant even as the number of transistors keeps increasing.\nHowever, as Patterson and Hennessy (2016) describes, technological constraints eventually forced a transition to the multicore era, with chips containing multiple processing cores to deliver performance gains. Power limitations prevented further scaling, which led to “dark silicon” (Dark Silicon), where not all chip areas could be simultaneously active (Xiu 2019).\n\nPatterson, David A, and John L Hennessy. 2016. Computer Organization and Design ARM Edition: The Hardware Software Interface. Morgan kaufmann.\n\nXiu, Liming. 2019. “Time Moore: Exploiting Moore’s Law from the Perspective of Time.” IEEE Solid-State Circuits Mag. 11 (1): 39–55. https://doi.org/10.1109/mssc.2018.2882285.\n“Dark silicon” refers to portions of the chip that cannot be powered simultaneously due to thermal and power limitations. Essentially, as the density of transistors increased, the proportion of the chip that could be actively used without overheating or exceeding power budgets shrank.\nThis phenomenon meant that while chips had more transistors, not all could be operational simultaneously, limiting potential performance gains. This power crisis necessitated a shift to the accelerator era, with specialized hardware units tailored for specific tasks to maximize efficiency. The explosion in AI workloads further drove demand for customized accelerators. Enabling factors included new programming languages, software tools, and manufacturing advances.\n\n\n\n\n\n\nFigure 10.1: Microprocessor trends. Source: Karl Rupp.\n\n\n\nFundamentally, hardware accelerators are evaluated on performance, power, and silicon area (PPA)—the nature of the target application—whether memory-bound or compute-bound—heavily influences the design. For example, memory-bound workloads demand high bandwidth and low latency access, while compute-bound applications require maximal computational throughput.\n\n\n10.2.3 General Principles\nThe design of specialized hardware accelerators involves navigating complex tradeoffs between performance, power efficiency, silicon area, and workload-specific optimizations. This section outlines core considerations and methodologies for achieving an optimal balance based on application requirements and hardware constraints.\n\nPerformance Within Power Budgets\nTo understand how to achieve the right balance between performance and power budgets, it’s important to first define a few key concepts that play a crucial role in this process. Performance broadly refers to the overall capability of a system to complete computational tasks effectively within given constraints. One of the key components of performance is throughput, which is the rate at which these tasks are processed, commonly measured in floating point operations per second (FLOPS) or frames per second (FPS). Throughput depends heavily on parallelism—the ability of the hardware to carry out multiple operations simultaneously—and clock frequency, which is the speed at which the processor cycles through these operations. Higher throughput typically leads to better performance, but it also increases power consumption as activity rises.\nSimply maximizing throughput is not enough; the efficiency of the hardware also matters. Efficiency is the measure of how many operations are performed per watt of power consumed, reflecting the relationship between computational work and energy use. In scenarios where power is a limiting factor, such as in edge devices, achieving high efficiency is critical. To help you remember how these concepts interconnect, consider the following relationships:\n\nPerformance = Throughput * Efficiency\nThroughput ~= Parallelism * Clock Frequency\nEfficiency = Operations / Watt\n\nHardware accelerators aim to maximize performance within set power budgets. This requires careful balancing of parallelism, the chip’s clock frequency, the operating voltage, workload optimization, and other techniques to maximize operations per watt.\nFor example, GPUs achieve high throughput via massively parallel architectures. However, their efficiency is lower than that of customized application-specific integrated circuits (ASICs) like Google’s TPU, which optimize for a specific workload.\n\n\nManaging Silicon Area and Costs\nThe size of a chip’s area has a direct impact on its manufacturing cost. To understand why, it helps to know a bit about the manufacturing process.\nChips are created from large, thin slices of semiconductor material known as wafers. During manufacturing, each wafer is divided into multiple smaller blocks called dies, with each die containing the circuitry for an individual chip. After the wafer is processed, it’s cut into these individual dies, which are then packaged to form the final chips used in electronic devices.\nLarger dies require more material and are more prone to defects, which can lower the yield—meaning fewer usable chips are produced from each wafer. While manufacturers can scale designs by combining multiple smaller dies into a single package (multi-die packages), this adds complexity and cost to the packaging and production process.\nThe amount of silicon area needed on a die depends on several factors:\n\nComputational resources - e.g., number of cores, memory, caches\nManufacturing process node - smaller transistors enable higher density\nProgramming model - programmed accelerators require more flexibility\n\nAccelerator design involves squeezing maximum performance within these silicon area constraints. Techniques like pruning and compression help fit larger models onto the chip without exceeding the available space.\n\n\nWorkload-Specific Optimizations\nDesigning effective hardware accelerators requires tailoring the architecture to the specific demands of the target workload. Different types of workloads—whether in AI, graphics, or robotics—have unique characteristics that dictate how the accelerator should be optimized.\nSome of the key considerations when optimizing hardware for specific workloads include:\n\nMemory vs Compute boundedness: Memory-bound workloads require more memory bandwidth, while compute-bound apps need arithmetic throughput.\nData locality: Data movement should be minimized for efficiency. Near-compute memory helps.\nBit-level operations: Low precision datatypes like INT8/INT4 optimize compute density.\nData parallelism: Multiple replicated compute units allow parallel execution.\nPipelining: Overlapped execution of operations increases throughput.\n\nUnderstanding workload characteristics enables customized acceleration. For example, convolutional neural networks use sliding window operations optimally mapped to spatial arrays of processing elements.\nBy understanding these architectural tradeoffs, designers can make informed decisions about the hardware accelerator’s architecture, ensuring that it delivers the best possible performance for its intended use.\n\n\nSustainable Hardware Design\nIn recent years, AI sustainability has become a pressing concern driven by two key factors - the exploding scale of AI workloads and their associated energy consumption.\nFirst, the size of AI models and datasets has rapidly grown. For example, based on OpenAI’s AI computing trends, the amount of computing used to train state-of-the-art models doubles every 3.5 months. This exponential growth requires massive computational resources in data centers.\nSecond, the energy usage of AI training and inference presents sustainability challenges. Data centers running AI applications consume substantial energy, contributing to high carbon emissions. It’s estimated that training a large AI model can have a carbon footprint of 626,000 pounds of CO2 equivalent, almost 5 times the lifetime emissions of an average car.\nTo address these challenges, sustainable hardware design focuses on optimizing energy efficiency without compromising performance. This involves developing specialized accelerators that minimize energy consumption while maximizing computational throughput.\nWe will learn about Sustainable AI in a later chapter, where we will discuss it in more detail.", "crumbs": [ "Training", "10  AI Acceleration" @@ -1038,7 +1038,7 @@ "href": "contents/hw_acceleration/hw_acceleration.html#sec-aihw", "title": "10  AI Acceleration", "section": "10.3 Accelerator Types", - "text": "10.3 Accelerator Types\nHardware accelerators can take on many forms. They can exist as a widget (like the Neural Engine in the Apple M1 chip) or as entire chips specially designed to perform certain tasks very well. This section will examine processors for machine learning workloads along the spectrum from highly specialized ASICs to more general-purpose CPUs. We first focus on custom hardware purpose-built for AI to understand the most extreme optimizations possible when design constraints are removed. This establishes a ceiling for performance and efficiency.\nWe then progressively consider more programmable and adaptable architectures, discussing GPUs and FPGAs. These make tradeoffs in customization to maintain flexibility. Finally, we cover general-purpose CPUs that sacrifice optimizations for a particular workload in exchange for versatile programmability across applications.\nBy structuring the analysis along this spectrum, we aim to illustrate the fundamental tradeoffs between utilization, efficiency, programmability, and flexibility in accelerator design. The optimal balance point depends on the constraints and requirements of the target application. This spectrum perspective provides a framework for reasoning about hardware choices for machine learning and the capabilities required at each level of specialization.\nFigure 10.2 illustrates the complex interplay between flexibility, performance, functional diversity, and area of architecture design. Notice how the ASIC is on the bottom-right corner, with minimal area, flexibility, and power consumption and maximal performance, due to its highly specialized application-specific nature. A key tradeoff is functional diversity vs performance: general purpose architectures can serve diverse applications but their application performance is degraded as compared to more customized architectures.\nThe progression begins with the most specialized option, ASICs purpose-built for AI, to ground our understanding in the maximum possible optimizations before expanding to more generalizable architectures. This structured approach aims to elucidate the accelerator design space.\n\n\n\n\n\n\nFigure 10.2: Design tradeoffs. Source: El-Rayis (2014).\n\n\nEl-Rayis, A. O. 2014. “Reconfigurable Architectures for the Next Generation of Mobile Device Telecommunications Systems.” : https://www.researchgate.net/publication/292608967.\n\n\n\n10.3.1 Application-Specific Integrated Circuits (ASICs)\nAn Application-Specific Integrated Circuit (ASIC) is a type of integrated circuit (IC) that is custom-designed for a specific application or workload rather than for general-purpose use. Unlike CPUs and GPUs, ASICs do not support multiple applications or workloads. Rather, they are optimized to perform a single task extremely efficiently. The Google TPU is an example of an ASIC.\nASICs achieve this efficiency by tailoring every aspect of the chip design - the underlying logic gates, electronic components, architecture, memory, I/O, and manufacturing process - specifically for the target application. This level of customization allows removing any unnecessary logic or functionality required for general computation. The result is an IC that maximizes performance and power efficiency on the desired workload. The efficiency gains from application-specific hardware are so substantial that these software-centric firms dedicate enormous engineering resources to designing customized ASICs.\nThe rise of more complex machine learning algorithms has made the performance advantages enabled by tailored hardware acceleration a key competitive differentiator, even for companies traditionally concentrated on software engineering. ASICs have become a high-priority investment for major cloud providers aiming to offer faster AI computation.\n\nAdvantages\nDue to their customized nature, ASICs provide significant benefits over general-purpose processors like CPUs and GPUs. The key advantages include the following.\n\nMaximized Performance and Efficiency\nThe most fundamental advantage of ASICs is maximizing performance and power efficiency by customizing the hardware architecture specifically for the target application. Every transistor and design aspect is optimized for the desired workload - no unnecessary logic or overhead is needed to support generic computation.\nFor example, Google’s Tensor Processing Units (TPUs) contain architectures tailored exactly for the matrix multiplication operations used in neural networks. To design the TPU ASICs, Google’s engineering teams need to define the chip specifications clearly, write the architecture description using Hardware Description Languages like Verilog, synthesize the design to map it to hardware components, and carefully place-and-route transistors and wires based on the fabrication process design rules. This complex design process, known as very-large-scale integration (VLSI), allows them to build an optimized IC for machine learning workloads.\nAs a result, TPU ASICs achieve over an order of magnitude higher efficiency in operations per watt than general-purpose GPUs on ML workloads by maximizing performance and minimizing power consumption through a full-stack custom hardware design.\n\n\nSpecialized On-Chip Memory\nASICs incorporate on-chip SRAM and caches specifically optimized to feed data to the computational units. For example, Apple’s M1 system-on-a-chip contains special low-latency SRAM to accelerate the performance of its Neural Engine machine learning hardware. Large local memory with high bandwidth enables data to be kept close to the processing elements. This provides tremendous speed advantages compared to off-chip DRAM access, which can be up to 100x slower.\nData locality and optimizing memory hierarchy are crucial for high throughput and low power. Table 10.1 shows “Numbers Everyone Should Know,” from Jeff Dean.\n\n\n\nTable 10.1: Latency comparison of operations in computing and networking.\n\n\n\n\n\n\n\n\n\n\nOperation\nLatency\nNotes\n\n\n\n\nL1 cache reference\n0.5 ns\n\n\n\nBranch mispredict\n5 ns\n\n\n\nL2 cache reference\n7 ns\n\n\n\nMutex lock/unlock\n25 ns\n\n\n\nMain memory reference\n100 ns\n\n\n\nCompress 1K bytes with Zippy\n3,000 ns\n3 us\n\n\nSend 1 KB bytes over 1 Gbps network\n10,000 ns\n10 us\n\n\nRead 4 KB randomly from SSD\n150,000 ns\n150 us\n\n\nRead 1 MB sequentially from memory\n250,000 ns\n250 us\n\n\nRound trip within same datacenter\n500,000 ns\n0.5 ms\n\n\nRead 1 MB sequentially from SSD\n1,000,000 ns\n1 ms\n\n\nDisk seek\n10,000,000 ns\n10 ms\n\n\nRead 1 MB sequentially from disk\n20,000,000 ns\n20 ms\n\n\nSend packet CA → Netherlands → CA\n150,000,000 ns\n150 ms\n\n\n\n\n\n\n\n\nCustom Datatypes and Operations\nUnlike general-purpose processors, ASICs can be designed to natively support custom datatypes like INT4 or bfloat16, which are widely used in ML models. For instance, Nvidia’s Ampere GPU architecture has dedicated bfloat16 Tensor Cores to accelerate AI workloads. Low-precision datatypes enable higher arithmetic density and performance. ASICs can also directly incorporate non-standard operations common in ML algorithms as primitive operations - for example, natively supporting activation functions like ReLU makes execution more efficient. Please refer to the Efficient Numeric Representations chapter for additional details.\n\n\nHigh Parallelism\nASIC architectures can leverage higher parallelism tuned for the target workload versus general-purpose CPUs or GPUs. More computational units tailored for the application mean more operations execute simultaneously. Highly parallel ASICs achieve tremendous throughput for data parallel workloads like neural network inference.\n\n\nAdvanced Process Nodes\nCutting-edge manufacturing processes allow more transistors to be packed into smaller die areas, increasing density. ASICs designed specifically for high-volume applications can better amortize the costs of cutting-edge process nodes.\n\n\n\nDisadvantages\n\nLong Design Timelines\nThe engineering process of designing and validating an ASIC can take 2-3 years. Synthesizing the architecture using hardware description languages, taping out the chip layout, and fabricating the silicon on advanced process nodes involve long development cycles. For example, to tape out a 7nm chip, teams need to define specifications carefully, write the architecture in HDL, synthesize the logic gates, place components, route all interconnections, and finalize the layout to send for fabrication. This very large-scale integration (VLSI) flow means ASIC design and manufacturing can traditionally take 2-5 years.\nThere are a few key reasons why the long design timelines of ASICs, often 2-3 years, can be challenging for machine learning workloads:\n\nML algorithms evolve rapidly: New model architectures, training techniques, and network optimizations are constantly emerging. For example, Transformers became hugely popular in NLP last few years. When an ASIC finishes tapeout, the optimal architecture for a workload may have changed.\nDatasets grow quickly: ASICs designed for certain model sizes or datatypes can become undersized relative to demand. For instance, natural language models are scaling exponentially with more data and parameters. A chip designed for BERT might not accommodate GPT-3.\nML applications change frequently: The industry focus shifts between computer vision, speech, NLP, recommender systems, etc. An ASIC optimized for image classification may have less relevance in a few years.\nFaster design cycles with GPUs/FPGAs: Programmable accelerators like GPUs can adapt much quicker by upgrading software libraries and frameworks. New algorithms can be deployed without hardware changes.\nTime-to-market needs: Getting a competitive edge in ML requires rapidly experimenting with and deploying new ideas. Waiting several years for an ASIC is different from fast iteration.\n\nThe pace of innovation in ML needs to be better matched to the multi-year timescale for ASIC development. Significant engineering efforts are required to extend ASIC lifespan through modular architectures, process scaling, model compression, and other techniques. However, the rapid evolution of ML makes fixed-function hardware challenging.\n\n\nHigh Non-Recurring Engineering Costs\nThe fixed costs of taking an ASIC from design to high-volume manufacturing can be very capital-intensive, often tens of millions of dollars. Photomask fabrication for taping out chips in advanced process nodes, packaging, and one-time engineering efforts is expensive. For instance, a 7nm chip tape-out alone could cost millions. The high non-recurring engineering (NRE) investment narrows ASIC viability to high-volume production use cases where the upfront cost can be amortized.\n\n\nComplex Integration and Programming\nASICs require extensive software integration work, including drivers, compilers, OS support, and debugging tools. They also need expertise in electrical and thermal packaging. Additionally, efficiently programming ASIC architectures can involve challenges like workload partitioning and scheduling across many parallel units. The customized nature necessitates significant integration efforts to turn raw hardware into fully operational accelerators.\nWhile ASICs provide massive efficiency gains on target applications by tailoring every aspect of the hardware design to one specific task, their fixed nature results in tradeoffs in flexibility and development costs compared to programmable accelerators, which must be weighed based on the application.\n\n\n\n\n10.3.2 Field-Programmable Gate Arrays (FPGAs)\nFPGAs are programmable integrated circuits that can be reconfigured for different applications. Their customizable nature provides advantages for accelerating AI algorithms compared to fixed ASICs or inflexible GPUs. While Google, Meta, and NVIDIA are considering putting ASICs in data centers, Microsoft deployed FPGAs in its data centers (Putnam et al. 2014) in 2011 to efficiently serve diverse data center workloads.\n\nAdvantages\nFPGAs provide several benefits over GPUs and ASICs for accelerating machine learning workloads.\n\nFlexibility Through Reconfigurable Fabric\nThe key advantage of FPGAs is the ability to reconfigure the underlying fabric to implement custom architectures optimized for different models, unlike fixed-function ASICs. For example, quant trading firms use FPGAs to accelerate their algorithms because they change frequently, and the low NRE cost of FPGAs is more viable than tapping out new ASICs. Figure 10.3 contains a table comparing three different FPGAs.\n\n\n\n\n\n\nFigure 10.3: Comparison of FPGAs. Source: Gwennap (n.d.).\n\n\nGwennap, Linley. n.d. “Certus-NX Innovates General-Purpose FPGAs.”\n\n\nFPGAs comprise basic building blocks - configurable logic blocks, RAM blocks, and interconnects. Vendors provide a base amount of these resources, and engineers program the chips by compiling HDL code into bitstreams that rearrange the fabric into different configurations. This makes FPGAs adaptable as algorithms evolve.\nWhile FPGAs may not achieve the utmost performance and efficiency of workload-specific ASICs, their programmability provides more flexibility as algorithms change. This adaptability makes FPGAs a compelling choice for accelerating evolving machine learning applications. Microsoft has deployed FPGAs in its Azure data centers for machine learning workloads to serve diverse applications instead of ASICs. The programmability enables optimization across changing ML models.\n\n\nCustomized Parallelism and Pipelining\nFPGA architectures can leverage spatial parallelism and pipelining by tailoring the hardware design to mirror the parallelism in ML models. For example, Intel’s HARPv2 FPGA platform splits the layers of an MNIST convolutional network across separate processing elements to maximize throughput. Unique parallel patterns like tree ensemble evaluations are also possible on FPGAs. Deep pipelines with optimized buffering and dataflow can be customized to each model’s structure and datatypes. This level of tailored parallelism and pipelining is not feasible on GPUs.\n\n\nLow Latency On-Chip Memory\nLarge amounts of high-bandwidth on-chip memory enable localized storage for weights and activations. For instance, Xilinx Versal FPGAs contain 32MB of low-latency RAM blocks and dual-channel DDR4 interfaces for external memory. Bringing memory physically closer to the compute units reduces access latency. This provides significant speed advantages over GPUs that traverse PCIe or other system buses to reach off-chip GDDR6 memory.\n\n\nNative Support for Low Precision\nA key advantage of FPGAs is the ability to natively implement any bit width for arithmetic units, such as INT4 or bfloat16, used in quantized ML models. For example, Intel’s Stratix 10 NX FPGAs have dedicated INT8 cores that can achieve up to 143 INT8 TOPS at ~1 TOPS/W Intel Stratix 10 NX FPGA. Lower bit widths increase arithmetic density and performance. FPGAs can even support mixed precision or dynamic precision tuning at runtime.\n\n\n\nDisadvantages\n\nLower Peak Throughput than ASICs\nFPGAs cannot match the raw throughput numbers of ASICs customized for a specific model and precision. The overheads of the reconfigurable fabric compared to fixed function hardware result in lower peak performance. For example, the TPU v5e pods allow up to 256 chips to be connected with more than 100 petaOps of INT8 performance, while FPGAs can offer up to 143 INT8 TOPS or 286 INT4 TOPS Intel Stratix 10 NX FPGA.\nThis is because FPGAs comprise basic building blocks—configurable logic blocks, RAM blocks, and interconnects. Vendors provide a set amount of these resources. To program FPGAs, engineers write HDL code and compile it into bitstreams that rearrange the fabric, which has inherent overheads versus an ASIC purpose-built for one computation.\n\n\nProgramming Complexity\nTo optimize FPGA performance, engineers must program the architectures in low-level hardware description languages like Verilog or VHDL. This requires hardware design expertise and longer development cycles than higher-level software frameworks like TensorFlow. Maximizing utilization can be challenging despite advances in high-level synthesis from C/C++.\n\n\nReconfiguration Overheads\nChanging FPGA configurations requires reloading a new bitstream, which has considerable latency and storage size costs. For example, partial reconfiguration on Xilinx FPGAs can take 100s of milliseconds. This makes dynamically swapping architectures in real-time infeasible. The bitstream storage also consumes on-chip memory.\n\n\nDiminishing Gains on Advanced Nodes\nWhile smaller process nodes greatly benefit ASICs, they provide fewer advantages for FPGAs. At 7nm and below, effects like process variation, thermal constraints, and aging disproportionately impact FPGA performance. The overheads of the configurable fabric also diminish gains compared to fixed-function ASICs.\n\n\nCase Study\nFPGAs have found widespread application in various fields, including medical imaging, robotics, and finance, where they excel in handling computationally intensive machine learning tasks. In medical imaging, an illustrative example is the application of FPGAs for brain tumor segmentation, a traditionally time-consuming and error-prone process. For instance, Xiong et al. developed a quantized segmentation accelerator, which they retrained using the BraTS19 and BraTS20 datasets. Their work yielded remarkable results, achieving over 5x and 44x performance improvements and 11x and 82x energy efficiency gains compared to GPU and CPU implementations, respectively (Xiong et al. 2021).\n\nXiong, Siyu, Guoqing Wu, Xitian Fan, Xuan Feng, Zhongcheng Huang, Wei Cao, Xuegong Zhou, et al. 2021. “MRI-Based Brain Tumor Segmentation Using FPGA-Accelerated Neural Network.” BMC Bioinf. 22 (1): 421. https://doi.org/10.1186/s12859-021-04347-6.\n\n\n\n\n10.3.3 Digital Signal Processors (DSPs)\nThe first digital signal processor core was built in 1948 by Texas Instruments (The Evolution of Audio DSPs). Traditionally, DSPs would have logic to directly access digital/audio data in memory, perform an arithmetic operation (multiply-add-accumulate-MAC was one of the most common operations), and then write the result back to memory. The DSP would include specialized analog components to retrieve digital/audio data.\nOnce we entered the smartphone era, DSPs started encompassing more sophisticated tasks. They required Bluetooth, Wi-Fi, and cellular connectivity. Media also became much more complex. Today, it’s rare to have entire chips dedicated to just DSP, but a System on Chip would include DSPs and general-purpose CPUs. For example, Qualcomm’s Hexagon Digital Signal Processor claims to be a “world-class processor with both CPU and DSP functionality to support deeply embedded processing needs of the mobile platform for both multimedia and modem functions.” Google Tensors, the chip in the Google Pixel phones, also includes CPUs and specialized DSP engines.\n\nAdvantages\nDSPs architecturally provide advantages in vector math throughput, low latency memory access, power efficiency, and support for diverse datatypes - making them well-suited for embedded ML acceleration.\n\nOptimized Architecture for Vector Math\nDSPs contain specialized data paths, register files, and instructions optimized specifically for vector math operations commonly used in machine learning models. This includes dot product engines, MAC units, and SIMD capabilities tailored for vector/matrix calculations. For example, the CEVA-XM6 DSP (“Ceva SensPro Fuses AI and Vector DSP”) has 512-bit vector units to accelerate convolutions. This efficiency on vector math workloads is far beyond general CPUs.\n\n\nLow Latency On-Chip Memory\nDSPs integrate large amounts of fast on-chip SRAM memory to hold data locally for processing. Bringing memory physically closer to the computation units reduces access latency. For example, Analog’s SHARC+ DSP contains 10MB of on-chip SRAM. This high-bandwidth local memory provides speed advantages for real-time applications.\n\n\nPower Efficiency\nDSPs are engineered to provide high performance per watt on digital signal workloads. Efficient data paths, parallelism, and memory architectures enable trillions of math operations per second within tight mobile power budgets. For example, Qualcomm’s Hexagon DSP can deliver 4 trillion operations per second (TOPS) while consuming minimal watts.\n\n\nSupport for Integer and Floating Point Math\nUnlike GPUs that excel at single or half precision, DSPs can natively support 8/16-bit integer and 32-bit floating point datatypes used across ML models. Some DSPs support dot product acceleration at INT8 precision for quantized neural networks.\n\n\n\nDisadvantages\nDSPs make architectural tradeoffs that limit peak throughput, precision, and model capacity compared to other AI accelerators. However, their advantages in power efficiency and integer math make them a strong edge computing option. So, while DSPs provide some benefits over CPUs, they also come with limitations for machine learning workloads:\n\nLower Peak Throughput than ASICs/GPUs\nDSPs cannot match the raw computational throughput of GPUs or customized ASICs designed specifically for machine learning. For example, Qualcomm’s Cloud AI 100 ASIC delivers 480 TOPS on INT8, while their Hexagon DSP provides 10 TOPS. DSPs lack the massive parallelism of GPU SM units.\n\n\nSlower Double Precision Performance\nMost DSPs must be optimized for the higher precision floating point needed in some ML models. Their dot product engines focus on INT8/16 and FP32, which provide better power efficiency. However, 64-bit floating point throughput is much lower, which can limit usage in models requiring high precision.\n\n\nConstrained Model Capacity\nThe limited on-chip memory of DSPs constrains the model sizes that can be run. Large deep learning models with hundreds of megabytes of parameters would exceed on-chip SRAM capacity. DSPs are best suited for small to mid-sized models targeted for edge devices.\n\n\nProgramming Complexity\nEfficient programming of DSP architectures requires expertise in parallel programming and optimizing data access patterns. Their specialized microarchitectures have a steeper learning curve than high-level software frameworks, making development more complex.\n\n\n\n\n10.3.4 Graphics Processing Units (GPUs)\nThe term graphics processing unit has existed since at least the 1980s. There had always been a demand for graphics hardware in video game consoles (high demand, needed to be relatively lower cost) and scientific simulations (lower demand, but higher resolution, could be at a high price point).\nThe term was popularized, however, in 1999 when NVIDIA launched the GeForce 256, mainly targeting the PC games market sector (Lindholm et al. 2008). As PC games became more sophisticated, NVIDIA GPUs became more programmable. Soon, users realized they could take advantage of this programmability, run various non-graphics-related workloads on GPUs, and benefit from the underlying architecture. And so, in the late 2000s, GPUs became general-purpose graphics processing units or GP-GPUs.\n\nLindholm, Erik, John Nickolls, Stuart Oberman, and John Montrym. 2008. “NVIDIA Tesla: A Unified Graphics and Computing Architecture.” IEEE Micro 28 (2): 39–55. https://doi.org/10.1109/mm.2008.31.\nIntel Arc Graphics and AMD Radeon RX have also developed their GPUs over time.\n\nAdvantages\n\nHigh Computational Throughput\nThe key advantage of GPUs is their ability to perform massively parallel floating-point calculations optimized for computer graphics and linear algebra (Raina, Madhavan, and Ng 2009). Modern GPUs like Nvidia’s A100 offer up to 19.5 teraflops of FP32 performance with 6912 CUDA cores and 40GB of graphics memory tightly coupled with 1.6TB/s of graphics memory bandwidth.\n\nRaina, Rajat, Anand Madhavan, and Andrew Y. Ng. 2009. “Large-Scale Deep Unsupervised Learning Using Graphics Processors.” In Proceedings of the 26th Annual International Conference on Machine Learning, edited by Andrea Pohoreckyj Danyluk, Léon Bottou, and Michael L. Littman, 382:873–80. ACM International Conference Proceeding Series. ACM. https://doi.org/10.1145/1553374.1553486.\nThis raw throughput stems from the highly parallel streaming multiprocessor (SM) architecture tailored for data-parallel workloads (Zhihao Jia, Zaharia, and Aiken 2019). Each SM contains hundreds of scalar cores optimized for float32/64 math. With thousands of SMs on a chip, GPUs are purpose-built for matrix multiplication and vector operations used throughout neural networks.\nFor example, Nvidia’s latest H100 GPU provides 4000 TFLOPs of FP8, 2000 TFLOPs of FP16, 1000 TFLOPs of TF32, 67 TFLOPs of FP32 and 34 TFLOPs of FP64 Compute performance, which can dramatically accelerate large batch training on models like BERT, GPT-3, and other transformer architectures. The scalable parallelism of GPUs is key to speeding up computationally intensive deep learning.\n\n\nMature Software Ecosystem\nNvidia provides extensive runtime libraries like cuDNN and cuBLAS that are highly optimized for deep learning primitives. Frameworks like TensorFlow and PyTorch integrate with these libraries to enable GPU acceleration without direct programming. CUDA provides lower-level control for custom computations.\nThis ecosystem enables quick leveraging of GPUs via high-level Python without GPU programming expertise. Known workflows and abstractions provide a convenient on-ramp for scaling up deep learning experiments. The software maturity supplements the throughput advantages.\n\n\nBroad Availability\nThe economies of scale of graphics processing make GPUs broadly accessible in data centers, cloud platforms like AWS and GCP, and desktop workstations. Their availability in research environments has provided a convenient ML experimentation and innovation platform. For example, nearly every state-of-the-art deep learning result has involved GPU acceleration because of this ubiquity. The broad access supplements the software maturity to make GPUs the standard ML accelerator.\n\n\nProgrammable Architecture\nWhile not as flexible as FPGAs, GPUs provide programmability via CUDA and shader languages to customize computations. Developers can optimize data access patterns, create new ops, and tune precisions for evolving models and algorithms.\n\n\n\nDisadvantages\nWhile GPUs have become the standard accelerator for deep learning, their architecture has some key downsides.\n\nLess Efficient than Custom ASICs\nThe statement “GPUs are less efficient than ASICs” could spark intense debate within the ML/AI field and cause this book to explode.\nTypically, GPUs are perceived as less efficient than ASICs because the latter are custom-built for specific tasks and thus can operate more efficiently by design. With their general-purpose architecture, GPUs are inherently more versatile and programmable, catering to a broad spectrum of computational tasks beyond ML/AI.\nHowever, modern GPUs have evolved to include specialized hardware support for essential AI operations, such as generalized matrix multiplication (GEMM) and other matrix operations, native support for quantization, and native support for pruning, which are critical for running ML models effectively. These enhancements have significantly improved the efficiency of GPUs for AI tasks to the point where they can rival the performance of ASICs for certain applications.\nConsequently, contemporary GPUs are convergent, incorporating specialized ASIC-like capabilities within a flexible, general-purpose processing framework. This adaptability has blurred the lines between the two types of hardware. GPUs offer a strong balance of specialization and programmability that is well-suited to the dynamic needs of ML/AI research and development.\n\n\nHigh Memory Bandwidth Needs\nThe massively parallel architecture requires tremendous memory bandwidth to supply thousands of cores, as shown in Figure 1. For example, the Nvidia A100 GPU requires 1.6TB/sec to fully saturate its computer. GPUs rely on wide 384-bit memory buses to high-bandwidth GDDR6 RAM, but even the fastest GDDR6 tops out at around 1 TB/sec. This dependence on external DRAM incurs latency and power overheads.\n\n\nProgramming Complexity\nWhile tools like CUDA help, optimally mapping and partitioning ML workloads across the massively parallel GPU architecture remains challenging, achieving both high utilization and memory locality requires low-level tuning (Zhe Jia et al. 2018). Abstractions like TensorFlow can leave performance on the table.\n\nJia, Zhe, Marco Maggioni, Benjamin Staiger, and Daniele P. Scarpazza. 2018. “Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking.” ArXiv Preprint. https://arxiv.org/abs/1804.06826.\n\n\nLimited On-Chip Memory\nGPUs have relatively small on-chip memory caches compared to ML models’ large working set requirements during training. They rely on high bandwidth access to external DRAM, which ASICs minimize with large on-chip SRAM.\n\n\nFixed Architecture\nUnlike FPGAs, the fundamental GPU architecture cannot be altered post-manufacture. This constraint limits adapting to novel ML workloads or layers. The CPU-GPU boundary also creates data movement overheads.\n\n\n\nCase Study\nThe recent groundbreaking research conducted by OpenAI (Brown et al. 2020) with their GPT-3 model. GPT-3, a language model with 175 billion parameters, demonstrated unprecedented language understanding and generation capabilities. Its training, which would have taken months on conventional CPUs, was accomplished in a matter of days using powerful GPUs, thus pushing the boundaries of natural language processing (NLP) capabilities.\n\n\n\n10.3.5 Central Processing Units (CPUs)\nThe term CPUs has a long history that dates back to 1955 (Weik 1955) while the first microprocessor CPU-the Intel 4004-was invented in 1971 (Who Invented the Microprocessor?). Compilers compile high-level programming languages like Python, Java, or C to assemble instructions (x86, ARM, RISC-V, etc.) for CPUs to process. The set of instructions a CPU understands is called the “instruction set architecture” (ISA), which defines the commands that the processor can execute directly. It must be agreed upon by both the hardware and software running atop it (See section 5 for a more in-depth description of instruction set architectures-ISAs).\n\nWeik, Martin H. 1955. A Survey of Domestic Electronic Digital Computing Systems. Ballistic Research Laboratories.\nAn overview of significant developments in CPUs:\n\nSingle-core Era (1950s- 2000): This era is known for aggressive microarchitectural improvements. Techniques like speculative execution (executing an instruction before the previous one was done), out-of-order execution (re-ordering instructions to be more effective), and wider issue widths (executing multiple instructions at once) were implemented to increase instruction throughput. The term “System on Chip” also originated in this era as different analog components (components designed with transistors) and digital components (components designed with hardware description languages that are mapped to transistors) were put on the same platform to achieve some task.\nMulticore Era (2000s): Driven by the decrease of Moore’s Law, this era is marked by scaling the number of cores within a CPU. Now, tasks can be split across many different cores, each with its own datapath and control unit. Many of the issues in this era pertained to how to share certain resources, which resources to share, and how to maintain coherency and consistency across all the cores.\nSea of accelerators (2010s): Again, driven by the decrease of Moore’s law, this era is marked by offloading more complicated tasks to accelerators (widgets) attached to the main datapath in CPUs. It’s common to see accelerators dedicated to various AI workloads, as well as image/digital processing, and cryptography. In these designs, CPUs are often described more as judges, deciding which tasks should be processed rather than doing the processing itself. Any task could still be run on the CPU rather than the accelerators, but the CPU would generally be slower. However, the cost of designing and programming the accelerator became a non-trivial hurdle that sparked interest in design-specific libraries (DSLs).\nPresence in data centers: Although we often hear that GPUs dominate the data center marker, CPUs are still well suited for tasks that don’t inherently possess a large amount of parallelism. CPUs often handle serial and small tasks and coordinate the data center.\nOn the edge: Given the tighter resource constraints on the edge, edge CPUs often only implement a subset of the techniques developed in the sing-core era because these optimizations tend to be heavy on power and area consumption. Edge CPUs still maintain a relatively simple datapath with limited memory capacities.\n\nTraditionally, CPUs have been synonymous with general-purpose computing, a term that has also changed as the “average” workload a consumer would run changes over time. For example, floating point components were once considered reserved for “scientific computing,” they were usually implemented as a co-processor (a modular component that worked with the datapath) and seldom deployed to average consumers. Compare this attitude to today, where FPUs are built into every datapath.\n\nAdvantages\nWhile raw throughput is limited, general-purpose CPUs provide practical AI acceleration benefits.\n\nGeneral Programmability\nCPUs support diverse workloads beyond ML, providing flexible general-purpose programmability. This versatility comes from their standardized instruction sets and mature compiler ecosystems, which allow running any application, from databases and web servers to analytics pipelines (Hennessy and Patterson 2019).\n\nHennessy, John L., and David A. Patterson. 2019. “A New Golden Age for Computer Architecture.” Commun. ACM 62 (2): 48–60. https://doi.org/10.1145/3282307.\nThis avoids the need for dedicated ML accelerators and enables leveraging existing CPU-based infrastructure for basic ML deployment. For example, X86 servers from vendors like Intel and AMD can run common ML frameworks using Python and TensorFlow packages alongside other enterprise workloads.\n\n\nMature Software Ecosystem\nFor decades, highly optimized math libraries like BLAS, LAPACK, and FFTW have leveraged vectorized instructions and multithreading on CPUs (Dongarra 2009). Major ML frameworks like PyTorch, TensorFlow, and SciKit-Learn are designed to integrate seamlessly with these CPU math kernels.\n\nDongarra, Jack J. 2009. “The Evolution of High Performance Computing on System z.” IBM J. Res. Dev. 53: 3–4.\nHardware vendors like Intel and AMD also provide low-level libraries to optimize performance for deep learning primitives fully (AI Inference Acceleration on CPUs). This robust, mature software ecosystem allows quickly deploying ML on existing CPU infrastructure.\n\n\nWide Availability\nThe economies of scale of CPU manufacturing, driven by demand across many markets like PCs, servers, and mobile, make them ubiquitously available. Intel CPUs, for example, have powered most servers for decades (Ranganathan 2011). This wide availability in data centers reduces hardware costs for basic ML deployment.\n\nRanganathan, Parthasarathy. 2011. “From Microprocessors to Nanostores: Rethinking Data-Centric Systems.” Computer 44 (1): 39–48. https://doi.org/10.1109/mc.2011.18.\nEven small embedded devices typically integrate some CPU, enabling edge inference. The ubiquity reduces the need to purchase specialized ML accelerators in many situations.\n\n\nLow Power for Inference\nOptimizations like ARM Neon and Intel AVX vector extensions provide power-efficient integer and floating point throughput optimized for “bursty” workloads such as inference (Ignatov et al. 2018). While slower than GPUs, CPU inference can be deployed in power-constrained environments. For example, ARM’s Cortex-M CPUs now deliver over 1 TOPS of INT8 performance under 1W, enabling keyword spotting and vision applications on edge devices (ARM).\n\n\n\nDisadvantages\nWhile providing some advantages, general-purpose CPUs also have limitations for AI workloads.\n\nLower Throughput than Accelerators\nCPUs lack the specialized architectures for massively parallel processing that GPUs and other accelerators provide. Their general-purpose design reduces computational throughput for the highly parallelizable math operations common in ML models (N. P. Jouppi et al. 2017a).\n\nJouppi, Norman P., Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, et al. 2017a. “In-Datacenter Performance Analysis of a Tensor Processing Unit.” In Proceedings of the 44th Annual International Symposium on Computer Architecture, 1–12. ISCA ’17. New York, NY, USA: ACM. https://doi.org/10.1145/3079856.3080246.\n\n\nNot Optimized for Data Parallelism\nThe architectures of CPUs are not specifically optimized for data parallel workloads inherent to AI (Sze et al. 2017). They allocate substantial silicon area to instruction decoding, speculative execution, caching, and flow control that provides little benefit for the array operations used in neural networks (AI Inference Acceleration on CPUs). However, modern CPUs are equipped with vector instructions like AVX-512 specifically to accelerate certain key operations like matrix multiplication.\nGPU streaming multiprocessors, for example, devote most transistors to floating point units instead of complex branch prediction logic. This specialization allows much higher utilization for ML math.\n\n\nHigher Memory Latency\nCPUs suffer from higher latency accessing main memory relative to GPUs and other accelerators (DDR). Techniques like tiling and caching can help, but the physical separation from off-chip RAM bottlenecks data-intensive ML workloads. This emphasizes the need for specialized memory architectures in ML hardware.\n\n\nPower Inefficiency Under Heavy Workloads\nWhile suitable for intermittent inference, sustaining near-peak throughput for training results in inefficient power consumption on CPUs, especially mobile CPUs (Ignatov et al. 2018). Accelerators explicitly optimize the data flow, memory, and computation for sustained ML workloads. CPUs are energy-inefficient for training large models.\n\n\n\n\n10.3.6 Comparison\nTable 10.2 compares the different types of hardware features.\n\n\n\nTable 10.2: Comparison of different hardware accelerators for AI workloads.\n\n\n\n\n\n\n\n\n\n\n\nAccelerator\nDescription\nKey Advantages\nKey Disadvantages\n\n\n\n\nASICs\nCustom ICs designed for target workloads like AI inference\n\nMaximizes perf/watt.\nOptimized for tensor ops\nLow latency on-chip memory\n\n\nFixed architecture lacks flexibility\nHigh NRE cost\nLong design cycles\n\n\n\nFPGAs\nReconfigurable fabric with programmable logic and routing\n\nFlexible architecture\nLow latency memory access\n\n\nLower perf/watt than ASICs\nComplex programming\n\n\n\nGPUs\nOriginally for graphics, now used for neural network acceleration\n\nHigh throughput\nParallel scalability\nSoftware ecosystem with CUDA\n\n\nNot as power efficient as ASICs\nRequire high memory bandwidth\n\n\n\nCPUs\nGeneral purpose processors\n\nProgrammability\nUbiquitous availability\n\n\nLower performance for AI workloads\n\n\n\n\n\n\n\nIn general, CPUs provide a readily available baseline, GPUs deliver broadly accessible acceleration, FPGAs offer programmability, and ASICs maximize efficiency for fixed functions. The optimal choice depends on the target application’s scale, cost, flexibility, and other requirements.\nAlthough first developed for data center deployment, Google has also put considerable effort into developing Edge TPUs. These Edge TPUs maintain the inspiration from systolic arrays but are tailored to the limited resources accessible at the edge.", + "text": "10.3 Accelerator Types\nHardware accelerators can take on many forms. They can exist as a widget (like the Neural Engine in the Apple M1 chip) or as entire chips specially designed to perform certain tasks very well. This section will examine processors for machine learning workloads along the spectrum from highly specialized ASICs to more general-purpose CPUs.\nWe first focus on custom hardware purpose-built for AI to understand the most extreme optimizations possible when design constraints are removed. This establishes a ceiling for performance and efficiency. We then progressively consider more programmable and adaptable architectures, discussing GPUs and FPGAs. These make tradeoffs in customization to maintain flexibility. Finally, we cover general-purpose CPUs that sacrifice optimizations for a particular workload in exchange for versatile programmability across applications.\nBy structuring the analysis along this spectrum, we aim to illustrate the fundamental tradeoffs between utilization, efficiency, programmability, and flexibility in accelerator design. The optimal balance point depends on the constraints and requirements of the target application. This spectrum perspective provides a framework for reasoning about hardware choices for machine learning and the capabilities required at each level of specialization.\nFigure 10.2 illustrates the complex interplay between flexibility, performance, functional diversity, and area of architecture design. Notice how the ASIC is on the bottom-right corner, with minimal area, flexibility, and power consumption and maximal performance, due to its highly specialized application-specific nature. A key tradeoff is functional diversity vs performance: general purpose architectures can serve diverse applications but their application performance is degraded as compared to more customized architectures.\nThe progression begins with the most specialized option, ASICs purpose-built for AI, to ground our understanding in the maximum possible optimizations before expanding to more generalizable architectures. This structured approach elucidates the accelerator design space.\n\n\n\n\n\n\nFigure 10.2: Design tradeoffs. Source: El-Rayis (2014).\n\n\nEl-Rayis, A. O. 2014. “Reconfigurable Architectures for the Next Generation of Mobile Device Telecommunications Systems.” : https://www.researchgate.net/publication/292608967.\n\n\n\n10.3.1 Application-Specific Integrated Circuits (ASICs)\nAn Application-Specific Integrated Circuit (ASIC) is a type of integrated circuit (IC) that is custom-designed for a specific application or workload rather than for general-purpose use. Unlike CPUs and GPUs, ASICs do not support multiple applications or workloads. Rather, they are optimized to perform a single task extremely efficiently. The Google TPU is an example of an ASIC.\nASICs achieve this efficiency by tailoring every aspect of the chip design - the underlying logic gates, electronic components, architecture, memory, I/O, and manufacturing process - specifically for the target application. This level of customization allows removing any unnecessary logic or functionality required for general computation. The result is an IC that maximizes performance and power efficiency on the desired workload. The efficiency gains from application-specific hardware are so substantial that these software-centric firms dedicate enormous engineering resources to designing customized ASICs.\nThe rise of more complex machine learning algorithms has made the performance advantages enabled by tailored hardware acceleration a key competitive differentiator, even for companies traditionally concentrated on software engineering. ASICs have become a high-priority investment for major cloud providers aiming to offer faster AI computation.\n\nAdvantages\nDue to their customized nature, ASICs provide significant benefits over general-purpose processors like CPUs and GPUs. The key advantages include the following.\n\nMaximized Performance and Efficiency\nThe most fundamental advantage of ASICs is maximizing performance and power efficiency by customizing the hardware architecture specifically for the target application. Every transistor and design aspect is optimized for the desired workload - no unnecessary logic or overhead is needed to support generic computation.\nFor example, Google’s Tensor Processing Units (TPUs) contain architectures tailored exactly for the matrix multiplication operations used in neural networks. To design the TPU ASICs, Google’s engineering teams need to define the chip specifications clearly, write the architecture description using Hardware Description Languages like Verilog, synthesize the design to map it to hardware components, and carefully place-and-route transistors and wires based on the fabrication process design rules. This complex design process, known as very-large-scale integration (VLSI), allows them to build an optimized IC for machine learning workloads.\nAs a result, TPU ASICs achieve over an order of magnitude higher efficiency in operations per watt than general-purpose GPUs on ML workloads by maximizing performance and minimizing power consumption through a full-stack custom hardware design.\n\n\nSpecialized On-Chip Memory\nASICs incorporate on-chip memory, such as SRAM (Static Random Access Memory), and caches that are specifically optimized to feed data to the computational units. SRAM is a type of memory that is faster and more reliable than DRAM (Dynamic Random Access Memory) because it does not need to be periodically refreshed. However, it requires more transistors per bit of data, making it take up more space and more expensive to produce as compared to DRAM.\nSRAM is ideal for on-chip memory, where speed is critical. The advantage of having large amounts of high-bandwidth, on-chip SRAM is that data can be stored close to the processing elements, allowing for rapid access. This provides tremendous speed advantages compared to acessing off-chip DRAM, which, although larger in capacity, can be up to 100x slower. For example, Apple’s M1 system-on-a-chip contains special low-latency SRAM to accelerate the performance of its Neural Engine machine learning hardware.\nData locality and optimizing memory hierarchy are crucial for high throughput and low power. Table 10.1 shows “Numbers Everyone Should Know,” from Jeff Dean.\n\n\n\nTable 10.1: Latency comparison of operations in computing and networking.\n\n\n\n\n\n\n\n\n\nOperation\nLatency\n\n\n\n\nL1 cache reference\n0.5 ns\n\n\nBranch mispredict\n5 ns\n\n\nL2 cache reference\n7 ns\n\n\nMutex lock/unlock\n25 ns\n\n\nMain memory reference\n100 ns\n\n\nCompress 1K bytes with Zippy\n3,000 ns (3 us)\n\n\nSend 1 KB bytes over 1 Gbps network\n10,000 ns (10 us)\n\n\nRead 4 KB randomly from SSD\n150,000 ns (150 us)\n\n\nRead 1 MB sequentially from memory\n250,000 ns (250 us)\n\n\nRound trip within same datacenter\n500,000 ns (0.5 ms)\n\n\nRead 1 MB sequentially from SSD\n1,000,000 ns (1 ms)\n\n\nDisk seek\n10,000,000 ns (10 ms)\n\n\nRead 1 MB sequentially from disk\n20,000,000 ns (20 ms)\n\n\nSend packet CA → Netherlands → CA\n150,000,000 ns (150 ms)\n\n\n\n\n\n\n\n\nCustom Datatypes and Operations\nUnlike general-purpose processors, ASICs can be designed to natively support custom datatypes like INT4 or bfloat16, which are widely used in ML models. For instance, Nvidia’s Ampere GPU architecture has dedicated bfloat16 Tensor Cores to accelerate AI workloads. Low-precision datatypes enable higher arithmetic density and performance. Please refer to Section 8.6 for additional details. ASICs can also directly incorporate non-standard operations common in ML algorithms as primitive operations - for example, natively supporting activation functions like ReLU makes execution more efficient.\n\n\nHigh Parallelism\nASIC architectures can leverage higher parallelism tuned for the target workload versus general-purpose CPUs or GPUs. More computational units tailored for the application mean more operations execute simultaneously. Highly parallel ASICs achieve tremendous throughput for data parallel workloads like neural network inference.\n\n\nAdvanced Process Nodes\nCutting-edge manufacturing processes allow more transistors to be packed into smaller die areas, increasing density. ASICs designed specifically for high-volume applications can better amortize the costs of cutting-edge process nodes.\n\n\n\nDisadvantages\n\nLong Design Timelines\nThe engineering process of designing and validating an ASIC can take 2-3 years. Synthesizing the architecture using hardware description languages, taping out the chip layout, and fabricating the silicon on advanced process nodes involve long development cycles. For example, to tape out a 7nm chip, teams need to define specifications carefully, write the architecture in HDL, synthesize the logic gates, place components, route all interconnections, and finalize the layout to send for fabrication. This very large-scale integration (VLSI) flow means ASIC design and manufacturing can traditionally take 2-5 years.\nThere are a few key reasons why the long design timelines of ASICs, often 2-3 years, can be challenging for machine learning workloads:\n\nML algorithms evolve rapidly: New model architectures, training techniques, and network optimizations are constantly emerging. For example, Transformers became hugely popular in NLP last few years. When an ASIC finishes tapeout, the optimal architecture for a workload may have changed.\nDatasets grow quickly: ASICs designed for certain model sizes or datatypes can become undersized relative to demand. For instance, natural language models are scaling exponentially with more data and parameters. A chip designed for BERT might not accommodate GPT-3.\nML applications change frequently: The industry focus shifts between computer vision, speech, NLP, recommender systems, etc. An ASIC optimized for image classification may have less relevance in a few years.\nFaster design cycles with GPUs/FPGAs: Programmable accelerators like GPUs can adapt much quicker by upgrading software libraries and frameworks. New algorithms can be deployed without hardware changes.\nTime-to-market needs: Getting a competitive edge in ML requires rapidly experimenting with and deploying new ideas. Waiting several years for an ASIC is different from fast iteration.\n\nThe pace of innovation in ML needs to be better matched to the multi-year timescale for ASIC development. Significant engineering efforts are required to extend ASIC lifespan through modular architectures, process scaling, model compression, and other techniques. However, the rapid evolution of ML makes fixed-function hardware challenging.\n\n\nHigh Non-Recurring Engineering Costs\nThe fixed costs of taking an ASIC from design to high-volume manufacturing can be very capital-intensive, often tens of millions of dollars. Photomask fabrication for taping out chips in advanced process nodes, packaging, and one-time engineering efforts is expensive. For instance, a 7nm chip tape-out alone could cost millions. The high non-recurring engineering (NRE) investment narrows ASIC viability to high-volume production use cases where the upfront cost can be amortized.\n\n\nComplex Integration and Programming\nASICs require extensive software integration work, including drivers, compilers, OS support, and debugging tools. They also need expertise in electrical and thermal packaging. Additionally, efficiently programming ASIC architectures can involve challenges like workload partitioning and scheduling across many parallel units. The customized nature necessitates significant integration efforts to turn raw hardware into fully operational accelerators.\nWhile ASICs provide massive efficiency gains on target applications by tailoring every aspect of the hardware design to one specific task, their fixed nature results in tradeoffs in flexibility and development costs compared to programmable accelerators, which must be weighed based on the application.\n\n\n\n\n10.3.2 Field-Programmable Gate Arrays (FPGAs)\nFPGAs are programmable integrated circuits that can be reconfigured for different applications. Their customizable nature provides advantages for accelerating AI algorithms compared to fixed ASICs or inflexible GPUs. While Google, Meta, and NVIDIA are considering putting ASICs in data centers, Microsoft deployed FPGAs in its data centers (Putnam et al. 2014) in 2011 to efficiently serve diverse data center workloads.\n\nXiong, Siyu, Guoqing Wu, Xitian Fan, Xuan Feng, Zhongcheng Huang, Wei Cao, Xuegong Zhou, et al. 2021. “MRI-Based Brain Tumor Segmentation Using FPGA-Accelerated Neural Network.” BMC Bioinf. 22 (1): 421. https://doi.org/10.1186/s12859-021-04347-6.\nFPGAs have found widespread application in various fields, including medical imaging, robotics, and finance, where they excel in handling computationally intensive machine learning tasks. In medical imaging, an illustrative example is the application of FPGAs for brain tumor segmentation, a traditionally time-consuming and error-prone process. Compared to traditional GPU and CPU implementations, FPGAs have demonstrated over 5x and 44x performance improvements, respectively, and 11x and 82x gains in energy efficiency, highlighting their potential for demanding applications (Xiong et al. 2021).\n\nAdvantages\nFPGAs provide several benefits over GPUs and ASICs for accelerating machine learning workloads.\n\nFlexibility Through Reconfigurable Fabric\nThe key advantage of FPGAs is the ability to reconfigure the underlying fabric to implement custom architectures optimized for different models, unlike fixed-function ASICs. For example, quant trading firms use FPGAs to accelerate their algorithms because they change frequently, and the low NRE cost of FPGAs is more viable than tapping out new ASICs. Figure 10.3 contains a table comparing three different FPGAs.\n\n\n\n\n\n\nFigure 10.3: Comparison of FPGAs. Source: Gwennap (n.d.).\n\n\nGwennap, Linley. n.d. “Certus-NX Innovates General-Purpose FPGAs.”\n\n\nFPGAs comprise basic building blocks - configurable logic blocks, RAM blocks, and interconnects. Vendors provide a base amount of these resources, and engineers program the chips by compiling HDL code into bitstreams that rearrange the fabric into different configurations. This makes FPGAs adaptable as algorithms evolve.\nWhile FPGAs may not achieve the utmost performance and efficiency of workload-specific ASICs, their programmability provides more flexibility as algorithms change. This adaptability makes FPGAs a compelling choice for accelerating evolving machine learning applications.\n\n\nCustomized Parallelism and Pipelining\nFPGA architectures can leverage spatial parallelism and pipelining by tailoring the hardware design to mirror the parallelism in ML models. For example, Intel’s HARPv2 FPGA platform splits the layers of an MNIST convolutional network across separate processing elements to maximize throughput. Unique parallel patterns like tree ensemble evaluations are also possible on FPGAs. Deep pipelines with optimized buffering and dataflow can be customized to each model’s structure and datatypes. This level of tailored parallelism and pipelining is not feasible on GPUs.\n\n\nLow Latency On-Chip Memory\nLarge amounts of high-bandwidth on-chip memory enable localized storage for weights and activations. For instance, Xilinx Versal FPGAs contain 32MB of low-latency RAM blocks and dual-channel DDR4 interfaces for external memory. Bringing memory physically closer to the compute units reduces access latency. This provides significant speed advantages over GPUs that traverse PCIe or other system buses to reach off-chip GDDR6 memory.\n\n\nNative Support for Low Precision\nA key advantage of FPGAs is the ability to natively implement any bit width for arithmetic units, such as INT4 or bfloat16, used in quantized ML models. For example, Intel’s Stratix 10 NX FPGAs have dedicated INT8 cores that can achieve up to 143 INT8 TOPS (Tera Operations Per Second) at ~1 TOPS/W (Tera Operations Per Second per Watt) Intel Stratix 10 NX FPGA. TOPS is a measure of performance similar to FLOPS, but while FLOPS measures floating-point calculations, TOPS measures the number of integer operations a system can perform per second. Lower bit widths, like INT8 or INT4, increase arithmetic density and performance. FPGAs can even support mixed precision or dynamic precision tuning at runtime.\n\n\n\nDisadvantages\n\nLower Peak Throughput than ASICs\nFPGAs cannot match the raw throughput numbers of ASICs customized for a specific model and precision. The overheads of the reconfigurable fabric compared to fixed function hardware result in lower peak performance. For example, the TPU v5e pods allow up to 256 chips to be connected with more than 100 petaOps (Peta Operations Per Second) of INT8 performance, while FPGAs can offer up to 143 INT8 TOPS or 286 INT4 TOPS Intel Stratix 10 NX FPGA. PetaOps represents quadrillions of operations per second, whereas TOPS measures trillions, highlighting the much greater throughput capability of TPU pods compared to FPGAs.\nThis is because FPGAs comprise basic building blocks—configurable logic blocks, RAM blocks, and interconnects. Vendors provide a set amount of these resources. To program FPGAs, engineers write HDL code and compile it into bitstreams that rearrange the fabric, which has inherent overheads versus an ASIC purpose-built for one computation.\n\n\nProgramming Complexity\nTo optimize FPGA performance, engineers must program the architectures in low-level hardware description languages like Verilog or VHDL. This requires hardware design expertise and longer development cycles than higher-level software frameworks like TensorFlow. Maximizing utilization can be challenging despite advances in high-level synthesis from C/C++.\n\n\nReconfiguration Overheads\nChanging FPGA configurations requires reloading a new bitstream, which has considerable latency and storage size costs. For example, partial reconfiguration on Xilinx FPGAs can take 100s of milliseconds. This makes dynamically swapping architectures in real-time infeasible. The bitstream storage also consumes on-chip memory.\n\n\nDiminishing Gains on Advanced Nodes\nWhile smaller process nodes greatly benefit ASICs, they provide fewer advantages for FPGAs. At 7nm and below, effects like process variation, thermal constraints, and aging disproportionately impact FPGA performance. The overheads of the configurable fabric also diminish gains compared to fixed-function ASICs.\n\n\n\n\n10.3.3 Digital Signal Processors (DSPs)\nThe first digital signal processor core was built in 1948 by Texas Instruments (The Evolution of Audio DSPs). Traditionally, DSPs would have logic to directly access digital/audio data in memory, perform an arithmetic operation (multiply-add-accumulate-MAC was one of the most common operations), and then write the result back to memory. The DSP would include specialized analog components to retrieve digital/audio data.\nOnce we entered the smartphone era, DSPs started encompassing more sophisticated tasks. They required Bluetooth, Wi-Fi, and cellular connectivity. Media also became much more complex. Today, it’s rare to have entire chips dedicated to just DSP, but a System on Chip would include DSPs and general-purpose CPUs. For example, Qualcomm’s Hexagon Digital Signal Processor claims to be a “world-class processor with both CPU and DSP functionality to support deeply embedded processing needs of the mobile platform for both multimedia and modem functions.” Google Tensors, the chip in the Google Pixel phones, also includes CPUs and specialized DSP engines.\n\nAdvantages\nDSPs architecturally provide advantages in vector math throughput, low latency memory access, power efficiency, and support for diverse datatypes - making them well-suited for embedded ML acceleration.\n\nOptimized Architecture for Vector Math\nDSPs contain specialized data paths, register files, and instructions optimized specifically for vector math operations commonly used in machine learning models. This includes dot product engines, MAC units, and SIMD capabilities tailored for vector/matrix calculations. For example, the CEVA-XM6 DSP (“Ceva SensPro Fuses AI and Vector DSP”) has 512-bit vector units to accelerate convolutions. This efficiency on vector math workloads is far beyond general CPUs.\n\n\nLow Latency On-Chip Memory\nDSPs integrate large amounts of fast on-chip SRAM memory to hold data locally for processing. Bringing memory physically closer to the computation units reduces access latency. For example, Analog’s SHARC+ DSP contains 10MB of on-chip SRAM. This high-bandwidth local memory provides speed advantages for real-time applications.\n\n\nPower Efficiency\nDSPs are engineered to provide high performance per watt on digital signal workloads. Efficient data paths, parallelism, and memory architectures enable trillions of math operations per second within tight mobile power budgets. For example, Qualcomm’s Hexagon DSP can deliver 4 trillion operations per second (TOPS) while consuming minimal watts.\n\n\nSupport for Integer and Floating Point Math\nUnlike GPUs that excel at single or half precision, DSPs can natively support 8/16-bit integer and 32-bit floating point datatypes used across ML models. Some DSPs support dot product acceleration at INT8 precision for quantized neural networks.\n\n\n\nDisadvantages\nDSPs make architectural tradeoffs that limit peak throughput, precision, and model capacity compared to other AI accelerators. However, their advantages in power efficiency and integer math make them a strong edge computing option. So, while DSPs provide some benefits over CPUs, they also come with limitations for machine learning workloads:\n\nLower Peak Throughput than ASICs/GPUs\nDSPs cannot match the raw computational throughput of GPUs or customized ASICs designed specifically for machine learning. For example, Qualcomm’s Cloud AI 100 ASIC delivers 480 TOPS on INT8, while their Hexagon DSP provides 10 TOPS. DSPs lack the massive parallelism of GPU SM units.\n\n\nSlower Double Precision Performance\nMost DSPs must be optimized for the higher precision floating point needed in some ML models. Their dot product engines focus on INT8/16 and FP32, which provide better power efficiency. However, 64-bit floating point throughput is much lower, which can limit usage in models requiring high precision.\n\n\nConstrained Model Capacity\nThe limited on-chip memory of DSPs constrains the model sizes that can be run. Large deep learning models with hundreds of megabytes of parameters would exceed on-chip SRAM capacity. DSPs are best suited for small to mid-sized models targeted for edge devices.\n\n\nProgramming Complexity\nEfficient programming of DSP architectures requires expertise in parallel programming and optimizing data access patterns. Their specialized microarchitectures have a steeper learning curve than high-level software frameworks, making development more complex.\n\n\n\n\n10.3.4 Graphics Processing Units (GPUs)\nThe term graphics processing unit has existed since at least the 1980s. There had always been a demand for graphics hardware in video game consoles (high demand, needed to be relatively lower cost) and scientific simulations (lower demand, but higher resolution, could be at a high price point).\nThe term was popularized, however, in 1999 when NVIDIA launched the GeForce 256, mainly targeting the PC games market sector (Lindholm et al. 2008). As PC games became more sophisticated, NVIDIA GPUs became more programmable. Soon, users realized they could take advantage of this programmability, run various non-graphics-related workloads on GPUs, and benefit from the underlying architecture. And so, in the late 2000s, GPUs became general-purpose graphics processing units or GP-GPUs.\n\nLindholm, Erik, John Nickolls, Stuart Oberman, and John Montrym. 2008. “NVIDIA Tesla: A Unified Graphics and Computing Architecture.” IEEE Micro 28 (2): 39–55. https://doi.org/10.1109/mm.2008.31.\nFollowing this shift, other major players like Intel with its Arc Graphics and AMD with their Radeon RX series also evolved their GPUs to support a broader range of applications beyond traditional graphics rendering. This expansion of GPU capabilities opened up new possibilities, particularly in fields requiring massive computational power.\nA striking example of this potential is the recent groundbreaking research conducted by OpenAI (Brown et al. 2020) with GPT-3, a language model with 175 billion parameters. Training such a massive model, which would have taken months on conventional CPUs, was completed in a matter of days using powerful GPUs, showcasing the transformative impact of GPUs in accelerating complex machine learning tasks.\n\nAdvantages\n\nHigh Computational Throughput\nThe key advantage of GPUs is their ability to perform massively parallel floating-point calculations optimized for computer graphics and linear algebra (Raina, Madhavan, and Ng 2009). Modern GPUs like Nvidia’s A100 offer up to 19.5 teraflops of FP32 performance with 6912 CUDA cores and 40GB of graphics memory tightly coupled with 1.6TB/s of graphics memory bandwidth.\n\nRaina, Rajat, Anand Madhavan, and Andrew Y. Ng. 2009. “Large-Scale Deep Unsupervised Learning Using Graphics Processors.” In Proceedings of the 26th Annual International Conference on Machine Learning, edited by Andrea Pohoreckyj Danyluk, Léon Bottou, and Michael L. Littman, 382:873–80. ACM International Conference Proceeding Series. ACM. https://doi.org/10.1145/1553374.1553486.\nThis raw throughput stems from the highly parallel streaming multiprocessor (SM) architecture tailored for data-parallel workloads (Zhihao Jia, Zaharia, and Aiken 2019). Each SM contains hundreds of scalar cores optimized for float32/64 math. With thousands of SMs on a chip, GPUs are purpose-built for matrix multiplication and vector operations used throughout neural networks.\nFor example, Nvidia’s latest H100 GPU provides 4000 TFLOPs of FP8, 2000 TFLOPs of FP16, 1000 TFLOPs of TF32, 67 TFLOPs of FP32 and 34 TFLOPs of FP64 compute performance, which can dramatically accelerate large batch training on models like BERT, GPT-3, and other transformer architectures. The scalable parallelism of GPUs is key to speeding up computationally intensive deep learning.\n\n\nMature Software Ecosystem\nNvidia provides extensive runtime libraries like cuDNN and cuBLAS that are highly optimized for deep learning primitives. Frameworks like TensorFlow and PyTorch integrate with these libraries to enable GPU acceleration without direct programming. These libraries are built on top of CUDA, Nvidia’s parallel computing platform and programming model.\nCUDA (Compute Unified Device Architecture) is the underlying framework that allows these high-level libraries to interact with the GPU’s hardware. It provides developers with low-level access to the GPU’s resources, enabling custom computations and optimizations that fully leverage the GPU’s parallel processing capabilities. By using CUDA, developers can write software that exploits the GPU’s architecture for high-performance computing tasks.\nThis ecosystem enables quick leveraging of GPUs via high-level Python without GPU programming expertise. Known workflows and abstractions provide a convenient on-ramp for scaling up deep learning experiments. The software maturity supplements the throughput advantages.\n\n\nBroad Availability\nThe economies of scale of graphics processing make GPUs broadly accessible in data centers, cloud platforms like AWS and GCP, and desktop workstations. Their availability in research environments has provided a convenient ML experimentation and innovation platform. For example, nearly every state-of-the-art deep learning result has involved GPU acceleration because of this ubiquity. The broad access supplements the software maturity to make GPUs the standard ML accelerator.\n\n\nProgrammable Architecture\nWhile not as flexible as FPGAs, GPUs provide programmability via CUDA and shader languages to customize computations. Developers can optimize data access patterns, create new ops, and tune precisions for evolving models and algorithms.\n\n\n\nDisadvantages\nWhile GPUs have become the standard accelerator for deep learning, their architecture has some key downsides.\n\nLess Efficient than Custom ASICs\nThe statement “GPUs are less efficient than ASICs” could spark intense debate within the ML/AI field and cause this book to explode.\nTypically, GPUs are perceived as less efficient than ASICs because the latter are custom-built for specific tasks and thus can operate more efficiently by design. With their general-purpose architecture, GPUs are inherently more versatile and programmable, catering to a broad spectrum of computational tasks beyond ML/AI.\nHowever, modern GPUs have evolved to include specialized hardware support for essential AI operations, such as generalized matrix multiplication (GEMM) and other matrix operations, native support for quantization, and native support for pruning, which are critical for running ML models effectively. These enhancements have significantly improved the efficiency of GPUs for AI tasks to the point where they can rival the performance of ASICs for certain applications.\nConsequently, contemporary GPUs are convergent, incorporating specialized ASIC-like capabilities within a flexible, general-purpose processing framework. This adaptability has blurred the lines between the two types of hardware. GPUs offer a strong balance of specialization and programmability that is well-suited to the dynamic needs of ML/AI research and development.\n\n\nHigh Memory Bandwidth Needs\nThe massively parallel architecture requires tremendous memory bandwidth to supply thousands of cores. For example, the Nvidia A100 GPU requires 1.6TB/sec to fully saturate its computer. GPUs rely on wide 384-bit memory buses to high-bandwidth GDDR6 RAM, but even the fastest GDDR6 tops out at around 1 TB/sec. This dependence on external DRAM incurs latency and power overheads.\n\n\nProgramming Complexity\nWhile tools like CUDA help, optimally mapping and partitioning ML workloads across the massively parallel GPU architecture remains challenging, achieving both high utilization and memory locality requires low-level tuning (Zhe Jia et al. 2018). Abstractions like TensorFlow can leave performance on the table.\n\nJia, Zhe, Marco Maggioni, Benjamin Staiger, and Daniele P. Scarpazza. 2018. “Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking.” ArXiv Preprint. https://arxiv.org/abs/1804.06826.\n\n\nLimited On-Chip Memory\nGPUs have relatively small on-chip memory caches compared to ML models’ large working set requirements during training. They rely on high bandwidth access to external DRAM, which ASICs minimize with large on-chip SRAM.\n\n\nFixed Architecture\nUnlike FPGAs, the fundamental GPU architecture cannot be altered post-manufacture. This constraint limits adapting to novel ML workloads or layers. The CPU-GPU boundary also creates data movement overheads.\n\n\n\n\n10.3.5 Central Processing Units (CPUs)\nThe term CPUs has a long history that dates back to 1955 (Weik 1955) while the first microprocessor CPU-the Intel 4004-was invented in 1971 (Who Invented the Microprocessor?). Compilers compile high-level programming languages like Python, Java, or C to assemble instructions (x86, ARM, RISC-V, etc.) for CPUs to process. The set of instructions a CPU understands is called the “instruction set architecture” (ISA), which defines the commands that the processor can execute directly. It must be agreed upon by both the hardware and software running atop it.\n\nWeik, Martin H. 1955. A Survey of Domestic Electronic Digital Computing Systems. Ballistic Research Laboratories.\nAn overview of significant developments in CPUs:\n\nSingle-core Era (1950s- 2000): This era is known for aggressive microarchitectural improvements. Techniques like speculative execution (executing an instruction before the previous one was done), out-of-order execution (re-ordering instructions to be more effective), and wider issue widths (executing multiple instructions at once) were implemented to increase instruction throughput. The term “System on Chip” also originated in this era as different analog components (components designed with transistors) and digital components (components designed with hardware description languages that are mapped to transistors) were put on the same platform to achieve some task.\nMulticore Era (2000s): Driven by the decrease of Moore’s Law, this era is marked by scaling the number of cores within a CPU. Now, tasks can be split across many different cores, each with its own datapath and control unit. Many of the issues in this era pertained to how to share certain resources, which resources to share, and how to maintain coherency and consistency across all the cores.\nSea of accelerators (2010s): Again, driven by the decrease of Moore’s law, this era is marked by offloading more complicated tasks to accelerators (widgets) attached to the main datapath in CPUs. It’s common to see accelerators dedicated to various AI workloads, as well as image/digital processing, and cryptography. In these designs, CPUs are often described more as judges, deciding which tasks should be processed rather than doing the processing itself. Any task could still be run on the CPU rather than the accelerators, but the CPU would generally be slower. However, the cost of designing and programming the accelerator became a non-trivial hurdle that sparked interest in design-specific libraries (DSLs).\nPresence in data centers: Although we often hear that GPUs dominate the data center marker, CPUs are still well suited for tasks that don’t inherently possess a large amount of parallelism. CPUs often handle serial and small tasks and coordinate the data center.\nOn the edge: Given the tighter resource constraints on the edge, edge CPUs often only implement a subset of the techniques developed in the sing-core era because these optimizations tend to be heavy on power and area consumption. Edge CPUs still maintain a relatively simple datapath with limited memory capacities.\n\nTraditionally, CPUs have been synonymous with general-purpose computing, a term that has also changed as the “average” workload a consumer would run changes over time. For example, floating point components were once considered reserved for “scientific computing,” they were usually implemented as a co-processor (a modular component that worked with the datapath) and seldom deployed to average consumers. Compare this attitude to today, where FPUs are built into every datapath.\n\nAdvantages\nWhile raw throughput is limited, general-purpose CPUs provide practical AI acceleration benefits.\n\nGeneral Programmability\nCPUs support diverse workloads beyond ML, providing flexible general-purpose programmability. This versatility comes from their standardized instruction sets and mature compiler ecosystems, which allow running any application, from databases and web servers to analytics pipelines (Hennessy and Patterson 2019).\n\nHennessy, John L., and David A. Patterson. 2019. “A New Golden Age for Computer Architecture.” Commun. ACM 62 (2): 48–60. https://doi.org/10.1145/3282307.\nThis avoids the need for dedicated ML accelerators and enables leveraging existing CPU-based infrastructure for basic ML deployment. For example, X86 servers from vendors like Intel and AMD can run common ML frameworks using Python and TensorFlow packages alongside other enterprise workloads.\n\n\nMature Software Ecosystem\nFor decades, highly optimized math libraries like BLAS, LAPACK, and FFTW have leveraged vectorized instructions and multithreading on CPUs (Dongarra 2009). Major ML frameworks like PyTorch, TensorFlow, and SciKit-Learn are designed to integrate seamlessly with these CPU math kernels.\n\nDongarra, Jack J. 2009. “The Evolution of High Performance Computing on System z.” IBM J. Res. Dev. 53: 3–4.\nHardware vendors like Intel and AMD also provide low-level libraries to optimize performance for deep learning primitives fully (AI Inference Acceleration on CPUs). This robust, mature software ecosystem allows quickly deploying ML on existing CPU infrastructure.\n\n\nWide Availability\nThe economies of scale of CPU manufacturing, driven by demand across many markets like PCs, servers, and mobile, make them ubiquitously available. Intel CPUs, for example, have powered most servers for decades (Ranganathan 2011). This wide availability in data centers reduces hardware costs for basic ML deployment.\n\nRanganathan, Parthasarathy. 2011. “From Microprocessors to Nanostores: Rethinking Data-Centric Systems.” Computer 44 (1): 39–48. https://doi.org/10.1109/mc.2011.18.\nEven small embedded devices typically integrate some CPU, enabling edge inference. The ubiquity reduces the need to purchase specialized ML accelerators in many situations.\n\n\nLow Power for Inference\nOptimizations like ARM Neon and Intel AVX vector extensions provide power-efficient integer and floating point throughput optimized for “bursty” workloads such as inference (Ignatov et al. 2018). While slower than GPUs, CPU inference can be deployed in power-constrained environments. For example, ARM’s Cortex-M CPUs now deliver over 1 TOPS of INT8 performance under 1W, enabling keyword spotting and vision applications on edge devices (ARM).\n\n\n\nDisadvantages\nWhile providing some advantages, general-purpose CPUs also have limitations for AI workloads.\n\nLower Throughput than Accelerators\nCPUs lack the specialized architectures for massively parallel processing that GPUs and other accelerators provide. Their general-purpose design reduces computational throughput for the highly parallelizable math operations common in ML models (N. P. Jouppi et al. 2017a).\n\nJouppi, Norman P., Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, et al. 2017a. “In-Datacenter Performance Analysis of a Tensor Processing Unit.” In Proceedings of the 44th Annual International Symposium on Computer Architecture, 1–12. ISCA ’17. New York, NY, USA: ACM. https://doi.org/10.1145/3079856.3080246.\n\n\nNot Optimized for Data Parallelism\nThe architectures of CPUs are not specifically optimized for data parallel workloads inherent to AI (Sze et al. 2017). They allocate substantial silicon area to instruction decoding, speculative execution, caching, and flow control that provides little benefit for the array operations used in neural networks (AI Inference Acceleration on CPUs). However, modern CPUs are equipped with vector instructions like AVX-512 specifically to accelerate certain key operations like matrix multiplication.\nGPU streaming multiprocessors, for example, devote most transistors to floating point units instead of complex branch prediction logic. This specialization allows much higher utilization for ML math.\n\n\nHigher Memory Latency\nCPUs suffer from higher latency accessing main memory relative to GPUs and other accelerators (DDR). Techniques like tiling and caching can help, but the physical separation from off-chip RAM bottlenecks data-intensive ML workloads. This emphasizes the need for specialized memory architectures in ML hardware.\n\n\nPower Inefficiency Under Heavy Workloads\nWhile suitable for intermittent inference, sustaining near-peak throughput for training results in inefficient power consumption on CPUs, especially mobile CPUs (Ignatov et al. 2018). Accelerators explicitly optimize the data flow, memory, and computation for sustained ML workloads. CPUs are energy-inefficient for training large models.\n\n\n\n\n10.3.6 Comparison\nTable 10.2 compares the different types of hardware features.\n\n\n\nTable 10.2: Comparison of different hardware accelerators for AI workloads.\n\n\n\n\n\n\n\n\n\n\n\nAccelerator\nDescription\nKey Advantages\nKey Disadvantages\n\n\n\n\nASICs\nCustom ICs designed for target workloads like AI inference\n\nMaximizes perf/watt.\nOptimized for tensor ops\nLow latency on-chip memory\n\n\nFixed architecture lacks flexibility\nHigh NRE cost\nLong design cycles\n\n\n\nFPGAs\nReconfigurable fabric with programmable logic and routing\n\nFlexible architecture\nLow latency memory access\n\n\nLower perf/watt than ASICs\nComplex programming\n\n\n\nGPUs\nOriginally for graphics, now used for neural network acceleration\n\nHigh throughput\nParallel scalability\nSoftware ecosystem with CUDA\n\n\nNot as power efficient as ASICs\nRequire high memory bandwidth\n\n\n\nCPUs\nGeneral purpose processors\n\nProgrammability\nUbiquitous availability\n\n\nLower performance for AI workloads\n\n\n\n\n\n\n\nIn general, CPUs provide a readily available baseline, GPUs deliver broadly accessible acceleration, FPGAs offer programmability, and ASICs maximize efficiency for fixed functions. The optimal choice depends on the target application’s scale, cost, flexibility, and other requirements.\nAlthough first developed for data center deployment, Google has also put considerable effort into developing Edge TPUs. These Edge TPUs maintain the inspiration from systolic arrays but are tailored to the limited resources accessible at the edge.", "crumbs": [ "Training", "10  AI Acceleration" @@ -1093,7 +1093,7 @@ "href": "contents/hw_acceleration/hw_acceleration.html#emerging-technologies", "title": "10  AI Acceleration", "section": "10.8 Emerging Technologies", - "text": "10.8 Emerging Technologies\nThus far, we have discussed AI hardware technology in the context of conventional von Neumann architecture design and CMOS-based implementation. These specialized AI chips offer benefits like higher throughput and power efficiency but rely on traditional computing principles. The relentless growth in demand for AI computing power is driving innovations in integration methods for AI hardware.\nTwo leading approaches have emerged for maximizing compute density—wafer-scale integration and chiplet-based architectures—which we will discuss in this section. Looking much further ahead, we will examine emerging technologies that diverge from conventional architectures and adopt fundamentally different approaches for AI-specialized computing.\nSome of these unconventional paradigms include neuromorphic computing, which mimics biological neural networks; quantum computing, which leverages quantum mechanical effects; and optical computing, which utilizes photons instead of electrons. Beyond novel computing substrates, new device technologies are enabling additional gains through better memory and interconnecting.\nExamples include memristors for in-memory computing and nanophotonics for integrated photonic communication. Together, these technologies offer the potential for orders of magnitude improvements in speed, efficiency, and scalability compared to current AI hardware. We will examine these in this section.\n\n10.8.1 Integration Methods\nIntegration methods refer to the approaches used to combine and interconnect an AI chip or system’s various computational and memory components. By closely linking the key processing elements, integration aims to maximize performance, power efficiency, and density.\nIn the past, AI computing was primarily performed on CPUs and GPUs built using conventional integration methods. These discrete components were manufactured separately and connected together on a board. However, this loose integration creates bottlenecks, such as data transfer overheads.\nAs AI workloads have grown, there is increasing demand for tighter integration between computing, memory, and communication elements. Some key drivers of integration include:\n\nMinimizing data movement: Tight integration reduces latency and power for moving data between components. This improves efficiency.\nCustomization: Tailoring all system components to AI workloads allows optimizations throughout the hardware stack.\nParallelism: Integrating many processing elements enables massively parallel computation.\nDensity: Tighter integration allows more transistors and memory to be packed into a given area.\nCost: Economies of scale from large integrated systems can reduce costs.\n\nIn response, new manufacturing techniques like wafer-scale fabrication and advanced packaging now allow much higher levels of integration. The goal is to create unified, specialized AI compute complexes tailored for deep learning and other AI algorithms. Tighter integration is key to delivering the performance and efficiency needed for the next generation of AI.\n\nWafer-scale AI\nWafer-scale AI takes an extremely integrated approach, manufacturing an entire silicon wafer as one gigantic chip. This differs drastically from conventional CPUs and GPUs, which cut each wafer into many smaller individual chips. Figure 10.4 shows a comparison between Cerebras Wafer Scale Engine 2, which is the largest chip ever built, and the largest GPU. While some GPUs may contain billions of transistors, they still pale in Comparison to the scale of a wafer-size chip with over a trillion transistors.\nThe wafer-scale approach also diverges from more modular system-on-chip designs that still have discrete components communicating by bus. Instead, wafer-scale AI enables full customization and tight integration of computation, memory, and interconnects across the entire die.\n\n\n\n\n\n\nFigure 10.4: Wafer-scale vs. GPU. Source: Cerebras.\n\n\n\nBy designing the wafer as one integrated logic unit, data transfer between elements is minimized. This provides lower latency and power consumption than discrete system-on-chip or chiplet designs. While chiplets can offer flexibility by mixing and matching components, communication between chiplets is challenging. The monolithic nature of wafer-scale integration eliminates these inter-chip communication bottlenecks.\nHowever, the ultra-large-scale also poses difficulties for manufacturability and yield with wafer-scale designs. Defects in any region of the wafer can make (certain parts of) the chip unusable. Specialized lithography techniques are required to produce such large dies. So, wafer-scale integration pursues the maximum performance gains from integration but requires overcoming substantial fabrication challenges.\nVideo 10.1 provides additional context about wafer-scale AI chips.\n\n\n\n\n\n\nVideo 10.1: Wafer-scale AI Chips\n\n\n\n\n\n\n\n\nChiplets for AI\nChiplet design refers to a semiconductor architecture in which a single integrated circuit (IC) is constructed from multiple smaller, individual components known as chiplets. Each chiplet is a self-contained functional block, typically specialized for a specific task or functionality. These chiplets are then interconnected on a larger substrate or package to create a cohesive system. Figure 10.5 illustrates this concept. For AI hardware, chiplets enable the mixing of different types of chips optimized for tasks like matrix multiplication, data movement, analog I/O, and specialized memories. This heterogeneous integration differs greatly from wafer-scale integration, where all logic is manufactured as one monolithic chip. Companies like Intel and AMD have adopted chiplet designs for their CPUs.\nChiplets are interconnected using advanced packaging techniques like high-density substrate interposers, 2.5D/3D stacking, and wafer-level packaging. This allows combining chiplets fabricated with different process nodes, specialized memories, and various optimized AI engines.\n\n\n\n\n\n\nFigure 10.5: Chiplet partitioning. Source: Vivet et al. (2021).\n\n\nVivet, Pascal, Eric Guthmuller, Yvain Thonnart, Gael Pillonnet, Cesar Fuguet, Ivan Miro-Panades, Guillaume Moritz, et al. 2021. “IntAct: A 96-Core Processor with Six Chiplets 3D-Stacked on an Active Interposer with Distributed Interconnects and Integrated Power Management.” IEEE J. Solid-State Circuits 56 (1): 79–97. https://doi.org/10.1109/jssc.2020.3036341.\n\n\nSome key advantages of using chiplets for AI include:\n\nFlexibility: Chiplets allow for the combination of different chip types, process nodes, and memories tailored for each function. This is more modular versus a fixed wafer-scale design.\nYield: Smaller chiplets have a higher yield than a gigantic wafer-scale chip. Defects are contained in individual chiplets.\nCost: Leverages existing manufacturing capabilities versus requiring specialized new processes. Reduces costs by reusing mature fabrication.\nCompatibility: Can integrate with more conventional system architectures like PCIe and standard DDR memory interfaces.\n\nHowever, chiplets also face integration and performance challenges:\n\nLower density compared to wafer-scale, as chiplets are limited in size.\nAdded latency when communicating between chiplets versus monolithic integration. Requires optimization for low-latency interconnect.\nAdvanced packaging adds complexity versus wafer-scale integration, though this is arguable.\n\nThe key objective of chiplets is finding the right balance between modular flexibility and integration density for optimal AI performance. Chiplets aim for efficient AI acceleration while working within the constraints of conventional manufacturing techniques. Chiplets take a middle path between the extremes of wafer-scale integration and fully discrete components. This provides practical benefits but may sacrifice some computational density and efficiency versus a theoretical wafer-size system.\n\n\n\n10.8.2 Neuromorphic Computing\nNeuromorphic computing is an emerging field aiming to emulate the efficiency and robustness of biological neural systems for machine learning applications. A key difference from classical Von Neumann architectures is the merging of memory and processing in the same circuit (Schuman et al. 2022; Marković et al. 2020; Furber 2016), as illustrated in Figure 10.6. The structure of the brain inspires this integrated approach. A key advantage is the potential for orders of magnitude improvement in energy-efficient computation compared to conventional AI hardware. For example, estimates project 100x-1000x gains in energy efficiency versus current GPU-based systems for equivalent workloads.\n\nMarković, Danijela, Alice Mizrahi, Damien Querlioz, and Julie Grollier. 2020. “Physics for Neuromorphic Computing.” Nature Reviews Physics 2 (9): 499–510. https://doi.org/10.1038/s42254-020-0208-2.\n\nFurber, Steve. 2016. “Large-Scale Neuromorphic Computing Systems.” J. Neural Eng. 13 (5): 051001. https://doi.org/10.1088/1741-2560/13/5/051001.\n\n\n\n\n\n\nFigure 10.6: Comparison of the von Neumann architecture with the neuromorphic architecture. Source: Schuman et al. (2022).\n\n\nSchuman, Catherine D., Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, Prasanna Date, and Bill Kay. 2022. “Opportunities for Neuromorphic Computing Algorithms and Applications.” Nature Computational Science 2 (1): 10–19. https://doi.org/10.1038/s43588-021-00184-y.\n\n\nIntel and IBM are leading commercial efforts in neuromorphic hardware. Intel’s Loihi and Loihi 2 chips (Davies et al. 2018, 2021) offer programmable neuromorphic cores with on-chip learning. IBM’s Northpole (Modha et al. 2023) device comprises over 100 million magnetic tunnel junction synapses and 68 billion transistors. These specialized chips deliver benefits like low power consumption for edge inference.\n\nDavies, Mike, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, et al. 2018. “Loihi: A Neuromorphic Manycore Processor with on-Chip Learning.” IEEE Micro 38 (1): 82–99. https://doi.org/10.1109/mm.2018.112130359.\n\nDavies, Mike, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya, Gabriel A. Fonseca Guerra, Prasad Joshi, Philipp Plank, and Sumedh R. Risbud. 2021. “Advancing Neuromorphic Computing with Loihi: A Survey of Results and Outlook.” Proc. IEEE 109 (5): 911–34. https://doi.org/10.1109/jproc.2021.3067593.\n\nModha, Dharmendra S., Filipp Akopyan, Alexander Andreopoulos, Rathinakumar Appuswamy, John V. Arthur, Andrew S. Cassidy, Pallab Datta, et al. 2023. “Neural Inference at the Frontier of Energy, Space, and Time.” Science 382 (6668): 329–35. https://doi.org/10.1126/science.adh1174.\n\nMaass, Wolfgang. 1997. “Networks of Spiking Neurons: The Third Generation of Neural Network Models.” Neural Networks 10 (9): 1659–71. https://doi.org/10.1016/s0893-6080(97)00011-7.\nSpiking neural networks (SNNs) (Maass 1997) are computational models for neuromorphic hardware. Unlike deep neural networks communicating via continuous values, SNNs use discrete spikes that are more akin to biological neurons. This allows efficient event-based computation rather than constant processing. Additionally, SNNs consider the temporal and spatial characteristics of input data. This better mimics biological neural networks, where the timing of neuronal spikes plays an important role. However, training SNNs remains challenging due to the added temporal complexity. Figure 10.7 provides an overview of the spiking methodology: (a) Diagram of a neuron; (b) Measuring an action potential propagated along the axon of a neuron. Only the action potential is detectable along the axon; (c) The neuron’s spike is approximated with a binary representation; (d) Event-Driven Processing; (e) Active Pixel Sensor and Dynamic Vision Sensor.\n\n\n\n\n\n\nFigure 10.7: Neuromorphic spiking. Source: Eshraghian et al. (2023).\n\n\nEshraghian, Jason K., Max Ward, Emre O. Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. 2023. “Training Spiking Neural Networks Using Lessons from Deep Learning.” Proc. IEEE 111 (9): 1016–54. https://doi.org/10.1109/jproc.2023.3308088.\n\n\nYou can also watch Video 10.2 linked below for a more detailed explanation.\n\n\n\n\n\n\nVideo 10.2: Neuromorphic Computing\n\n\n\n\n\n\nSpecialized nanoelectronic devices called memristors (Chua 1971) are synaptic components in neuromorphic systems. Memristors act as nonvolatile memory with adjustable conductance, emulating the plasticity of real synapses. Memristors enable in-situ learning without separate data transfers by combining memory and processing functions. However, memristor technology has yet to reach maturity and scalability for commercial hardware.\n\nChua, L. 1971. “Memristor-the Missing Circuit Element.” #IEEE_J_CT# 18 (5): 507–19. https://doi.org/10.1109/tct.1971.1083337.\nThe integration of photonics with neuromorphic computing (Shastri et al. 2021) has recently emerged as an active research area. Using light for computation and communication allows high speeds and reduced energy consumption. However, fully realizing photonic neuromorphic systems requires overcoming design and integration challenges.\nNeuromorphic computing offers promising capabilities for efficient edge inference but faces obstacles around training algorithms, nanodevice integration, and system design. Ongoing multidisciplinary research across computer science, engineering, materials science, and physics will be key to unlocking this technology’s full potential for AI use cases.\n\n\n10.8.3 Analog Computing\nAnalog computing is an emerging approach that uses analog signals and components like capacitors, inductors, and amplifiers rather than digital logic for computing. It represents information as continuous electrical signals instead of discrete 0s and 1s. This allows the computation to directly reflect the analog nature of real-world data, avoiding digitization errors and overhead.\nAnalog computing has generated renewed interest in efficient AI hardware, particularly for inference directly on low-power edge devices. Analog circuits, such as multiplication and summation at the core of neural networks, can be used with very low energy consumption. This makes analog well-suited for deploying ML models on energy-constrained end nodes. Startups like Mythic are developing analog AI accelerators.\nWhile analog computing was popular in early computers, the boom of digital logic led to its decline. However, analog is compelling for niche applications requiring extreme efficiency (Haensch, Gokmen, and Puri 2019). It contrasts with digital neuromorphic approaches that still use digital spikes for computation. Analog may allow lower precision computation but requires expertise in analog circuit design. Tradeoffs around precision, programming complexity, and fabrication costs remain active research areas.\n\nHaensch, Wilfried, Tayfun Gokmen, and Ruchir Puri. 2019. “The Next Generation of Deep Learning Hardware: Analog Computing.” Proc. IEEE 107 (1): 108–22. https://doi.org/10.1109/jproc.2018.2871057.\n\nHazan, Avi, and Elishai Ezra Tsur. 2021. “Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation.” Front. Neurosci. 15 (February): 627221. https://doi.org/10.3389/fnins.2021.627221.\nNeuromorphic computing, which aims to emulate biological neural systems for efficient ML inference, can use analog circuits to implement the key components and behaviors of brains. For example, researchers have designed analog circuits to model neurons and synapses using capacitors, transistors, and operational amplifiers (Hazan and Ezra Tsur 2021). The capacitors can exhibit the spiking dynamics of biological neurons, while the amplifiers and transistors provide a weighted summation of inputs to mimic dendrites. Variable resistor technologies like memristors can realize analog synapses with spike-timing-dependent plasticity, which can strengthen or weaken connections based on spiking activity.\nStartups like SynSense have developed analog neuromorphic chips containing these biomimetic components (Bains 2020). This analog approach results in low power consumption and high scalability for edge devices versus complex digital SNN implementations.\n\nBains, Sunny. 2020. “The Business of Building Brains.” Nature Electronics 3 (7): 348–51. https://doi.org/10.1038/s41928-020-0449-1.\nHowever, training analog SNNs on chips remains an open challenge. Overall, analog realization is a promising technique for delivering the efficiency, scalability, and biological plausibility envisioned with neuromorphic computing. The physics of analog components combined with neural architecture design could improve inference efficiency over conventional digital neural networks.\n\n\n10.8.4 Flexible Electronics\nWhile much of the new hardware technology in the ML workspace has been focused on optimizing and making systems more efficient, there’s a parallel trajectory aiming to adapt hardware for specific applications (Gates 2009; Musk et al. 2019; Tang et al. 2023; Tang, He, and Liu 2022; Kwon and Dong 2022). One such avenue is the development of flexible electronics for AI use cases.\n\nGates, Byron D. 2009. “Flexible Electronics.” Science 323 (5921): 1566–67. https://doi.org/10.1126/science.1171230.\n\nTang, Xin, Hao Shen, Siyuan Zhao, Na Li, and Jia Liu. 2023. “Flexible Braincomputer Interfaces.” Nature Electronics 6 (2): 109–18. https://doi.org/10.1038/s41928-022-00913-9.\n\nTang, Xin, Yichun He, and Jia Liu. 2022. “Soft Bioelectronics for Cardiac Interfaces.” Biophysics Reviews 3 (1). https://doi.org/10.1063/5.0069516.\nFlexible electronics refer to electronic circuits and devices fabricated on flexible plastic or polymer substrates rather than rigid silicon. Unlike conventional rigid boards and chips, this allows the electronics to bend, twist, and conform to irregular shapes. Figure 10.8 shows an example of a flexible device prototype that wirelessly measures body temperature, which can be seamlessly integrated into clothing or skin patches. The flexibility and bendability of emerging electronic materials allow them to be integrated into thin, lightweight form factors that are well-suited for embedded AI and TinyML applications.\nFlexible AI hardware can conform to curvy surfaces and operate efficiently with microwatt power budgets. Flexibility also enables rollable or foldable form factors to minimize device footprint and weight, ideal for small, portable smart devices and wearables incorporating TinyML. Another key advantage of flexible electronics compared to conventional technologies is lower manufacturing costs and simpler fabrication processes, which could democratize access to these technologies. While silicon masks and fabrication costs typically cost millions of dollars, flexible hardware typically costs only tens of cents to manufacture (Huang et al. 2011; Biggs et al. 2021). The potential to fabricate flexible electronics directly onto plastic films using high-throughput printing and coating processes can reduce costs and improve manufacturability at scale versus rigid AI chips (Musk et al. 2019).\n\nHuang, Tsung-Ching, Kenjiro Fukuda, Chun-Ming Lo, Yung-Hui Yeh, Tsuyoshi Sekitani, Takao Someya, and Kwang-Ting Cheng. 2011. “Pseudo-CMOS: A Design Style for Low-Cost and Robust Flexible Electronics.” IEEE Trans. Electron Devices 58 (1): 141–50. https://doi.org/10.1109/ted.2010.2088127.\n\nBiggs, John, James Myers, Jedrzej Kufel, Emre Ozer, Simon Craske, Antony Sou, Catherine Ramsdale, Ken Williamson, Richard Price, and Scott White. 2021. “A Natively Flexible 32-Bit Arm Microprocessor.” Nature 595 (7868): 532–36. https://doi.org/10.1038/s41586-021-03625-w.\n\n\n\n\n\n\nFigure 10.8: Flexible device prototype. Source: Jabil Circuit.\n\n\n\nThe field is enabled by advances in organic semiconductors and nanomaterials that can be deposited on thin, flexible films. However, fabrication remains challenging compared to mature silicon processes. Flexible circuits currently typically exhibit lower performance than rigid equivalents. Still, they promise to transform electronics into lightweight, bendable materials.\nFlexible electronics use cases are well-suited for intimate integration with the human body. Potential medical AI applications include bio-integrated sensors, soft assistive robots, and implants that monitor or stimulate the nervous system intelligently. Specifically, flexible electrode arrays could enable higher-density, less-invasive neural interfaces compared to rigid equivalents.\nTherefore, flexible electronics are ushering in a new era of wearables and body sensors, largely due to innovations in organic transistors. These components allow for more lightweight and bendable electronics, ideal for wearables, electronic skin, and body-conforming medical devices.\nThey are well-suited for bioelectronic devices in terms of biocompatibility, opening avenues for applications in brain and cardiac interfaces. For example, research in flexible brain-computer interfaces and soft bioelectronics for cardiac applications demonstrates the potential for wide-ranging medical applications.\nCompanies and research institutions are not only developing and investing great amounts of resources in flexible electrodes, as showcased in Neuralink’s work (Musk et al. 2019). Still, they are also pushing the boundaries to integrate machine learning models within the systems (Kwon and Dong 2022). These smart sensors aim for a seamless, long-lasting symbiosis with the human body.\n\nMusk, Elon et al. 2019. “An Integrated Brain-Machine Interface Platform with Thousands of Channels.” J. Med. Internet Res. 21 (10): e16194. https://doi.org/10.2196/16194.\n\nKwon, Sun Hwa, and Lin Dong. 2022. “Flexible Sensors and Machine Learning for Heart Monitoring.” Nano Energy 102 (November): 107632. https://doi.org/10.1016/j.nanoen.2022.107632.\n\nSegura Anaya, L. H., Abeer Alsadoon, N. Costadopoulos, and P. W. C. Prasad. 2017. “Ethical Implications of User Perceptions of Wearable Devices.” Sci. Eng. Ethics 24 (1): 1–28. https://doi.org/10.1007/s11948-017-9872-8.\n\nGoodyear, Victoria A. 2017. “Social Media, Apps and Wearable Technologies: Navigating Ethical Dilemmas and Procedures.” Qualitative Research in Sport, Exercise and Health 9 (3): 285–302. https://doi.org/10.1080/2159676x.2017.1303790.\n\nFarah, Martha J. 2005. “Neuroethics: The Practical and the Philosophical.” Trends Cogn. Sci. 9 (1): 34–40. https://doi.org/10.1016/j.tics.2004.12.001.\n\nRoskies, Adina. 2002. “Neuroethics for the New Millenium.” Neuron 35 (1): 21–23. https://doi.org/10.1016/s0896-6273(02)00763-8.\nEthically, incorporating smart, machine-learning-driven sensors within the body raises important questions. Issues surrounding data privacy, informed consent, and the long-term societal implications of such technologies are the focus of ongoing work in neuroethics and bioethics (Segura Anaya et al. 2017; Goodyear 2017; Farah 2005; Roskies 2002). The field is progressing at a pace that necessitates parallel advancements in ethical frameworks to guide the responsible development and deployment of these technologies. While there are limitations and ethical hurdles to overcome, the prospects for flexible electronics are expansive and hold immense promise for future research and applications.\n\n\n10.8.5 Memory Technologies\nMemory technologies are critical to AI hardware, but conventional DDR DRAM and SRAM create bottlenecks. AI workloads require high bandwidth (>1 TB/s). Extreme scientific applications of AI require extremely low latency (<50 ns) to feed data to compute units (Duarte et al. 2022), high density (>128Gb) to store large model parameters and data sets, and excellent energy efficiency (<100 fJ/b) for embedded use (Verma et al. 2019). New memories are needed to meet these demands. Emerging options include several new technologies:\n\nDuarte, Javier, Nhan Tran, Ben Hawks, Christian Herwig, Jules Muhizi, Shvetank Prakash, and Vijay Janapa Reddi. 2022. “FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning.” ArXiv Preprint abs/2207.07958. https://arxiv.org/abs/2207.07958.\n\nVerma, Naveen, Hongyang Jia, Hossein Valavi, Yinqi Tang, Murat Ozatay, Lung-Yen Chen, Bonan Zhang, and Peter Deaville. 2019. “In-Memory Computing: Advances and Prospects.” IEEE Solid-State Circuits Mag. 11 (3): 43–55. https://doi.org/10.1109/mssc.2019.2922889.\n\nResistive RAM (ReRAM) can improve density with simple, passive arrays. However, challenges around variability remain (Chi et al. 2016).\nPhase change memory (PCM) exploits the unique properties of chalcogenide glass. Crystalline and amorphous phases have different resistances. Intel’s Optane DCPMM provides fast (100ns), high endurance PCM. However, challenges include limited write cycles and high reset current (Burr et al. 2016).\n3D stacking can also boost memory density and bandwidth by vertically integrating memory layers with TSV interconnects (Loh 2008). For example, HBM provides 1024-bit wide interfaces.\n\n\nBurr, Geoffrey W., Matthew J. BrightSky, Abu Sebastian, Huai-Yu Cheng, Jau-Yi Wu, Sangbum Kim, Norma E. Sosa, et al. 2016. “Recent Progress in Phase-Change?Pub _Newline ?Memory Technology.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems 6 (2): 146–62. https://doi.org/10.1109/jetcas.2016.2547718.\n\nLoh, Gabriel H. 2008. “3D-Stacked Memory Architectures for Multi-Core Processors.” ACM SIGARCH Computer Architecture News 36 (3): 453–64. https://doi.org/10.1145/1394608.1382159.\nNew memory technologies, with their innovative cell architectures and materials, are critical to unlocking the next level of AI hardware performance and efficiency. Realizing their benefits in commercial systems remains an ongoing challenge.\nIn-memory computing is gaining traction as a promising avenue for optimizing machine learning and high-performance computing workloads. At its core, the technology co-locates data storage and computation to improve energy efficiency and reduce latency Wong et al. (2012). Two key technologies under this umbrella are Resistive RAM (ReRAM) and Processing-In-Memory (PIM).\n\nWong, H.-S. Philip, Heng-Yuan Lee, Shimeng Yu, Yu-Sheng Chen, Yi Wu, Pang-Shiu Chen, Byoungil Lee, Frederick T. Chen, and Ming-Jinn Tsai. 2012. “MetalOxide RRAM.” Proc. IEEE 100 (6): 1951–70. https://doi.org/10.1109/jproc.2012.2190369.\n\nChi, Ping, Shuangchen Li, Cong Xu, Tao Zhang, Jishen Zhao, Yongpan Liu, Yu Wang, and Yuan Xie. 2016. “Prime: A Novel Processing-in-Memory Architecture for Neural Network Computation in ReRAM-Based Main Memory.” ACM SIGARCH Computer Architecture News 44 (3): 27–39. https://doi.org/10.1145/3007787.3001140.\nReRAM (Wong et al. 2012) and PIM (Chi et al. 2016) are the backbones for in-memory computing, storing and computing data in the same location. ReRAM focuses on issues of uniformity, endurance, retention, multi-bit operation, and scalability. On the other hand, PIM involves CPU units integrated directly into memory arrays, specialized for tasks like matrix multiplication, which are central in AI computations.\nThese technologies find applications in AI workloads and high-performance computing, where the synergy of storage and computation can lead to significant performance gains. The architecture is particularly useful for compute-intensive tasks common in machine learning models.\nWhile in-memory computing technologies like ReRAM and PIM offer exciting prospects for efficiency and performance, they come with their own challenges, such as data uniformity and scalability issues in ReRAM (Imani, Rahimi, and S. Rosing 2016). Nonetheless, the field is ripe for innovation, and addressing these limitations can open new frontiers in AI and high-performance computing.\n\nImani, Mohsen, Abbas Rahimi, and Tajana S. Rosing. 2016. “Resistive Configurable Associative Memory for Approximate Computing.” In Proceedings of the 2016 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 1327–32. IEEE; Research Publishing Services. https://doi.org/10.3850/9783981537079_0454.\n\n\n10.8.6 Optical Computing\nIn AI acceleration, a burgeoning area of interest lies in novel technologies that deviate from traditional paradigms. Some emerging technologies mentioned above, such as flexible electronics, in-memory computing, or even neuromorphic computing, are close to becoming a reality, given their ground-breaking innovations and applications. One of the promising and leading next-gen frontiers is optical computing technologies H. Zhou et al. (2022). Companies like [LightMatter] are pioneering the use of light photonics for calculations, thereby utilizing photons instead of electrons for data transmission and computation.\n\nZhou, Hailong, Jianji Dong, Junwei Cheng, Wenchan Dong, Chaoran Huang, Yichen Shen, Qiming Zhang, et al. 2022. “Photonic Matrix Multiplication Lights up Photonic Accelerator and Beyond.” Light: Science &Amp; Applications 11 (1): 30. https://doi.org/10.1038/s41377-022-00717-8.\n\nShastri, Bhavin J., Alexander N. Tait, T. Ferreira de Lima, Wolfram H. P. Pernice, Harish Bhaskaran, C. D. Wright, and Paul R. Prucnal. 2021. “Photonics for Artificial Intelligence and Neuromorphic Computing.” Nat. Photonics 15 (2): 102–14. https://doi.org/10.1038/s41566-020-00754-y.\nOptical computing utilizes photons and photonic devices rather than traditional electronic circuits for computing and data processing. It takes inspiration from fiber optic communication links that rely on light for fast, efficient data transfer (Shastri et al. 2021). Light can propagate with much less loss than semiconductors’ electrons, enabling inherent speed and efficiency benefits.\nSome specific advantages of optical computing include:\n\nHigh throughput: Photons can transmit with bandwidths >100 Tb/s using wavelength division multiplexing.\nLow latency: Photons interact on femtosecond timescales, millions faster than silicon transistors.\nParallelism: Multiple data signals can propagate simultaneously through the same optical medium.\nLow power: Photonic circuits utilizing waveguides and resonators can achieve complex logic and memory with only microwatts of power.\n\nHowever, optical computing currently faces significant challenges:\n\nLack of optical memory equivalent to electronic RAM\nRequires conversion between optical and electrical domains.\nLimited set of available optical components compared to rich electronics ecosystem.\nImmature integration methods to combine photonics with traditional CMOS chips.\nComplex programming models required to handle parallelism.\n\nAs a result, optical computing is still in the very early research stage despite its promising potential. However, technical breakthroughs could enable it to complement electronics and unlock performance gains for AI workloads. Companies like Lightmatter are pioneering early optical AI accelerators. In the long term, if key challenges are overcome, it could represent a revolutionary computing substrate.\n\n\n10.8.7 Quantum Computing\nQuantum computers leverage unique phenomena of quantum physics, like superposition and entanglement, to represent and process information in ways not possible classically. Instead of binary bits, the fundamental unit is the quantum bit or qubit. Unlike classical bits, which are limited to 0 or 1, qubits can exist simultaneously in a superposition of both states due to quantum effects.\nMultiple qubits can also be entangled, leading to exponential information density but introducing probabilistic results. Superposition enables parallel computation on all possible states, while entanglement allows nonlocal correlations between qubits.\nQuantum algorithms carefully manipulate these inherently quantum mechanical effects to solve problems like optimization or search more efficiently than their classical counterparts in theory.\n\nFaster training of deep neural networks by exploiting quantum parallelism for linear algebra operations.\nEfficient quantum ML algorithms make use of the unique capabilities of qubits.\nQuantum neural networks with inherent quantum effects baked into the model architecture.\nQuantum optimizers leveraging quantum annealing or adiabatic algorithms for combinatorial optimization problems.\n\nHowever, quantum states are fragile and prone to errors that require error-correcting protocols. The non-intuitive nature of quantum programming also introduces challenges not present in classical computing.\n\nNoisy and fragile quantum bits are difficult to scale up. The largest quantum computer today has less than 1000 qubits.\nRestricted set of available quantum gates and circuits relative to classical programming.\nLack of datasets and benchmarks to evaluate quantum ML in practical domains.\n\nWhile meaningful quantum advantage for ML remains far off, active research at companies like D-Wave, Rigetti, and IonQ is advancing quantum computer engineering and quantum algorithms. Major technology companies like Google, IBM, and Microsoft are actively exploring quantum computing. Google recently announced a 72-qubit quantum processor called Bristlecone and plans to build a 49-qubit commercial quantum system. Microsoft also has an active research program in topological quantum computing and collaborates with quantum startup IonQ\nQuantum techniques may first make inroads into optimization before more generalized ML adoption. Realizing quantum ML’s full potential awaits major milestones in quantum hardware development and ecosystem maturity.", + "text": "10.8 Emerging Technologies\nThus far, we have discussed AI hardware technology in the context of conventional von Neumann architecture design and CMOS-based implementation. These specialized AI chips offer benefits like higher throughput and power efficiency but rely on traditional computing principles. The relentless growth in demand for AI computing power is driving innovations in integration methods for AI hardware.\nTwo leading approaches have emerged for maximizing compute density—wafer-scale integration and chiplet-based architectures—which we will discuss in this section. Looking much further ahead, we will examine emerging technologies that diverge from conventional architectures and adopt fundamentally different approaches for AI-specialized computing.\nSome of these unconventional paradigms include neuromorphic computing, which mimics biological neural networks; quantum computing, which leverages quantum mechanical effects; and optical computing, which utilizes photons instead of electrons. Beyond novel computing substrates, new device technologies are enabling additional gains through better memory and interconnecting.\nExamples include memristors for in-memory computing and nanophotonics for integrated photonic communication. Together, these technologies offer the potential for orders of magnitude improvements in speed, efficiency, and scalability compared to current AI hardware. We will examine these in this section.\n\n10.8.1 Integration Methods\nIntegration methods refer to the approaches used to combine and interconnect an AI chip or system’s various computational and memory components. By closely linking the key processing elements, integration tries to maximize performance, power efficiency, and density.\nIn the past, AI computing was primarily performed on CPUs and GPUs built using conventional integration methods. These discrete components were manufactured separately and connected together on a board. However, this loose integration creates bottlenecks, such as data transfer overheads.\nAs AI workloads have grown, there is increasing demand for tighter integration between computing, memory, and communication elements. Some key drivers of integration include:\n\nMinimizing data movement: Tight integration reduces latency and power for moving data between components. This improves efficiency.\nCustomization: Tailoring all system components to AI workloads allows optimizations throughout the hardware stack.\nParallelism: Integrating many processing elements enables massively parallel computation.\nDensity: Tighter integration allows more transistors and memory to be packed into a given area.\nCost: Economies of scale from large integrated systems can reduce costs.\n\nIn response, new manufacturing techniques like wafer-scale fabrication and advanced packaging now allow much higher levels of integration. The goal is to create unified, specialized AI compute complexes tailored for deep learning and other AI algorithms. Tighter integration is key to delivering the performance and efficiency needed for the next generation of AI.\n\nWafer-scale AI\nWafer-scale AI takes an extremely integrated approach, manufacturing an entire silicon wafer as one gigantic chip. This differs drastically from conventional CPUs and GPUs, which cut each wafer into many smaller individual chips. Figure 10.4 shows a comparison between Cerebras Wafer Scale Engine 2, which is the largest chip ever built, and the largest GPU. While some GPUs may contain billions of transistors, they still pale in Comparison to the scale of a wafer-size chip with over a trillion transistors.\nThe wafer-scale approach also diverges from more modular system-on-chip designs that still have discrete components communicating by bus. Instead, wafer-scale AI enables full customization and tight integration of computation, memory, and interconnects across the entire die.\n\n\n\n\n\n\nFigure 10.4: Wafer-scale vs. GPU. Source: Cerebras.\n\n\n\nBy designing the wafer as one integrated logic unit, data transfer between elements is minimized. This provides lower latency and power consumption than discrete system-on-chip or chiplet designs. While chiplets can offer flexibility by mixing and matching components, communication between chiplets is challenging. The monolithic nature of wafer-scale integration eliminates these inter-chip communication bottlenecks.\nHowever, the ultra-large-scale also poses difficulties for manufacturability and yield with wafer-scale designs. Defects in any region of the wafer can make (certain parts of) the chip unusable. Specialized lithography techniques are required to produce such large dies. So, wafer-scale integration pursues the maximum performance gains from integration but requires overcoming substantial fabrication challenges.\nVideo 10.1 provides additional context about wafer-scale AI chips.\n\n\n\n\n\n\nVideo 10.1: Wafer-scale AI Chips\n\n\n\n\n\n\n\n\nChiplets for AI\nChiplet design refers to a semiconductor architecture in which a single integrated circuit (IC) is constructed from multiple smaller, individual components known as chiplets. Each chiplet is a self-contained functional block, typically specialized for a specific task or functionality. These chiplets are then interconnected on a larger substrate or package to create a cohesive system. Figure 10.5 illustrates this concept. For AI hardware, chiplets enable the mixing of different types of chips optimized for tasks like matrix multiplication, data movement, analog I/O, and specialized memories. This heterogeneous integration differs greatly from wafer-scale integration, where all logic is manufactured as one monolithic chip. Companies like Intel and AMD have adopted chiplet designs for their CPUs.\nChiplets are interconnected using advanced packaging techniques like high-density substrate interposers, 2.5D/3D stacking, and wafer-level packaging. This allows combining chiplets fabricated with different process nodes, specialized memories, and various optimized AI engines.\n\n\n\n\n\n\nFigure 10.5: Chiplet partitioning. Source: Vivet et al. (2021).\n\n\nVivet, Pascal, Eric Guthmuller, Yvain Thonnart, Gael Pillonnet, Cesar Fuguet, Ivan Miro-Panades, Guillaume Moritz, et al. 2021. “IntAct: A 96-Core Processor with Six Chiplets 3D-Stacked on an Active Interposer with Distributed Interconnects and Integrated Power Management.” IEEE J. Solid-State Circuits 56 (1): 79–97. https://doi.org/10.1109/jssc.2020.3036341.\n\n\nSome key advantages of using chiplets for AI include:\n\nFlexibility: Chiplets allow for the combination of different chip types, process nodes, and memories tailored for each function. This is more modular versus a fixed wafer-scale design.\nYield: Smaller chiplets have a higher yield than a gigantic wafer-scale chip. Defects are contained in individual chiplets.\nCost: Leverages existing manufacturing capabilities versus requiring specialized new processes. Reduces costs by reusing mature fabrication.\nCompatibility: Can integrate with more conventional system architectures like PCIe and standard DDR memory interfaces.\n\nHowever, chiplets also face integration and performance challenges:\n\nLower density compared to wafer-scale, as chiplets are limited in size.\nAdded latency when communicating between chiplets versus monolithic integration. Requires optimization for low-latency interconnect.\nAdvanced packaging adds complexity versus wafer-scale integration, though this is arguable.\n\nThe key objective of chiplets is finding the right balance between modular flexibility and integration density for optimal AI performance. Chiplets aim for efficient AI acceleration while working within the constraints of conventional manufacturing techniques. Chiplets take a middle path between the extremes of wafer-scale integration and fully discrete components. This provides practical benefits but may sacrifice some computational density and efficiency versus a theoretical wafer-size system.\n\n\n\n10.8.2 Neuromorphic Computing\nNeuromorphic computing is an emerging field aiming to emulate the efficiency and robustness of biological neural systems for machine learning applications. A key difference from classical Von Neumann architectures is the merging of memory and processing in the same circuit (Schuman et al. 2022; Marković et al. 2020; Furber 2016), as illustrated in Figure 10.6. The structure of the brain inspires this integrated approach. A key advantage is the potential for orders of magnitude improvement in energy-efficient computation compared to conventional AI hardware. For example, estimates project 100x-1000x gains in energy efficiency versus current GPU-based systems for equivalent workloads.\n\nMarković, Danijela, Alice Mizrahi, Damien Querlioz, and Julie Grollier. 2020. “Physics for Neuromorphic Computing.” Nature Reviews Physics 2 (9): 499–510. https://doi.org/10.1038/s42254-020-0208-2.\n\nFurber, Steve. 2016. “Large-Scale Neuromorphic Computing Systems.” J. Neural Eng. 13 (5): 051001. https://doi.org/10.1088/1741-2560/13/5/051001.\n\n\n\n\n\n\nFigure 10.6: Comparison of the von Neumann architecture with the neuromorphic architecture. Source: Schuman et al. (2022).\n\n\nSchuman, Catherine D., Shruti R. Kulkarni, Maryam Parsa, J. Parker Mitchell, Prasanna Date, and Bill Kay. 2022. “Opportunities for Neuromorphic Computing Algorithms and Applications.” Nature Computational Science 2 (1): 10–19. https://doi.org/10.1038/s43588-021-00184-y.\n\n\nIntel and IBM are leading commercial efforts in neuromorphic hardware. Intel’s Loihi and Loihi 2 chips (Davies et al. 2018, 2021) offer programmable neuromorphic cores with on-chip learning. IBM’s Northpole (Modha et al. 2023) device comprises over 100 million magnetic tunnel junction synapses and 68 billion transistors. These specialized chips deliver benefits like low power consumption for edge inference.\n\nDavies, Mike, Narayan Srinivasa, Tsung-Han Lin, Gautham Chinya, Yongqiang Cao, Sri Harsha Choday, Georgios Dimou, et al. 2018. “Loihi: A Neuromorphic Manycore Processor with on-Chip Learning.” IEEE Micro 38 (1): 82–99. https://doi.org/10.1109/mm.2018.112130359.\n\nDavies, Mike, Andreas Wild, Garrick Orchard, Yulia Sandamirskaya, Gabriel A. Fonseca Guerra, Prasad Joshi, Philipp Plank, and Sumedh R. Risbud. 2021. “Advancing Neuromorphic Computing with Loihi: A Survey of Results and Outlook.” Proc. IEEE 109 (5): 911–34. https://doi.org/10.1109/jproc.2021.3067593.\n\nModha, Dharmendra S., Filipp Akopyan, Alexander Andreopoulos, Rathinakumar Appuswamy, John V. Arthur, Andrew S. Cassidy, Pallab Datta, et al. 2023. “Neural Inference at the Frontier of Energy, Space, and Time.” Science 382 (6668): 329–35. https://doi.org/10.1126/science.adh1174.\n\nMaass, Wolfgang. 1997. “Networks of Spiking Neurons: The Third Generation of Neural Network Models.” Neural Networks 10 (9): 1659–71. https://doi.org/10.1016/s0893-6080(97)00011-7.\nSpiking neural networks (SNNs) (Maass 1997) are computational models for neuromorphic hardware. Unlike deep neural networks communicating via continuous values, SNNs use discrete spikes that are more akin to biological neurons. This allows efficient event-based computation rather than constant processing. Additionally, SNNs consider the temporal and spatial characteristics of input data. This better mimics biological neural networks, where the timing of neuronal spikes plays an important role. However, training SNNs remains challenging due to the added temporal complexity. Figure 10.7 provides an overview of the spiking methodology: (a) Diagram of a neuron; (b) Measuring an action potential propagated along the axon of a neuron. Only the action potential is detectable along the axon; (c) The neuron’s spike is approximated with a binary representation; (d) Event-Driven Processing; (e) Active Pixel Sensor and Dynamic Vision Sensor.\n\n\n\n\n\n\nFigure 10.7: Neuromorphic spiking. Source: Eshraghian et al. (2023).\n\n\nEshraghian, Jason K., Max Ward, Emre O. Neftci, Xinxin Wang, Gregor Lenz, Girish Dwivedi, Mohammed Bennamoun, Doo Seok Jeong, and Wei D. Lu. 2023. “Training Spiking Neural Networks Using Lessons from Deep Learning.” Proc. IEEE 111 (9): 1016–54. https://doi.org/10.1109/jproc.2023.3308088.\n\n\nYou can also watch Video 10.2 linked below for a more detailed explanation.\n\n\n\n\n\n\nVideo 10.2: Neuromorphic Computing\n\n\n\n\n\n\nSpecialized nanoelectronic devices called memristors (Chua 1971) are synaptic components in neuromorphic systems. Memristors act as nonvolatile memory with adjustable conductance, emulating the plasticity of real synapses. Memristors enable in-situ learning without separate data transfers by combining memory and processing functions. However, memristor technology has yet to reach maturity and scalability for commercial hardware.\n\nChua, L. 1971. “Memristor-the Missing Circuit Element.” #IEEE_J_CT# 18 (5): 507–19. https://doi.org/10.1109/tct.1971.1083337.\nThe integration of photonics with neuromorphic computing (Shastri et al. 2021) has recently emerged as an active research area. Using light for computation and communication allows high speeds and reduced energy consumption. However, fully realizing photonic neuromorphic systems requires overcoming design and integration challenges.\nNeuromorphic computing offers promising capabilities for efficient edge inference but faces obstacles around training algorithms, nanodevice integration, and system design. Ongoing multidisciplinary research across computer science, engineering, materials science, and physics will be key to unlocking this technology’s full potential for AI use cases.\n\n\n10.8.3 Analog Computing\nAnalog computing is an emerging approach that uses analog signals and components like capacitors, inductors, and amplifiers rather than digital logic for computing. It represents information as continuous electrical signals instead of discrete 0s and 1s. This allows the computation to directly reflect the analog nature of real-world data, avoiding digitization errors and overhead.\nAnalog computing has generated renewed interest in efficient AI hardware, particularly for inference directly on low-power edge devices. Analog circuits, such as multiplication and summation at the core of neural networks, can be used with very low energy consumption. This makes analog well-suited for deploying ML models on energy-constrained end nodes. Startups like Mythic are developing analog AI accelerators.\nWhile analog computing was popular in early computers, the boom of digital logic led to its decline. However, analog is compelling for niche applications requiring extreme efficiency (Haensch, Gokmen, and Puri 2019). It contrasts with digital neuromorphic approaches that still use digital spikes for computation. Analog may allow lower precision computation but requires expertise in analog circuit design. Tradeoffs around precision, programming complexity, and fabrication costs remain active research areas.\n\nHaensch, Wilfried, Tayfun Gokmen, and Ruchir Puri. 2019. “The Next Generation of Deep Learning Hardware: Analog Computing.” Proc. IEEE 107 (1): 108–22. https://doi.org/10.1109/jproc.2018.2871057.\n\nHazan, Avi, and Elishai Ezra Tsur. 2021. “Neuromorphic Analog Implementation of Neural Engineering Framework-Inspired Spiking Neuron for High-Dimensional Representation.” Front. Neurosci. 15 (February): 627221. https://doi.org/10.3389/fnins.2021.627221.\nNeuromorphic computing, which emulates biological neural systems for efficient ML inference, can use analog circuits to implement the key components and behaviors of brains. For example, researchers have designed analog circuits to model neurons and synapses using capacitors, transistors, and operational amplifiers (Hazan and Ezra Tsur 2021). The capacitors can exhibit the spiking dynamics of biological neurons, while the amplifiers and transistors provide a weighted summation of inputs to mimic dendrites. Variable resistor technologies like memristors can realize analog synapses with spike-timing-dependent plasticity, which can strengthen or weaken connections based on spiking activity.\nStartups like SynSense have developed analog neuromorphic chips containing these biomimetic components (Bains 2020). This analog approach results in low power consumption and high scalability for edge devices versus complex digital SNN implementations.\n\nBains, Sunny. 2020. “The Business of Building Brains.” Nature Electronics 3 (7): 348–51. https://doi.org/10.1038/s41928-020-0449-1.\nHowever, training analog SNNs on chips remains an open challenge. Overall, analog realization is a promising technique for delivering the efficiency, scalability, and biological plausibility envisioned with neuromorphic computing. The physics of analog components combined with neural architecture design could improve inference efficiency over conventional digital neural networks.\n\n\n10.8.4 Flexible Electronics\nWhile much of the new hardware technology in the ML workspace has been focused on optimizing and making systems more efficient, there’s a parallel trajectory aiming to adapt hardware for specific applications (Gates 2009; Musk et al. 2019; Tang et al. 2023; Tang, He, and Liu 2022; Kwon and Dong 2022). One such avenue is the development of flexible electronics for AI use cases.\n\nGates, Byron D. 2009. “Flexible Electronics.” Science 323 (5921): 1566–67. https://doi.org/10.1126/science.1171230.\n\nTang, Xin, Hao Shen, Siyuan Zhao, Na Li, and Jia Liu. 2023. “Flexible Braincomputer Interfaces.” Nature Electronics 6 (2): 109–18. https://doi.org/10.1038/s41928-022-00913-9.\n\nTang, Xin, Yichun He, and Jia Liu. 2022. “Soft Bioelectronics for Cardiac Interfaces.” Biophysics Reviews 3 (1). https://doi.org/10.1063/5.0069516.\nFlexible electronics refer to electronic circuits and devices fabricated on flexible plastic or polymer substrates rather than rigid silicon. Unlike conventional rigid boards and chips, this allows the electronics to bend, twist, and conform to irregular shapes. Figure 10.8 shows an example of a flexible device prototype that wirelessly measures body temperature, which can be seamlessly integrated into clothing or skin patches. The flexibility and bendability of emerging electronic materials allow them to be integrated into thin, lightweight form factors that are well-suited for embedded AI and TinyML applications.\nFlexible AI hardware can conform to curvy surfaces and operate efficiently with microwatt power budgets. Flexibility also enables rollable or foldable form factors to minimize device footprint and weight, ideal for small, portable smart devices and wearables incorporating TinyML. Another key advantage of flexible electronics compared to conventional technologies is lower manufacturing costs and simpler fabrication processes, which could democratize access to these technologies. While silicon masks and fabrication costs typically cost millions of dollars, flexible hardware typically costs only tens of cents to manufacture (Huang et al. 2011; Biggs et al. 2021). The potential to fabricate flexible electronics directly onto plastic films using high-throughput printing and coating processes can reduce costs and improve manufacturability at scale versus rigid AI chips (Musk et al. 2019).\n\nHuang, Tsung-Ching, Kenjiro Fukuda, Chun-Ming Lo, Yung-Hui Yeh, Tsuyoshi Sekitani, Takao Someya, and Kwang-Ting Cheng. 2011. “Pseudo-CMOS: A Design Style for Low-Cost and Robust Flexible Electronics.” IEEE Trans. Electron Devices 58 (1): 141–50. https://doi.org/10.1109/ted.2010.2088127.\n\nBiggs, John, James Myers, Jedrzej Kufel, Emre Ozer, Simon Craske, Antony Sou, Catherine Ramsdale, Ken Williamson, Richard Price, and Scott White. 2021. “A Natively Flexible 32-Bit Arm Microprocessor.” Nature 595 (7868): 532–36. https://doi.org/10.1038/s41586-021-03625-w.\n\n\n\n\n\n\nFigure 10.8: Flexible device prototype. Source: Jabil Circuit.\n\n\n\nThe field is enabled by advances in organic semiconductors and nanomaterials that can be deposited on thin, flexible films. However, fabrication remains challenging compared to mature silicon processes. Flexible circuits currently typically exhibit lower performance than rigid equivalents. Still, they promise to transform electronics into lightweight, bendable materials.\nFlexible electronics use cases are well-suited for intimate integration with the human body. Potential medical AI applications include bio-integrated sensors, soft assistive robots, and implants that monitor or stimulate the nervous system intelligently. Specifically, flexible electrode arrays could enable higher-density, less-invasive neural interfaces compared to rigid equivalents.\nTherefore, flexible electronics are ushering in a new era of wearables and body sensors, largely due to innovations in organic transistors. These components allow for more lightweight and bendable electronics, ideal for wearables, electronic skin, and body-conforming medical devices.\nThey are well-suited for bioelectronic devices in terms of biocompatibility, opening avenues for applications in brain and cardiac interfaces. For example, research in flexible brain-computer interfaces and soft bioelectronics for cardiac applications demonstrates the potential for wide-ranging medical applications.\nCompanies and research institutions are not only developing and investing great amounts of resources in flexible electrodes, as showcased in Neuralink’s work (Musk et al. 2019). Still, they are also pushing the boundaries to integrate machine learning models within the systems (Kwon and Dong 2022). These smart sensors aim for a seamless, long-lasting symbiosis with the human body.\n\nMusk, Elon et al. 2019. “An Integrated Brain-Machine Interface Platform with Thousands of Channels.” J. Med. Internet Res. 21 (10): e16194. https://doi.org/10.2196/16194.\n\nKwon, Sun Hwa, and Lin Dong. 2022. “Flexible Sensors and Machine Learning for Heart Monitoring.” Nano Energy 102 (November): 107632. https://doi.org/10.1016/j.nanoen.2022.107632.\n\nSegura Anaya, L. H., Abeer Alsadoon, N. Costadopoulos, and P. W. C. Prasad. 2017. “Ethical Implications of User Perceptions of Wearable Devices.” Sci. Eng. Ethics 24 (1): 1–28. https://doi.org/10.1007/s11948-017-9872-8.\n\nGoodyear, Victoria A. 2017. “Social Media, Apps and Wearable Technologies: Navigating Ethical Dilemmas and Procedures.” Qualitative Research in Sport, Exercise and Health 9 (3): 285–302. https://doi.org/10.1080/2159676x.2017.1303790.\n\nFarah, Martha J. 2005. “Neuroethics: The Practical and the Philosophical.” Trends Cogn. Sci. 9 (1): 34–40. https://doi.org/10.1016/j.tics.2004.12.001.\n\nRoskies, Adina. 2002. “Neuroethics for the New Millenium.” Neuron 35 (1): 21–23. https://doi.org/10.1016/s0896-6273(02)00763-8.\nEthically, incorporating smart, machine-learning-driven sensors within the body raises important questions. Issues surrounding data privacy, informed consent, and the long-term societal implications of such technologies are the focus of ongoing work in neuroethics and bioethics (Segura Anaya et al. 2017; Goodyear 2017; Farah 2005; Roskies 2002). The field is progressing at a pace that necessitates parallel advancements in ethical frameworks to guide the responsible development and deployment of these technologies. While there are limitations and ethical hurdles to overcome, the prospects for flexible electronics are expansive and hold immense promise for future research and applications.\n\n\n10.8.5 Memory Technologies\nMemory technologies are critical to AI hardware, but conventional DDR DRAM and SRAM create bottlenecks. AI workloads require high bandwidth (>1 TB/s). Extreme scientific applications of AI require extremely low latency (<50 ns) to feed data to compute units (Duarte et al. 2022), high density (>128Gb) to store large model parameters and data sets, and excellent energy efficiency (<100 fJ/b) for embedded use (Verma et al. 2019). New memories are needed to meet these demands. Emerging options include several new technologies:\n\nDuarte, Javier, Nhan Tran, Ben Hawks, Christian Herwig, Jules Muhizi, Shvetank Prakash, and Vijay Janapa Reddi. 2022. “FastML Science Benchmarks: Accelerating Real-Time Scientific Edge Machine Learning.” ArXiv Preprint abs/2207.07958. https://arxiv.org/abs/2207.07958.\n\nVerma, Naveen, Hongyang Jia, Hossein Valavi, Yinqi Tang, Murat Ozatay, Lung-Yen Chen, Bonan Zhang, and Peter Deaville. 2019. “In-Memory Computing: Advances and Prospects.” IEEE Solid-State Circuits Mag. 11 (3): 43–55. https://doi.org/10.1109/mssc.2019.2922889.\n\nResistive RAM (ReRAM) can improve density with simple, passive arrays. However, challenges around variability remain (Chi et al. 2016).\nPhase change memory (PCM) exploits the unique properties of chalcogenide glass. Crystalline and amorphous phases have different resistances. Intel’s Optane DCPMM provides fast (100ns), high endurance PCM. However, challenges include limited write cycles and high reset current (Burr et al. 2016).\n3D stacking can also boost memory density and bandwidth by vertically integrating memory layers with TSV interconnects (Loh 2008). For example, HBM provides 1024-bit wide interfaces.\n\n\nBurr, Geoffrey W., Matthew J. BrightSky, Abu Sebastian, Huai-Yu Cheng, Jau-Yi Wu, Sangbum Kim, Norma E. Sosa, et al. 2016. “Recent Progress in Phase-Change?Pub _Newline ?Memory Technology.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems 6 (2): 146–62. https://doi.org/10.1109/jetcas.2016.2547718.\n\nLoh, Gabriel H. 2008. “3D-Stacked Memory Architectures for Multi-Core Processors.” ACM SIGARCH Computer Architecture News 36 (3): 453–64. https://doi.org/10.1145/1394608.1382159.\nNew memory technologies, with their innovative cell architectures and materials, are critical to unlocking the next level of AI hardware performance and efficiency. Realizing their benefits in commercial systems remains an ongoing challenge.\nIn-memory computing is gaining traction as a promising avenue for optimizing machine learning and high-performance computing workloads. At its core, the technology co-locates data storage and computation to improve energy efficiency and reduce latency Wong et al. (2012). Two key technologies under this umbrella are Resistive RAM (ReRAM) and Processing-In-Memory (PIM).\n\nWong, H.-S. Philip, Heng-Yuan Lee, Shimeng Yu, Yu-Sheng Chen, Yi Wu, Pang-Shiu Chen, Byoungil Lee, Frederick T. Chen, and Ming-Jinn Tsai. 2012. “MetalOxide RRAM.” Proc. IEEE 100 (6): 1951–70. https://doi.org/10.1109/jproc.2012.2190369.\n\nChi, Ping, Shuangchen Li, Cong Xu, Tao Zhang, Jishen Zhao, Yongpan Liu, Yu Wang, and Yuan Xie. 2016. “Prime: A Novel Processing-in-Memory Architecture for Neural Network Computation in ReRAM-Based Main Memory.” ACM SIGARCH Computer Architecture News 44 (3): 27–39. https://doi.org/10.1145/3007787.3001140.\nReRAM (Wong et al. 2012) and PIM (Chi et al. 2016) are the backbones for in-memory computing, storing and computing data in the same location. ReRAM focuses on issues of uniformity, endurance, retention, multi-bit operation, and scalability. On the other hand, PIM involves CPU units integrated directly into memory arrays, specialized for tasks like matrix multiplication, which are central in AI computations.\nThese technologies find applications in AI workloads and high-performance computing, where the synergy of storage and computation can lead to significant performance gains. The architecture is particularly useful for compute-intensive tasks common in machine learning models.\nWhile in-memory computing technologies like ReRAM and PIM offer exciting prospects for efficiency and performance, they come with their own challenges, such as data uniformity and scalability issues in ReRAM (Imani, Rahimi, and S. Rosing 2016). Nonetheless, the field is ripe for innovation, and addressing these limitations can open new frontiers in AI and high-performance computing.\n\nImani, Mohsen, Abbas Rahimi, and Tajana S. Rosing. 2016. “Resistive Configurable Associative Memory for Approximate Computing.” In Proceedings of the 2016 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 1327–32. IEEE; Research Publishing Services. https://doi.org/10.3850/9783981537079_0454.\n\n\n10.8.6 Optical Computing\nIn AI acceleration, a burgeoning area of interest lies in novel technologies that deviate from traditional paradigms. Some emerging technologies mentioned above, such as flexible electronics, in-memory computing, or even neuromorphic computing, are close to becoming a reality, given their ground-breaking innovations and applications. One of the promising and leading next-gen frontiers is optical computing technologies H. Zhou et al. (2022). Companies like [LightMatter] are pioneering the use of light photonics for calculations, thereby utilizing photons instead of electrons for data transmission and computation.\n\nZhou, Hailong, Jianji Dong, Junwei Cheng, Wenchan Dong, Chaoran Huang, Yichen Shen, Qiming Zhang, et al. 2022. “Photonic Matrix Multiplication Lights up Photonic Accelerator and Beyond.” Light: Science &Amp; Applications 11 (1): 30. https://doi.org/10.1038/s41377-022-00717-8.\n\nShastri, Bhavin J., Alexander N. Tait, T. Ferreira de Lima, Wolfram H. P. Pernice, Harish Bhaskaran, C. D. Wright, and Paul R. Prucnal. 2021. “Photonics for Artificial Intelligence and Neuromorphic Computing.” Nat. Photonics 15 (2): 102–14. https://doi.org/10.1038/s41566-020-00754-y.\nOptical computing utilizes photons and photonic devices rather than traditional electronic circuits for computing and data processing. It takes inspiration from fiber optic communication links that rely on light for fast, efficient data transfer (Shastri et al. 2021). Light can propagate with much less loss than semiconductors’ electrons, enabling inherent speed and efficiency benefits.\nSome specific advantages of optical computing include:\n\nHigh throughput: Photons can transmit with bandwidths >100 Tb/s using wavelength division multiplexing.\nLow latency: Photons interact on femtosecond timescales, millions faster than silicon transistors.\nParallelism: Multiple data signals can propagate simultaneously through the same optical medium.\nLow power: Photonic circuits utilizing waveguides and resonators can achieve complex logic and memory with only microwatts of power.\n\nHowever, optical computing currently faces significant challenges:\n\nLack of optical memory equivalent to electronic RAM\nRequires conversion between optical and electrical domains.\nLimited set of available optical components compared to rich electronics ecosystem.\nImmature integration methods to combine photonics with traditional CMOS chips.\nComplex programming models required to handle parallelism.\n\nAs a result, optical computing is still in the very early research stage despite its promising potential. However, technical breakthroughs could enable it to complement electronics and unlock performance gains for AI workloads. Companies like Lightmatter are pioneering early optical AI accelerators. In the long term, if key challenges are overcome, it could represent a revolutionary computing substrate.\n\n\n10.8.7 Quantum Computing\nQuantum computers leverage unique phenomena of quantum physics, like superposition and entanglement, to represent and process information in ways not possible classically. Instead of binary bits, the fundamental unit is the quantum bit or qubit. Unlike classical bits, which are limited to 0 or 1, qubits can exist simultaneously in a superposition of both states due to quantum effects.\nMultiple qubits can also be entangled, leading to exponential information density but introducing probabilistic results. Superposition enables parallel computation on all possible states, while entanglement allows nonlocal correlations between qubits.\nQuantum algorithms carefully manipulate these inherently quantum mechanical effects to solve problems like optimization or search more efficiently than their classical counterparts in theory.\n\nFaster training of deep neural networks by exploiting quantum parallelism for linear algebra operations.\nEfficient quantum ML algorithms make use of the unique capabilities of qubits.\nQuantum neural networks with inherent quantum effects baked into the model architecture.\nQuantum optimizers leveraging quantum annealing or adiabatic algorithms for combinatorial optimization problems.\n\nHowever, quantum states are fragile and prone to errors that require error-correcting protocols. The non-intuitive nature of quantum programming also introduces challenges not present in classical computing.\n\nNoisy and fragile quantum bits are difficult to scale up. The largest quantum computer today has less than 1000 qubits.\nRestricted set of available quantum gates and circuits relative to classical programming.\nLack of datasets and benchmarks to evaluate quantum ML in practical domains.\n\nWhile meaningful quantum advantage for ML remains far off, active research at companies like D-Wave, Rigetti, and IonQ is advancing quantum computer engineering and quantum algorithms. Major technology companies like Google, IBM, and Microsoft are actively exploring quantum computing. Google recently announced a 72-qubit quantum processor called Bristlecone and plans to build a 49-qubit commercial quantum system. Microsoft also has an active research program in topological quantum computing and collaborates with quantum startup IonQ\nQuantum techniques may first make inroads into optimization before more generalized ML adoption. Realizing quantum ML’s full potential awaits major milestones in quantum hardware development and ecosystem maturity.", "crumbs": [ "Training", "10  AI Acceleration" @@ -1203,7 +1203,7 @@ "href": "contents/benchmarking/benchmarking.html#data-benchmarking", "title": "11  Benchmarking AI", "section": "11.6 Data Benchmarking", - "text": "11.6 Data Benchmarking\nFor the past several years, AI has focused on developing increasingly sophisticated machine learning models like large language models. The goal has been to create models capable of human-level or superhuman performance on a wide range of tasks by training them on massive datasets. This model-centric approach produced rapid progress, with models attaining state-of-the-art results on many established benchmarks. Figure 11.6 shows the performance of AI systems relative to human performance (marked by the horizontal line at 0) across five applications: handwriting recognition, speech recognition, image recognition, reading comprehension, and language understanding. Over the past decade, the AI performance has surpassed that of humans.\nHowever, growing concerns about issues like bias, safety, and robustness persist even in models that achieve high accuracy on standard benchmarks. Additionally, some popular datasets used for evaluating models are beginning to saturate, with models reaching near-perfect performance on existing test splits (Kiela et al. 2021). As a simple example, there are test images in the classic MNIST handwritten digit dataset that may look indecipherable to most human evaluators but were assigned a label when the dataset was created - models that happen to agree with those labels may appear to exhibit superhuman performance but instead may only be capturing idiosyncrasies of the labeling and acquisition process from the dataset’s creation in 1994. In the same spirit, computer vision researchers now ask, “Are we done with ImageNet?” (Beyer et al. 2020). This highlights limitations in the conventional model-centric approach of optimizing accuracy on fixed datasets through architectural innovations.\n\nBeyer, Lucas, Olivier J Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord. 2020. “Are We Done with Imagenet?” ArXiv Preprint abs/2006.07159. https://arxiv.org/abs/2006.07159.\n\n\n\n\n\n\nFigure 11.6: AI vs human performane. Source: Kiela et al. (2021).\n\n\nKiela, Douwe, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, et al. 2021. “Dynabench: Rethinking Benchmarking in NLP.” In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4110–24. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.324.\n\n\nAn alternative paradigm is emerging called data-centric AI. Rather than treating data as static and focusing narrowly on model performance, this approach recognizes that models are only as good as their training data. So, the emphasis shifts to curating high-quality datasets that better reflect real-world complexity, developing more informative evaluation benchmarks, and carefully considering how data is sampled, preprocessed, and augmented. The goal is to optimize model behavior by improving the data rather than just optimizing metrics on flawed datasets. Data-centric AI critically examines and enhances the data itself to produce beneficial AI. This reflects an important evolution in mindset as the field addresses the shortcomings of narrow benchmarking.\nThis section will explore the key differences between model-centric and data-centric approaches to AI. This distinction has important implications for how we benchmark AI systems. Specifically, we will see how focusing on data quality and Efficiency can directly improve machine learning performance as an alternative to optimizing model architectures solely. The data-centric approach recognizes that models are only as good as their training data. So, enhancing data curation, evaluation benchmarks, and data handling processes can produce AI systems that are safer, fairer, and more robust. Rethinking benchmarking to prioritize data alongside models represents an important evolution as the field aims to deliver trustworthy real-world impact.\n\n11.6.1 Limitations of Model-Centric AI\nIn the model-centric AI era, a prominent characteristic was the development of complex model architectures. Researchers and practitioners dedicated substantial effort to devising sophisticated and intricate models in the quest for superior performance. This frequently involved the incorporation of additional layers and the fine-tuning of a multitude of hyperparameters to achieve incremental improvements in accuracy. Concurrently, there was a significant emphasis on leveraging advanced algorithms. These algorithms, often at the forefront of the latest research, were employed to improve the performance of AI models. The primary aim of these algorithms was to optimize the learning process of models, thereby extracting maximal information from the training data.\nWhile the model-centric approach has been central to many advancements in AI, it has several areas for improvement. First, the development of complex model architectures can often lead to overfitting. This is when the model performs well on the training data but needs to generalize to new, unseen data. The additional layers and complexity can capture noise in the training data as if it were a real pattern, harming the model’s performance on new data.\nSecond, relying on advanced algorithms can sometimes obscure the real understanding of a model’s functioning. These algorithms often act as a black box, making it difficult to interpret how the model is making decisions. This lack of transparency can be a significant hurdle, especially in critical applications such as healthcare and finance, where understanding the model’s decision-making process is crucial.\nThird, the emphasis on achieving state-of-the-art results on benchmark datasets can sometimes be misleading. These datasets need to represent the complexities and variability of real-world data more fully. A model that performs well on a benchmark dataset may not necessarily generalize well to new, unseen data in a real-world application. This discrepancy can lead to false confidence in the model’s capabilities and hinder its practical applicability.\nLastly, the model-centric approach often relies on large labeled datasets for training. However, obtaining such datasets takes time and effort in many real-world scenarios. This reliance on large datasets also limits AI’s applicability in domains where data is scarce or expensive to label.\nAs a result of the above reasons, and many more, the AI community is shifting to a more data-centric approach. Rather than focusing just on model architecture, researchers are now prioritizing curating high-quality datasets, developing better evaluation benchmarks, and considering how data is sampled and preprocessed. The key idea is that models are only as good as their training data. So, focusing on getting the right data will allow us to develop AI systems that are more fair, safe, and aligned with human values. This data-centric shift represents an important change in mindset as AI progresses.\n\n\n11.6.2 The Shift Toward Data-centric AI\nData-centric AI is a paradigm that emphasizes the importance of high-quality, well-labeled, and diverse datasets in developing AI models. In contrast to the model-centric approach, which focuses on refining and iterating on the model architecture and algorithm to improve performance, data-centric AI prioritizes the quality of the input data as the primary driver of improved model performance. High-quality data is clean, well-labeled and representative of the real-world scenarios the model will encounter. In contrast, low-quality data can lead to poor model performance, regardless of the complexity or sophistication of the model architecture.\nData-centric AI puts a strong emphasis on the cleaning and labeling of data. Cleaning involves the removal of outliers, handling missing values, and addressing other data inconsistencies. Labeling, on the other hand, involves assigning meaningful and accurate labels to the data. Both these processes are crucial in ensuring that the AI model is trained on accurate and relevant data. Another important aspect of the data-centric approach is data augmentation. This involves artificially increasing the size and diversity of the dataset by applying various transformations to the data, such as rotation, scaling, and flipping training images. Data augmentation helps in improving the model’s robustness and generalization capabilities.\nThere are several benefits to adopting a data-centric approach to AI development. First and foremost, it leads to improved model performance and generalization capabilities. By ensuring that the model is trained on high-quality, diverse data, the model can better generalize to new, unseen data (Mattson et al. 2020b).\nAdditionally, a data-centric approach can often lead to simpler models that are easier to interpret and maintain. This is because the emphasis is on the data rather than the model architecture, meaning simpler models can achieve high performance when trained on high-quality data.\nThe shift towards data-centric AI represents a significant paradigm shift. By prioritizing the quality of the input data, this approach aims to improve model performance and generalization capabilities, ultimately leading to more robust and reliable AI systems. As we continue to advance in our understanding and application of AI, the data-centric approach is likely to play an important role in shaping the future of this field.\n\n\n11.6.3 Benchmarking Data\nData benchmarking aims to evaluate common issues in datasets, such as identifying label errors, noisy features, representation imbalance (for example, out of the 1000 classes in Imagenet-1K, there are over 100 categories which are just types of dogs), class imbalance (where some classes have many more samples than others), whether models trained on a given dataset can generalize to out-of-distribution features, or what types of biases might exist in a given dataset (Mattson et al. 2020b). In its simplest form, data benchmarking aims to improve accuracy on a test set by removing noisy or mislabeled training samples while keeping the model architecture fixed. Recent competitions in data benchmarking have invited participants to submit novel augmentation strategies and active learning techniques.\n\nMattson, Peter, Vijay Janapa Reddi, Christine Cheng, Cody Coleman, Greg Diamos, David Kanter, Paulius Micikevicius, et al. 2020b. “MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance.” IEEE Micro 40 (2): 8–16. https://doi.org/10.1109/mm.2020.2974843.\nData-centric techniques continue to gain attention in benchmarking, especially as foundation models are increasingly trained on self-supervised objectives. Compared to smaller datasets like Imagenet-1K, massive datasets commonly used in self-supervised learning, such as Common Crawl, OpenImages, and LAION-5B, contain higher amounts of noise, duplicates, bias, and potentially offensive data.\nDataComp is a recently launched dataset competition that targets the evaluation of large corpora. DataComp focuses on language-image pairs used to train CLIP models. The introductory whitepaper finds that when the total compute budget for training is constant, the best-performing CLIP models on downstream tasks, such as ImageNet classification, are trained on just 30% of the available training sample pool. This suggests that proper filtering of large corpora is critical to improving the accuracy of foundation models. Similarly, Demystifying CLIP Data (Xu et al. 2023) asks whether the success of CLIP is attributable to the architecture or the dataset.\n\nXu, Hu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, and Christoph Feichtenhofer. 2023. “Demystifying CLIP Data.” ArXiv Preprint abs/2309.16671. https://arxiv.org/abs/2309.16671.\nDataPerf is another recent effort focusing on benchmarking data in various modalities. DataPerf provides rounds of online competition to spur improvement in datasets. The inaugural offering launched with challenges in vision, speech, acquisition, debugging, and text prompting for image generation.\n\n\n11.6.4 Data Efficiency\nAs machine learning models grow larger and more complex and compute resources become more scarce in the face of rising demand, it becomes challenging to meet the computation requirements even with the largest machine learning fleets. To overcome these challenges and ensure machine learning system scalability, it is necessary to explore novel opportunities that increase conventional approaches to resource scaling.\nImproving data quality can be a useful method to impact machine learning system performance significantly. One of the primary benefits of enhancing data quality is the potential to reduce the size of the training dataset while still maintaining or even improving model performance. This data size reduction directly relates to the amount of training time required, thereby allowing models to converge more quickly and efficiently. Achieving this balance between data quality and dataset size is a challenging task that requires the development of sophisticated methods, algorithms, and techniques.\nSeveral approaches can be taken to improve data quality. These methods include and are not limited to the following:\n\nData Cleaning: This involves handling missing values, correcting errors, and removing outliers. Clean data ensures that the model is not learning from noise or inaccuracies.\nData Interpretability and Explainability: Common techniques include LIME (Ribeiro, Singh, and Guestrin 2016), which provides insight into the decision boundaries of classifiers, and Shapley values (Lundberg and Lee 2017), which estimate the importance of individual samples in contributing to a model’s predictions.\nFeature Engineering: Transforming or creating new features can significantly improve model performance by providing more relevant information for learning.\nData Augmentation: Augmenting data by creating new samples through various transformations can help improve model robustness and generalization.\nActive Learning: This is a semi-supervised learning approach where the model actively queries a human oracle to label the most informative samples (Coleman et al. 2022). This ensures that the model is trained on the most relevant data.\nDimensionality Reduction: Techniques like PCA can reduce the number of features in a dataset, thereby reducing complexity and training time.\n\n\nRibeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. 2016. “” Why Should i Trust You?” Explaining the Predictions of Any Classifier.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–44.\n\nLundberg, Scott M., and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, edited by Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, 4765–74. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.\n\nColeman, Cody, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter Bailis, Alexander C. Berg, Robert D. Nowak, Roshan Sumbaly, Matei Zaharia, and I. Zeki Yalniz. 2022. “Similarity Search for Efficient Active Learning and Search of Rare Concepts.” In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, the Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, 6402–10. AAAI Press. https://ojs.aaai.org/index.php/AAAI/article/view/20591.\nThere are many other methods in the wild. But the goal is the same. Refining the dataset and ensuring it is of the highest quality can reduce the training time required for models to converge. However, achieving this requires developing and implementing sophisticated methods, algorithms, and techniques that can clean, preprocess, and augment data while retaining the most informative samples. This is an ongoing challenge that will require continued research and innovation in the field of machine learning.", + "text": "11.6 Data Benchmarking\nFor the past several years, AI has focused on developing increasingly sophisticated machine learning models like large language models. The goal has been to create models capable of human-level or superhuman performance on a wide range of tasks by training them on massive datasets. This model-centric approach produced rapid progress, with models attaining state-of-the-art results on many established benchmarks. Figure 11.6 shows the performance of AI systems relative to human performance (marked by the horizontal line at 0) across five applications: handwriting recognition, speech recognition, image recognition, reading comprehension, and language understanding. Over the past decade, the AI performance has surpassed that of humans.\nHowever, growing concerns about issues like bias, safety, and robustness persist even in models that achieve high accuracy on standard benchmarks. Additionally, some popular datasets used for evaluating models are beginning to saturate, with models reaching near-perfect performance on existing test splits (Kiela et al. 2021). As a simple example, there are test images in the classic MNIST handwritten digit dataset that may look indecipherable to most human evaluators but were assigned a label when the dataset was created - models that happen to agree with those labels may appear to exhibit superhuman performance but instead may only be capturing idiosyncrasies of the labeling and acquisition process from the dataset’s creation in 1994. In the same spirit, computer vision researchers now ask, “Are we done with ImageNet?” (Beyer et al. 2020). This highlights limitations in the conventional model-centric approach of optimizing accuracy on fixed datasets through architectural innovations.\n\nBeyer, Lucas, Olivier J Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord. 2020. “Are We Done with Imagenet?” ArXiv Preprint abs/2006.07159. https://arxiv.org/abs/2006.07159.\n\n\n\n\n\n\nFigure 11.6: AI vs human performane. Source: Kiela et al. (2021).\n\n\nKiela, Douwe, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, et al. 2021. “Dynabench: Rethinking Benchmarking in NLP.” In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4110–24. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.naacl-main.324.\n\n\nAn alternative paradigm is emerging called data-centric AI. Rather than treating data as static and focusing narrowly on model performance, this approach recognizes that models are only as good as their training data. So, the emphasis shifts to curating high-quality datasets that better reflect real-world complexity, developing more informative evaluation benchmarks, and carefully considering how data is sampled, preprocessed, and augmented. The goal is to optimize model behavior by improving the data rather than just optimizing metrics on flawed datasets. Data-centric AI critically examines and enhances the data itself to produce beneficial AI. This reflects an important evolution in mindset as the field addresses the shortcomings of narrow benchmarking.\nThis section will explore the key differences between model-centric and data-centric approaches to AI. This distinction has important implications for how we benchmark AI systems. Specifically, we will see how focusing on data quality and Efficiency can directly improve machine learning performance as an alternative to optimizing model architectures solely. The data-centric approach recognizes that models are only as good as their training data. So, enhancing data curation, evaluation benchmarks, and data handling processes can produce AI systems that are safer, fairer, and more robust. Rethinking benchmarking to prioritize data alongside models represents an important evolution as the field strives to deliver trustworthy real-world impact.\n\n11.6.1 Limitations of Model-Centric AI\nIn the model-centric AI era, a prominent characteristic was the development of complex model architectures. Researchers and practitioners dedicated substantial effort to devising sophisticated and intricate models in the quest for superior performance. This frequently involved the incorporation of additional layers and the fine-tuning of a multitude of hyperparameters to achieve incremental improvements in accuracy. Concurrently, there was a significant emphasis on leveraging advanced algorithms. These algorithms, often at the forefront of the latest research, were employed to improve the performance of AI models. The primary aim of these algorithms was to optimize the learning process of models, thereby extracting maximal information from the training data.\nWhile the model-centric approach has been central to many advancements in AI, it has several areas for improvement. First, the development of complex model architectures can often lead to overfitting. This is when the model performs well on the training data but needs to generalize to new, unseen data. The additional layers and complexity can capture noise in the training data as if it were a real pattern, harming the model’s performance on new data.\nSecond, relying on advanced algorithms can sometimes obscure the real understanding of a model’s functioning. These algorithms often act as a black box, making it difficult to interpret how the model is making decisions. This lack of transparency can be a significant hurdle, especially in critical applications such as healthcare and finance, where understanding the model’s decision-making process is crucial.\nThird, the emphasis on achieving state-of-the-art results on benchmark datasets can sometimes be misleading. These datasets need to represent the complexities and variability of real-world data more fully. A model that performs well on a benchmark dataset may not necessarily generalize well to new, unseen data in a real-world application. This discrepancy can lead to false confidence in the model’s capabilities and hinder its practical applicability.\nLastly, the model-centric approach often relies on large labeled datasets for training. However, obtaining such datasets takes time and effort in many real-world scenarios. This reliance on large datasets also limits AI’s applicability in domains where data is scarce or expensive to label.\nAs a result of the above reasons, and many more, the AI community is shifting to a more data-centric approach. Rather than focusing just on model architecture, researchers are now prioritizing curating high-quality datasets, developing better evaluation benchmarks, and considering how data is sampled and preprocessed. The key idea is that models are only as good as their training data. So, focusing on getting the right data will allow us to develop AI systems that are more fair, safe, and aligned with human values. This data-centric shift represents an important change in mindset as AI progresses.\n\n\n11.6.2 The Shift Toward Data-centric AI\nData-centric AI is a paradigm that emphasizes the importance of high-quality, well-labeled, and diverse datasets in developing AI models. In contrast to the model-centric approach, which focuses on refining and iterating on the model architecture and algorithm to improve performance, data-centric AI prioritizes the quality of the input data as the primary driver of improved model performance. High-quality data is clean, well-labeled and representative of the real-world scenarios the model will encounter. In contrast, low-quality data can lead to poor model performance, regardless of the complexity or sophistication of the model architecture.\nData-centric AI puts a strong emphasis on the cleaning and labeling of data. Cleaning involves the removal of outliers, handling missing values, and addressing other data inconsistencies. Labeling, on the other hand, involves assigning meaningful and accurate labels to the data. Both these processes are crucial in ensuring that the AI model is trained on accurate and relevant data. Another important aspect of the data-centric approach is data augmentation. This involves artificially increasing the size and diversity of the dataset by applying various transformations to the data, such as rotation, scaling, and flipping training images. Data augmentation helps in improving the model’s robustness and generalization capabilities.\nThere are several benefits to adopting a data-centric approach to AI development. First and foremost, it leads to improved model performance and generalization capabilities. By ensuring that the model is trained on high-quality, diverse data, the model can better generalize to new, unseen data (Mattson et al. 2020b).\nAdditionally, a data-centric approach can often lead to simpler models that are easier to interpret and maintain. This is because the emphasis is on the data rather than the model architecture, meaning simpler models can achieve high performance when trained on high-quality data.\nThe shift towards data-centric AI represents a significant paradigm shift. By prioritizing the quality of the input data, this approach tries to model performance and generalization capabilities, ultimately leading to more robust and reliable AI systems. As we continue to advance in our understanding and application of AI, the data-centric approach is likely to play an important role in shaping the future of this field.\n\n\n11.6.3 Benchmarking Data\nData benchmarking focuses on evaluating common issues in datasets, such as identifying label errors, noisy features, representation imbalance (for example, out of the 1000 classes in Imagenet-1K, there are over 100 categories which are just types of dogs), class imbalance (where some classes have many more samples than others), whether models trained on a given dataset can generalize to out-of-distribution features, or what types of biases might exist in a given dataset (Mattson et al. 2020b). In its simplest form, data benchmarking seeks to improve accuracy on a test set by removing noisy or mislabeled training samples while keeping the model architecture fixed. Recent competitions in data benchmarking have invited participants to submit novel augmentation strategies and active learning techniques.\n\nMattson, Peter, Vijay Janapa Reddi, Christine Cheng, Cody Coleman, Greg Diamos, David Kanter, Paulius Micikevicius, et al. 2020b. “MLPerf: An Industry Standard Benchmark Suite for Machine Learning Performance.” IEEE Micro 40 (2): 8–16. https://doi.org/10.1109/mm.2020.2974843.\nData-centric techniques continue to gain attention in benchmarking, especially as foundation models are increasingly trained on self-supervised objectives. Compared to smaller datasets like Imagenet-1K, massive datasets commonly used in self-supervised learning, such as Common Crawl, OpenImages, and LAION-5B, contain higher amounts of noise, duplicates, bias, and potentially offensive data.\nDataComp is a recently launched dataset competition that targets the evaluation of large corpora. DataComp focuses on language-image pairs used to train CLIP models. The introductory whitepaper finds that when the total compute budget for training is constant, the best-performing CLIP models on downstream tasks, such as ImageNet classification, are trained on just 30% of the available training sample pool. This suggests that proper filtering of large corpora is critical to improving the accuracy of foundation models. Similarly, Demystifying CLIP Data (Xu et al. 2023) asks whether the success of CLIP is attributable to the architecture or the dataset.\n\nXu, Hu, Saining Xie, Xiaoqing Ellen Tan, Po-Yao Huang, Russell Howes, Vasu Sharma, Shang-Wen Li, Gargi Ghosh, Luke Zettlemoyer, and Christoph Feichtenhofer. 2023. “Demystifying CLIP Data.” ArXiv Preprint abs/2309.16671. https://arxiv.org/abs/2309.16671.\nDataPerf is another recent effort focusing on benchmarking data in various modalities. DataPerf provides rounds of online competition to spur improvement in datasets. The inaugural offering launched with challenges in vision, speech, acquisition, debugging, and text prompting for image generation.\n\n\n11.6.4 Data Efficiency\nAs machine learning models grow larger and more complex and compute resources become more scarce in the face of rising demand, it becomes challenging to meet the computation requirements even with the largest machine learning fleets. To overcome these challenges and ensure machine learning system scalability, it is necessary to explore novel opportunities that increase conventional approaches to resource scaling.\nImproving data quality can be a useful method to impact machine learning system performance significantly. One of the primary benefits of enhancing data quality is the potential to reduce the size of the training dataset while still maintaining or even improving model performance. This data size reduction directly relates to the amount of training time required, thereby allowing models to converge more quickly and efficiently. Achieving this balance between data quality and dataset size is a challenging task that requires the development of sophisticated methods, algorithms, and techniques.\nSeveral approaches can be taken to improve data quality. These methods include and are not limited to the following:\n\nData Cleaning: This involves handling missing values, correcting errors, and removing outliers. Clean data ensures that the model is not learning from noise or inaccuracies.\nData Interpretability and Explainability: Common techniques include LIME (Ribeiro, Singh, and Guestrin 2016), which provides insight into the decision boundaries of classifiers, and Shapley values (Lundberg and Lee 2017), which estimate the importance of individual samples in contributing to a model’s predictions.\nFeature Engineering: Transforming or creating new features can significantly improve model performance by providing more relevant information for learning.\nData Augmentation: Augmenting data by creating new samples through various transformations can help improve model robustness and generalization.\nActive Learning: This is a semi-supervised learning approach where the model actively queries a human oracle to label the most informative samples (Coleman et al. 2022). This ensures that the model is trained on the most relevant data.\nDimensionality Reduction: Techniques like PCA can reduce the number of features in a dataset, thereby reducing complexity and training time.\n\n\nRibeiro, Marco Tulio, Sameer Singh, and Carlos Guestrin. 2016. “” Why Should i Trust You?” Explaining the Predictions of Any Classifier.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1135–44.\n\nLundberg, Scott M., and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, edited by Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, 4765–74. https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html.\n\nColeman, Cody, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter Bailis, Alexander C. Berg, Robert D. Nowak, Roshan Sumbaly, Matei Zaharia, and I. Zeki Yalniz. 2022. “Similarity Search for Efficient Active Learning and Search of Rare Concepts.” In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, the Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, 6402–10. AAAI Press. https://ojs.aaai.org/index.php/AAAI/article/view/20591.\nThere are many other methods in the wild. But the goal is the same. Refining the dataset and ensuring it is of the highest quality can reduce the training time required for models to converge. However, achieving this requires developing and implementing sophisticated methods, algorithms, and techniques that can clean, preprocess, and augment data while retaining the most informative samples. This is an ongoing challenge that will require continued research and innovation in the field of machine learning.", "crumbs": [ "Deployment", "11  Benchmarking AI" @@ -1291,7 +1291,7 @@ "href": "contents/ondevice_learning/ondevice_learning.html#on-device-adaptation", "title": "12  On-Device Learning", "section": "12.3 On-device Adaptation", - "text": "12.3 On-device Adaptation\nIn an ML task, resource consumption mainly comes from three sources:\n\nThe ML model itself;\nThe optimization process during model learning\nStoring and processing the dataset used for learning.\n\nCorrespondingly, there are three approaches to adapting existing ML algorithms onto resource-constrained devices:\n\nReducing the complexity of the ML model\nModifying optimizations to reduce training resource requirements\nCreating new storage-efficient data representations\n\nIn the following section, we will review these on-device learning adaptation methods. The Model Optimizations chapter provides more details on model optimizations.\n\n12.3.1 Reducing Model Complexity\nIn this section, we will briefly discuss ways to reduce model complexity when adapting ML models on-device. For details on reducing model complexity, please refer to the Model Optimization Chapter.\n\nTraditional ML Algorithms\nDue to edge devices’ computing and memory limitations, select traditional ML algorithms are great candidates for on-device learning applications due to their lightweight nature. Some example algorithms with low resource footprints include Naive Bayes Classifiers, Support Vector Machines (SVMs), Linear Regression, Logistic Regression, and select Decision Tree algorithms.\nWith some refinements, these classical ML algorithms can be adapted to specific hardware architectures and perform simple tasks. Their low-performance requirements make it easy to integrate continuous learning even on edge devices.\n\n\nPruning\nPruning is a technique for reducing the size and complexity of an ML model to improve its efficiency and generalization performance. This is beneficial for training models on edge devices, where we want to minimize resource usage while maintaining competitive accuracy.\nThe primary goal of pruning is to remove parts of the model that do not contribute significantly to its predictive power while retaining the most informative aspects. In the context of decision trees, pruning involves removing some branches (subtrees) from the tree, leading to a smaller and simpler tree. In the context of DNN, pruning is used to reduce the number of neurons (units) or connections in the network, as shown in Figure 12.2.\n\n\n\n\n\n\nFigure 12.2: Network pruning.\n\n\n\n\n\nReducing Complexity of Deep Learning Models\nTraditional cloud-based DNN frameworks have too much memory overhead to be used on-device. For example, deep learning systems like PyTorch and TensorFlow require hundreds of megabytes of memory overhead when training models such as MobilenetV2, and the overhead scales as the number of training parameters increases.\nCurrent research for lightweight DNNs mostly explores CNN architectures. Several bare-metal frameworks designed for running Neural Networks on MCUs by keeping computational overhead and memory footprint low also exist. Some examples include MNN, TVM, and TensorFlow Lite. However, they can only perform inference during forward passes and lack support for backpropagation. While these models are designed for edge deployment, their reduction in model weights and architectural connections led to reduced resource requirements for continuous learning.\nThe tradeoff between performance and model support is clear when adapting the most popular DNN systems. How do we adapt existing DNN models to resource-constrained settings while maintaining support for backpropagation and continuous learning? The latest research suggests algorithm and system codesign techniques that help reduce the resource consumption of ML training on edge devices. Utilizing techniques such as quantization-aware scaling (QAS), sparse updates, and other cutting-edge techniques, on-device learning is possible on embedded systems with a few hundred kilobytes of RAM without additional memory while maintaining high accuracy.\n\n\n\n12.3.2 Modifying Optimization Processes\nChoosing the right optimization strategy is important for DNN training on a device since this allows for finding a good local minimum. Since training occurs on a device, this strategy must also consider limited memory and power.\n\nQuantization-Aware Scaling\nQuantization is a common method for reducing the memory footprint of DNN training. Although this could introduce new errors, these errors can be mitigated by designing a model to characterize this statistical error. For example, models could use stochastic rounding or introduce the quantization error into the gradient updates.\nA specific algorithmic technique is Quantization-Aware Scaling (QAS), which improves the performance of neural networks on low-precision hardware, such as edge devices, mobile devices, or TinyML systems, by adjusting the scale factors during the quantization process.\nAs we discussed in the Model Optimizations chapter, quantization is the process of mapping a continuous range of values to a discrete set of values. In the context of neural networks, quantization often involves reducing the precision of the weights and activations from 32-bit floating point to lower-precision formats such as 8-bit integers. This reduction in precision can significantly reduce the computational cost and memory footprint of the model, making it suitable for deployment on low-precision hardware. Figure 12.3 is an example of float-to-integer quantization.\n\n\n\n\n\n\nFigure 12.3: Float to integer quantization. Source: Nvidia.\n\n\n\nHowever, the quantization process can also introduce quantization errors that can degrade the model’s performance. Quantization-aware scaling is a technique that aims to minimize these errors by adjusting the scale factors used in the quantization process.\nThe QAS process involves two main steps:\n\nQuantization-aware training: In this step, the neural network is trained with quantization in mind, simulating it to mimic its effects during forward and backward passes. This allows the model to learn to compensate for the quantization errors and improve its performance on low-precision hardware. Refer to the QAT section in Model Optimizations for details.\nQuantization and scaling: After training, the model is quantized to a low-precision format, and the scale factors are adjusted to minimize the quantization errors. The scale factors are chosen based on the distribution of the weights and activations in the model and are adjusted to ensure that the quantized values are within the range of the low-precision format.\n\nQAS is used to overcome the difficulties of optimizing models on tiny devices without needing hyperparameter tuning; QAS automatically scales tensor gradients with various bit precisions. This stabilizes the training process and matches the accuracy of floating-point precision.\n\n\nSparse Updates\nAlthough QAS enables the optimization of a quantized model, it uses a large amount of memory, which is unrealistic for on-device training. So, spare updates are used to reduce the memory footprint of full backward computation. Instead of pruning weights for inference, sparse update prunes the gradient during backward propagation to update the model sparsely. In other words, sparse update skips computing gradients of less important layers and sub-tensors.\nHowever, determining the optimal sparse update scheme given a constraining memory budget can be challenging due to the large search space. For example, the MCUNet model has 43 convolutional layers and a search space of approximately 1030. One technique to address this issue is contribution analysis. Contribution analysis measures the accuracy improvement from biases (updating the last few biases compared to only updating the classifier) and weights (updating the weight of one extra layer compared to only having a bias update). By trying to maximize these improvements, contribution analysis automatically derives an optimal sparse update scheme for enabling on-device training.\n\n\nLayer-Wise Training\nOther methods besides quantization can help optimize routines. One such method is layer-wise training. A significant memory consumer of DNN training is end-to-end backpropagation, which requires all intermediate feature maps to be stored so the model can calculate gradients. An alternative to this approach that reduces the memory footprint of DNN training is sequential layer-by-layer training (T. Chen et al. 2016). Instead of training end-to-end, training a single layer at a time helps avoid having to store intermediate feature maps.\n\nChen, Tianqi, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016. “Training Deep Nets with Sublinear Memory Cost.” ArXiv Preprint abs/1604.06174. https://arxiv.org/abs/1604.06174.\n\n\nTrading Computation for Memory\nThe strategy of trading computation for memory involves releasing some of the memory being used to store intermediate results. Instead, these results can be recomputed as needed. Reducing memory in exchange for more computation is shown to reduce the memory footprint of DNN training to fit into almost any budget while also minimizing computational cost (Gruslys et al. 2016).\n\nGruslys, Audrunas, Rémi Munos, Ivo Danihelka, Marc Lanctot, and Alex Graves. 2016. “Memory-Efficient Backpropagation Through Time.” In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, edited by Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett, 4125–33. https://proceedings.neurips.cc/paper/2016/hash/a501bebf79d570651ff601788ea9d16d-Abstract.html.\n\n\n\n12.3.3 Developing New Data Representations\nThe dimensionality and volume of the training data can significantly impact on-device adaptation. So, another technique for adapting models onto resource-constrained devices is to represent datasets more efficiently.\n\nData Compression\nThe goal of data compression is to reach high accuracies while limiting the amount of training data. One method to achieve this is prioritizing sample complexity: the amount of training data required for the algorithm to reach a target accuracy (Dhar et al. 2021).\n\nDhar, Sauptik, Junyao Guo, Jiayi (Jason) Liu, Samarth Tripathi, Unmesh Kurup, and Mohak Shah. 2021. “A Survey of on-Device Machine Learning: An Algorithms and Learning Theory Perspective.” ACM Transactions on Internet of Things 2 (3): 1–49. https://doi.org/10.1145/3450494.\n\nDarvish Rouhani, Bita, Azalia Mirhoseini, and Farinaz Koushanfar. 2017. “TinyDL: Just-in-time Deep Learning Solution for Constrained Embedded Systems.” In 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 1–4. IEEE. https://doi.org/10.1109/iscas.2017.8050343.\n\nLi, Xiang, Tao Qin, Jian Yang, and Tie-Yan Liu. 2016. “LightRNN: Memory and Computation-Efficient Recurrent Neural Networks.” In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, edited by Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett, 4385–93. https://proceedings.neurips.cc/paper/2016/hash/c3e4035af2a1cde9f21e1ae1951ac80b-Abstract.html.\nOther more common methods of data compression focus on reducing the dimensionality and the volume of the training data. For example, an approach could take advantage of matrix sparsity to reduce the memory footprint of storing training data. Training data can be transformed into a lower-dimensional embedding and factorized into a dictionary matrix multiplied by a block-sparse coefficient matrix (Darvish Rouhani, Mirhoseini, and Koushanfar 2017). Another example could involve representing words from a large language training dataset in a more compressed vector format (Li et al. 2016).", + "text": "12.3 On-device Adaptation\nIn an ML task, resource consumption mainly comes from three sources:\n\nThe ML model itself;\nThe optimization process during model learning\nStoring and processing the dataset used for learning.\n\nCorrespondingly, there are three approaches to adapting existing ML algorithms onto resource-constrained devices:\n\nReducing the complexity of the ML model\nModifying optimizations to reduce training resource requirements\nCreating new storage-efficient data representations\n\nIn the following section, we will review these on-device learning adaptation methods. The Model Optimizations chapter provides more details on model optimizations.\n\n12.3.1 Reducing Model Complexity\nIn this section, we will briefly discuss ways to reduce model complexity when adapting ML models on-device. For details on reducing model complexity, please refer to the Model Optimization Chapter.\n\nTraditional ML Algorithms\nDue to edge devices’ computing and memory limitations, select traditional ML algorithms are great candidates for on-device learning applications due to their lightweight nature. Some example algorithms with low resource footprints include Naive Bayes Classifiers, Support Vector Machines (SVMs), Linear Regression, Logistic Regression, and select Decision Tree algorithms.\nWith some refinements, these classical ML algorithms can be adapted to specific hardware architectures and perform simple tasks. Their low-performance requirements make it easy to integrate continuous learning even on edge devices.\n\n\nPruning\nPruning is a technique for reducing the size and complexity of an ML model to improve its efficiency and generalization performance. This is beneficial for training models on edge devices, where we want to minimize resource usage while maintaining competitive accuracy.\nThe primary goal of pruning is to remove parts of the model that do not contribute significantly to its predictive power while retaining the most informative aspects. In the context of decision trees, pruning involves removing some branches (subtrees) from the tree, leading to a smaller and simpler tree. In the context of DNN, pruning is used to reduce the number of neurons (units) or connections in the network, as shown in Figure 12.2.\n\n\n\n\n\n\nFigure 12.2: Network pruning.\n\n\n\n\n\nReducing Complexity of Deep Learning Models\nTraditional cloud-based DNN frameworks have too much memory overhead to be used on-device. For example, deep learning systems like PyTorch and TensorFlow require hundreds of megabytes of memory overhead when training models such as MobilenetV2, and the overhead scales as the number of training parameters increases.\nCurrent research for lightweight DNNs mostly explores CNN architectures. Several bare-metal frameworks designed for running Neural Networks on MCUs by keeping computational overhead and memory footprint low also exist. Some examples include MNN, TVM, and TensorFlow Lite. However, they can only perform inference during forward passes and lack support for backpropagation. While these models are designed for edge deployment, their reduction in model weights and architectural connections led to reduced resource requirements for continuous learning.\nThe tradeoff between performance and model support is clear when adapting the most popular DNN systems. How do we adapt existing DNN models to resource-constrained settings while maintaining support for backpropagation and continuous learning? The latest research suggests algorithm and system codesign techniques that help reduce the resource consumption of ML training on edge devices. Utilizing techniques such as quantization-aware scaling (QAS), sparse updates, and other cutting-edge techniques, on-device learning is possible on embedded systems with a few hundred kilobytes of RAM without additional memory while maintaining high accuracy.\n\n\n\n12.3.2 Modifying Optimization Processes\nChoosing the right optimization strategy is important for DNN training on a device since this allows for finding a good local minimum. Since training occurs on a device, this strategy must also consider limited memory and power.\n\nQuantization-Aware Scaling\nQuantization is a common method for reducing the memory footprint of DNN training. Although this could introduce new errors, these errors can be mitigated by designing a model to characterize this statistical error. For example, models could use stochastic rounding or introduce the quantization error into the gradient updates.\nA specific algorithmic technique is Quantization-Aware Scaling (QAS), which improves the performance of neural networks on low-precision hardware, such as edge devices, mobile devices, or TinyML systems, by adjusting the scale factors during the quantization process.\nAs we discussed in the Model Optimizations chapter, quantization is the process of mapping a continuous range of values to a discrete set of values. In the context of neural networks, quantization often involves reducing the precision of the weights and activations from 32-bit floating point to lower-precision formats such as 8-bit integers. This reduction in precision can significantly reduce the computational cost and memory footprint of the model, making it suitable for deployment on low-precision hardware. Figure 12.3 is an example of float-to-integer quantization.\n\n\n\n\n\n\nFigure 12.3: Float to integer quantization. Source: Nvidia.\n\n\n\nHowever, the quantization process can also introduce quantization errors that can degrade the model’s performance. Quantization-aware scaling is a technique that minimizes these errors by adjusting the scale factors used in the quantization process.\nThe QAS process involves two main steps:\n\nQuantization-aware training: In this step, the neural network is trained with quantization in mind, simulating it to mimic its effects during forward and backward passes. This allows the model to learn to compensate for the quantization errors and improve its performance on low-precision hardware. Refer to the QAT section in Model Optimizations for details.\nQuantization and scaling: After training, the model is quantized to a low-precision format, and the scale factors are adjusted to minimize the quantization errors. The scale factors are chosen based on the distribution of the weights and activations in the model and are adjusted to ensure that the quantized values are within the range of the low-precision format.\n\nQAS is used to overcome the difficulties of optimizing models on tiny devices without needing hyperparameter tuning; QAS automatically scales tensor gradients with various bit precisions. This stabilizes the training process and matches the accuracy of floating-point precision.\n\n\nSparse Updates\nAlthough QAS enables the optimization of a quantized model, it uses a large amount of memory, which is unrealistic for on-device training. So, spare updates are used to reduce the memory footprint of full backward computation. Instead of pruning weights for inference, sparse update prunes the gradient during backward propagation to update the model sparsely. In other words, sparse update skips computing gradients of less important layers and sub-tensors.\nHowever, determining the optimal sparse update scheme given a constraining memory budget can be challenging due to the large search space. For example, the MCUNet model has 43 convolutional layers and a search space of approximately 1030. One technique to address this issue is contribution analysis. Contribution analysis measures the accuracy improvement from biases (updating the last few biases compared to only updating the classifier) and weights (updating the weight of one extra layer compared to only having a bias update). By trying to maximize these improvements, contribution analysis automatically derives an optimal sparse update scheme for enabling on-device training.\n\n\nLayer-Wise Training\nOther methods besides quantization can help optimize routines. One such method is layer-wise training. A significant memory consumer of DNN training is end-to-end backpropagation, which requires all intermediate feature maps to be stored so the model can calculate gradients. An alternative to this approach that reduces the memory footprint of DNN training is sequential layer-by-layer training (T. Chen et al. 2016). Instead of training end-to-end, training a single layer at a time helps avoid having to store intermediate feature maps.\n\nChen, Tianqi, Bing Xu, Chiyuan Zhang, and Carlos Guestrin. 2016. “Training Deep Nets with Sublinear Memory Cost.” ArXiv Preprint abs/1604.06174. https://arxiv.org/abs/1604.06174.\n\n\nTrading Computation for Memory\nThe strategy of trading computation for memory involves releasing some of the memory being used to store intermediate results. Instead, these results can be recomputed as needed. Reducing memory in exchange for more computation is shown to reduce the memory footprint of DNN training to fit into almost any budget while also minimizing computational cost (Gruslys et al. 2016).\n\nGruslys, Audrunas, Rémi Munos, Ivo Danihelka, Marc Lanctot, and Alex Graves. 2016. “Memory-Efficient Backpropagation Through Time.” In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, edited by Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett, 4125–33. https://proceedings.neurips.cc/paper/2016/hash/a501bebf79d570651ff601788ea9d16d-Abstract.html.\n\n\n\n12.3.3 Developing New Data Representations\nThe dimensionality and volume of the training data can significantly impact on-device adaptation. So, another technique for adapting models onto resource-constrained devices is to represent datasets more efficiently.\n\nData Compression\nThe goal of data compression is to reach high accuracies while limiting the amount of training data. One method to achieve this is prioritizing sample complexity: the amount of training data required for the algorithm to reach a target accuracy (Dhar et al. 2021).\n\nDhar, Sauptik, Junyao Guo, Jiayi (Jason) Liu, Samarth Tripathi, Unmesh Kurup, and Mohak Shah. 2021. “A Survey of on-Device Machine Learning: An Algorithms and Learning Theory Perspective.” ACM Transactions on Internet of Things 2 (3): 1–49. https://doi.org/10.1145/3450494.\n\nDarvish Rouhani, Bita, Azalia Mirhoseini, and Farinaz Koushanfar. 2017. “TinyDL: Just-in-time Deep Learning Solution for Constrained Embedded Systems.” In 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 1–4. IEEE. https://doi.org/10.1109/iscas.2017.8050343.\n\nLi, Xiang, Tao Qin, Jian Yang, and Tie-Yan Liu. 2016. “LightRNN: Memory and Computation-Efficient Recurrent Neural Networks.” In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, edited by Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett, 4385–93. https://proceedings.neurips.cc/paper/2016/hash/c3e4035af2a1cde9f21e1ae1951ac80b-Abstract.html.\nOther more common methods of data compression focus on reducing the dimensionality and the volume of the training data. For example, an approach could take advantage of matrix sparsity to reduce the memory footprint of storing training data. Training data can be transformed into a lower-dimensional embedding and factorized into a dictionary matrix multiplied by a block-sparse coefficient matrix (Darvish Rouhani, Mirhoseini, and Koushanfar 2017). Another example could involve representing words from a large language training dataset in a more compressed vector format (Li et al. 2016).", "crumbs": [ "Deployment", "12  On-Device Learning" @@ -1324,7 +1324,7 @@ "href": "contents/ondevice_learning/ondevice_learning.html#security-concerns", "title": "12  On-Device Learning", "section": "12.6 Security Concerns", - "text": "12.6 Security Concerns\nPerforming ML model training and adaptation on end-user devices also introduces security risks that must be addressed. Some key security concerns include:\n\nExposure of private data: Training data may be leaked or stolen from devices\nData poisoning: Adversaries can manipulate training data to degrade model performance\nModel extraction: Attackers may attempt to steal trained model parameters\nMembership inference: Models may reveal the participation of specific users’ data\nEvasion attacks: Specially crafted inputs can cause misclassification\n\nAny system that performs learning on-device introduces security concerns, as it may expose vulnerabilities in larger-scale models. Numerous security risks are associated with any ML model, but these risks have specific consequences for on-device learning. Fortunately, there are methods to mitigate these risks and improve the real-world performance of on-device learning.\n\n12.6.1 Data Poisoning\nOn-device ML introduces unique data security challenges compared to traditional cloud-based training. In particular, data poisoning attacks pose a serious threat during on-device learning. Adversaries can manipulate training data to degrade model performance when deployed.\nSeveral data poisoning attack techniques exist:\n\nLabel Flipping: It involves applying incorrect labels to samples. For instance, in image classification, cat photos may be labeled as dogs to confuse the model. Flipping even 10% of labels can have significant consequences on the model.\nData Insertion: It introduces fake or distorted inputs into the training set. This could include pixelated images, noisy audio, or garbled text.\nLogic Corruption: This alters the underlying [patterns] (https://www.worldscientific.com/doi/10.1142/S0218001414600027) in data to mislead the model. In sentiment analysis, highly negative reviews may be marked positive through this technique. For this reason, recent surveys have shown that many companies are more afraid of data poisoning than other adversarial ML concerns.\n\nWhat makes data poisoning alarming is how it exploits the discrepancy between curated datasets and live training data. Consider a cat photo dataset collected from the internet. In the weeks later, when this data trains a model on-device, new cat photos on the web differ significantly.\nWith data poisoning, attackers purchase domains and upload content that influences a portion of the training data. Even small data changes significantly impact the model’s learned behavior. Consequently, poisoning can instill racist, sexist, or other harmful biases if unchecked.\nMicrosoft Tay was a chatbot launched by Microsoft in 2016. It was designed to learn from its interactions with users on social media platforms like Twitter. Unfortunately, Microsoft Tay became a prime example of data poisoning in ML models. Within 24 hours of its launch, Microsoft had to take Tay offline because it had started producing offensive and inappropriate messages, including hate speech and racist comments. This occurred because some users on social media intentionally fed Tay with harmful and offensive input, which the chatbot then learned from and incorporated into its responses.\nThis incident is a clear example of data poisoning because malicious actors intentionally manipulated the data used to train the chatbot and shape its responses. The data poisoning resulted in the chatbot adopting harmful biases and producing output that its developers did not intend. It demonstrates how even small amounts of maliciously crafted data can significantly impact the behavior of ML models and highlights the importance of implementing robust data filtering and validation mechanisms to prevent such incidents from occurring.\nSuch biases could have dangerous real-world impacts. Rigorous data validation, anomaly detection, and tracking of data provenance are critical defensive measures. Adopting frameworks like Five Safes ensures models are trained on high-quality, representative data (Desai et al. 2016).\n\nDesai, Tanvi, Felix Ritchie, Richard Welpton, et al. 2016. “Five Safes: Designing Data Access for Research.” Economics Working Paper Series 1601: 28.\nData poisoning is a pressing concern for secure on-device learning since data at the endpoint cannot be easily monitored in real-time. If models are allowed to adapt on their own, then we run the risk of the device acting maliciously. However, continued research in adversarial ML aims to develop robust solutions to detect and mitigate such data attacks.\n\n\n12.6.2 Adversarial Attacks\nDuring the training phase, attackers might inject malicious data into the training dataset, which can subtly alter the model’s behavior. For example, an attacker could add images of cats labeled as dogs to a dataset used to train an image classification model. If done cleverly, the model’s accuracy might not significantly drop, and the attack could be noticed. The model would then incorrectly classify some cats as dogs, which could have consequences depending on the application.\nIn an embedded security camera system, for instance, this could allow an intruder to avoid detection by wearing a specific pattern that the model has been tricked into classifying as non-threatening.\nDuring the inference phase, attackers can use adversarial examples to fool the model. Adversarial examples are inputs that have been slightly altered in a way that causes the model to make incorrect predictions. For instance, an attacker might add a small amount of noise to an image in a way that causes a face recognition system to misidentify a person. These attacks can be particularly concerning in applications where safety is at stake, such as autonomous vehicles. A real-world example of this is when researchers were able to cause a traffic sign recognition system to misclassify a stop sign as a speed limit sign. This type of misclassification could lead to accidents if it occurred in a real-world autonomous driving system.\nTo mitigate these risks, several defenses can be employed:\n\nData Validation and Sanitization: Before incorporating new data into the training dataset, it should be thoroughly validated and sanitized to ensure it is not malicious.\nAdversarial Training: The model can be trained on adversarial examples to make it more robust to these types of attacks.\nInput Validation: During inference, inputs should be validated to ensure they have not been manipulated to create adversarial examples.\nRegular Auditing and Monitoring: Regularly auditing and monitoring the model’s behavior can help detect and mitigate adversarial attacks. However, this is easier said than done in the context of tiny ML systems. It is often hard to monitor embedded ML systems at the endpoint due to communication bandwidth limitations, which we will discuss in the MLOps chapter.\n\nBy understanding the potential risks and implementing these defenses, we can help secure on-device training at the endpoint/edge and mitigate the impact of adversarial attacks. Most people easily confuse data poisoning and adversarial attacks. So Table 12.2 compares data poisoning and adversarial attacks:\n\n\n\nTable 12.2: Comparison of data poisoning and adversarial attacks.\n\n\n\n\n\n\n\n\n\n\nAspect\nData Poisoning\nAdversarial Attacks\n\n\n\n\nTiming\nTraining phase\nInference phase\n\n\nTarget\nTraining data\nInput data\n\n\nGoal\nNegatively affect model’s performance\nCause incorrect predictions\n\n\nMethod\nInsert malicious examples into training data, often with incorrect labels\nAdd carefully crafted noise to input data\n\n\nExample\nAdding images of cats labeled as dogs to a dataset used for training an image classification model\nAdding a small amount of noise to an image in a way that causes a face recognition system to misidentify a person\n\n\nPotential Effects\nModel learns incorrect patterns and makes incorrect predictions\nImmediate and potentially dangerous incorrect predictions\n\n\nApplications Affected\nAny ML model\nAutonomous vehicles, security systems, etc.\n\n\n\n\n\n\n\n\n12.6.3 Model Inversion\nModel inversion attacks are a privacy threat to on-device machine learning models trained on sensitive user data (Nguyen et al. 2023). Understanding this attack vector and mitigation strategies will be important for building secure and ethical on-device AI. For example, imagine an iPhone app that uses on-device learning to categorize photos in your camera roll into groups like “beach,” “food,” or “selfies” for easier searching.\n\nNguyen, Ngoc-Bao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, and Ngai-Man Cheung. 2023. “Re-Thinking Model Inversion Attacks Against Deep Neural Networks.” In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16384–93. IEEE. https://doi.org/10.1109/cvpr52729.2023.01572.\nThe on-device model may be trained by Apple on a dataset of iCloud photos from consenting users. A malicious attacker could attempt to extract parts of those original iCloud training photos using model inversion. Specifically, the attacker feeds crafted synthetic inputs into the on-device photo classifier. By tweaking the synthetic inputs and observing how the model categorizes them, they can refine the inputs until they reconstruct copies of the original training data - like a beach photo from a user’s iCloud. Now, the attacker has breached that user’s privacy by obtaining one of their photos without consent. This demonstrates why model inversion is dangerous - it can potentially leak highly sensitive training data.\nPhotos are an especially high-risk data type because they often contain identifiable people, location information, and private moments. However, the same attack methodology could apply to other personal data, such as audio recordings, text messages, or users’ health data.\nTo defend against model inversion, one would need to take precautions like adding noise to the model outputs or using privacy-preserving machine learning techniques like federated learning to train the on-device model. The goal is to prevent attackers from being able to reconstruct the original training data.\n\n\n12.6.4 On-Device Learning Security Concerns\nWhile data poisoning and adversarial attacks are common concerns for ML models in general, on-device learning introduces unique security risks. When on-device variants of large-scale models are published, adversaries can exploit these smaller models to attack their larger counterparts. Research has demonstrated that as on-device models and full-scale models become more similar, the vulnerability of the original large-scale models increases significantly. For instance, evaluations across 19 Deep Neural Networks (DNNs) revealed that exploiting on-device models could increase the vulnerability of the original large-scale models by up to 100 times.\nThere are three primary types of security risks specific to on-device learning:\n\nTransfer-Based Attacks: These attacks exploit the transferability property between a surrogate model (an approximation of the target model, similar to an on-device model) and a remote target model (the original full-scale model). Attackers generate adversarial examples using the surrogate model, which can then be used to deceive the target model. For example, imagine an on-device model designed to identify spam emails. An attacker could use this model to generate a spam email that is not detected by the larger, full-scale filtering system.\nOptimization-Based Attacks: These attacks generate adversarial examples for transfer-based attacks using some form of the objective function and iteratively modify inputs to achieve the desired outcome. Gradient estimation attacks, for example, approximate the model’s gradient using query outputs (such as softmax confidence scores), while gradient-free attacks use the model’s final decision (the predicted class) to approximate the gradient, albeit requiring many more queries.\nQuery Attacks with Transfer Priors: These attacks combine elements of transfer-based and optimization-based attacks. They reverse engineer on-device models to serve as surrogates for the target full-scale model. In other words, attackers use the smaller on-device model to understand how the larger model works and then use this knowledge to attack the full-scale model.\n\nBy understanding these specific risks associated with on-device learning, we can develop more robust security protocols to protect both on-device and full-scale models from potential attacks.\n\n\n12.6.5 Mitigation of On-Device Learning Risks\nVarious methods can be employed to mitigate the numerous security risks associated with on-device learning. These methods may be specific to the type of attack or serve as a general tool to bolster security.\nOne strategy to reduce security risks is to diminish the similarity between on-device models and full-scale models, thereby reducing transferability by up to 90%. This method, known as similarity-unpairing, addresses the problem that arises when adversaries exploit the input-gradient similarity between the two models. By finetuning the full-scale model to create a new version with similar accuracy but different input gradients, we can construct the on-device model by quantizing this updated full-scale model. This unpairing reduces the vulnerability of on-device models by limiting the exposure of the original full-scale model. Importantly, the order of finetuning and quantization can be varied while still achieving risk mitigation (Hong, Carlini, and Kurakin 2023).\nTo tackle data poisoning, it is imperative to source datasets from trusted and reliable vendors.\nSeveral strategies can be employed to combat adversarial attacks. A proactive approach involves generating adversarial examples and incorporating them into the model’s training dataset, thereby fortifying the model against such attacks. Tools like CleverHans, an open-source training library, are instrumental in creating adversarial examples. Defense distillation is another effective strategy, wherein the on-device model outputs probabilities of different classifications rather than definitive decisions (Hong, Carlini, and Kurakin 2023), making it more challenging for adversarial examples to exploit the model.\n\nHong, Sanghyun, Nicholas Carlini, and Alexey Kurakin. 2023. “Publishing Efficient on-Device Models Increases Adversarial Vulnerability.” In 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 271–90. IEEE; IEEE. https://doi.org/10.1109/satml54575.2023.00026.\nThe theft of intellectual property is another significant concern when deploying on-device models. Intellectual property theft is a concern when deploying on-device models, as adversaries may attempt to reverse-engineer the model to steal the underlying technology. To safeguard against intellectual property theft, the binary executable of the trained model should be stored on a microcontroller unit with encrypted software and secured physical interfaces of the chip. Furthermore, the final dataset used for training the model should be kept private.\nFurthermore, on-device models often use well-known or open-source datasets, such as MobileNet’s Visual Wake Words. As such, it is important to maintain the privacy of the final dataset used for training the model. Additionally, protecting the data augmentation process and incorporating specific use cases can minimize the risk of reverse-engineering an on-device model.\nLastly, the Adversarial Threat Landscape for Artificial Intelligence Systems (ATLAS) serves as a valuable matrix tool that helps assess the risk profile of on-device models, empowering developers to identify and mitigate potential risks proactively.\n\n\n12.6.6 Securing Training Data\nThere are various ways to secure on-device training data. Each concept is really deep and could be worth a class by itself. So here, we’ll briefly allude to those concepts so you’re aware of what to learn further.\n\nEncryption\nEncryption serves as the first line of defense for training data. This involves implementing end-to-end encryption for local storage on devices and communication channels to prevent unauthorized access to raw training data. Trusted execution environments, such as Intel SGX and ARM TrustZone, are essential for facilitating secure training on encrypted data.\nAdditionally, when aggregating updates from multiple devices, secure multi-party computation protocols can be employed to improve security (Kairouz, Oh, and Viswanath 2015); a practical application of this is in collaborative on-device learning, where cryptographic privacy-preserving aggregation of user model updates can be implemented. This technique effectively hides individual user data even during the aggregation phase.\n\nKairouz, Peter, Sewoong Oh, and Pramod Viswanath. 2015. “Secure Multi-Party Differential Privacy.” In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, edited by Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, 2008–16. https://proceedings.neurips.cc/paper/2015/hash/a01610228fe998f515a72dd730294d87-Abstract.html.\n\n\nDifferential Privacy\nDifferential privacy is another crucial strategy for protecting training data. By injecting calibrated statistical noise into the data, we can mask individual records while still extracting valuable population patterns (Dwork and Roth 2013). Managing the privacy budget across multiple training iterations and reducing noise as the model converges is also vital (Abadi et al. 2016). Methods such as formally provable differential privacy, which may include adding Laplace or Gaussian noise scaled to the dataset’s sensitivity, can be employed.\n\nDwork, Cynthia, and Aaron Roth. 2013. “The Algorithmic Foundations of Differential Privacy.” Foundations and Trends in Theoretical Computer Science 9 (3-4): 211–407. https://doi.org/10.1561/0400000042.\n\nAbadi, Martin, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. “Deep Learning with Differential Privacy.” In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–18. CCS ’16. New York, NY, USA: ACM. https://doi.org/10.1145/2976749.2978318.\n\n\nAnomaly Detection\nAnomaly detection plays an important role in identifying and mitigating potential data poisoning attacks. This can be achieved through statistical analyses like Principal Component Analysis (PCA) and clustering, which help to detect deviations in aggregated training data. Time-series methods such as Cumulative Sum (CUSUM) charts are useful for identifying shifts indicative of potential poisoning. Comparing current data distributions with previously seen clean data distributions can also help to flag anomalies. Moreover, suspected poisoned batches should be removed from the training update aggregation process. For example, spot checks on subsets of training images on devices can be conducted using photoDNA hashes to identify poisoned inputs.\n\n\nInput Data Validation\nLastly, input data validation is essential for ensuring the integrity and validity of input data before it is fed into the training model, thereby protecting against adversarial payloads. Similarity measures, such as cosine distance, can be employed to catch inputs that deviate significantly from the expected distribution. Suspicious inputs that may contain adversarial payloads should be quarantined and sanitized. Furthermore, parser access to training data should be restricted to validated code paths only. Leveraging hardware security features, such as ARM Pointer Authentication, can prevent memory corruption (ARM Limited, 2023). An example of this is implementing input integrity checks on audio training data used by smart speakers before processing by the speech recognition model (Z. Chen and Xu 2023).\n\nChen, Zhiyong, and Shugong Xu. 2023. “Learning Domain-Heterogeneous Speaker Recognition Systems with Personalized Continual Federated Learning.” EURASIP Journal on Audio, Speech, and Music Processing 2023 (1): 33. https://doi.org/10.1186/s13636-023-00299-2.", + "text": "12.6 Security Concerns\nPerforming ML model training and adaptation on end-user devices also introduces security risks that must be addressed. Some key security concerns include:\n\nExposure of private data: Training data may be leaked or stolen from devices\nData poisoning: Adversaries can manipulate training data to degrade model performance\nModel extraction: Attackers may attempt to steal trained model parameters\nMembership inference: Models may reveal the participation of specific users’ data\nEvasion attacks: Specially crafted inputs can cause misclassification\n\nAny system that performs learning on-device introduces security concerns, as it may expose vulnerabilities in larger-scale models. Numerous security risks are associated with any ML model, but these risks have specific consequences for on-device learning. Fortunately, there are methods to mitigate these risks and improve the real-world performance of on-device learning.\n\n12.6.1 Data Poisoning\nOn-device ML introduces unique data security challenges compared to traditional cloud-based training. In particular, data poisoning attacks pose a serious threat during on-device learning. Adversaries can manipulate training data to degrade model performance when deployed.\nSeveral data poisoning attack techniques exist:\n\nLabel Flipping: It involves applying incorrect labels to samples. For instance, in image classification, cat photos may be labeled as dogs to confuse the model. Flipping even 10% of labels can have significant consequences on the model.\nData Insertion: It introduces fake or distorted inputs into the training set. This could include pixelated images, noisy audio, or garbled text.\nLogic Corruption: This alters the underlying [patterns] (https://www.worldscientific.com/doi/10.1142/S0218001414600027) in data to mislead the model. In sentiment analysis, highly negative reviews may be marked positive through this technique. For this reason, recent surveys have shown that many companies are more afraid of data poisoning than other adversarial ML concerns.\n\nWhat makes data poisoning alarming is how it exploits the discrepancy between curated datasets and live training data. Consider a cat photo dataset collected from the internet. In the weeks later, when this data trains a model on-device, new cat photos on the web differ significantly.\nWith data poisoning, attackers purchase domains and upload content that influences a portion of the training data. Even small data changes significantly impact the model’s learned behavior. Consequently, poisoning can instill racist, sexist, or other harmful biases if unchecked.\nMicrosoft Tay was a chatbot launched by Microsoft in 2016. It was designed to learn from its interactions with users on social media platforms like Twitter. Unfortunately, Microsoft Tay became a prime example of data poisoning in ML models. Within 24 hours of its launch, Microsoft had to take Tay offline because it had started producing offensive and inappropriate messages, including hate speech and racist comments. This occurred because some users on social media intentionally fed Tay with harmful and offensive input, which the chatbot then learned from and incorporated into its responses.\nThis incident is a clear example of data poisoning because malicious actors intentionally manipulated the data used to train the chatbot and shape its responses. The data poisoning resulted in the chatbot adopting harmful biases and producing output that its developers did not intend. It demonstrates how even small amounts of maliciously crafted data can significantly impact the behavior of ML models and highlights the importance of implementing robust data filtering and validation mechanisms to prevent such incidents from occurring.\nSuch biases could have dangerous real-world impacts. Rigorous data validation, anomaly detection, and tracking of data provenance are critical defensive measures. Adopting frameworks like Five Safes ensures models are trained on high-quality, representative data (Desai et al. 2016).\n\nDesai, Tanvi, Felix Ritchie, Richard Welpton, et al. 2016. “Five Safes: Designing Data Access for Research.” Economics Working Paper Series 1601: 28.\nData poisoning is a pressing concern for secure on-device learning since data at the endpoint cannot be easily monitored in real-time. If models are allowed to adapt on their own, then we run the risk of the device acting maliciously. However, continued research in adversarial ML is needed to develop robust solutions to detect and mitigate such data attacks.\n\n\n12.6.2 Adversarial Attacks\nDuring the training phase, attackers might inject malicious data into the training dataset, which can subtly alter the model’s behavior. For example, an attacker could add images of cats labeled as dogs to a dataset used to train an image classification model. If done cleverly, the model’s accuracy might not significantly drop, and the attack could be noticed. The model would then incorrectly classify some cats as dogs, which could have consequences depending on the application.\nIn an embedded security camera system, for instance, this could allow an intruder to avoid detection by wearing a specific pattern that the model has been tricked into classifying as non-threatening.\nDuring the inference phase, attackers can use adversarial examples to fool the model. Adversarial examples are inputs that have been slightly altered in a way that causes the model to make incorrect predictions. For instance, an attacker might add a small amount of noise to an image in a way that causes a face recognition system to misidentify a person. These attacks can be particularly concerning in applications where safety is at stake, such as autonomous vehicles. A real-world example of this is when researchers were able to cause a traffic sign recognition system to misclassify a stop sign as a speed limit sign. This type of misclassification could lead to accidents if it occurred in a real-world autonomous driving system.\nTo mitigate these risks, several defenses can be employed:\n\nData Validation and Sanitization: Before incorporating new data into the training dataset, it should be thoroughly validated and sanitized to ensure it is not malicious.\nAdversarial Training: The model can be trained on adversarial examples to make it more robust to these types of attacks.\nInput Validation: During inference, inputs should be validated to ensure they have not been manipulated to create adversarial examples.\nRegular Auditing and Monitoring: Regularly auditing and monitoring the model’s behavior can help detect and mitigate adversarial attacks. However, this is easier said than done in the context of tiny ML systems. It is often hard to monitor embedded ML systems at the endpoint due to communication bandwidth limitations, which we will discuss in the MLOps chapter.\n\nBy understanding the potential risks and implementing these defenses, we can help secure on-device training at the endpoint/edge and mitigate the impact of adversarial attacks. Most people easily confuse data poisoning and adversarial attacks. So Table 12.2 compares data poisoning and adversarial attacks:\n\n\n\nTable 12.2: Comparison of data poisoning and adversarial attacks.\n\n\n\n\n\n\n\n\n\n\nAspect\nData Poisoning\nAdversarial Attacks\n\n\n\n\nTiming\nTraining phase\nInference phase\n\n\nTarget\nTraining data\nInput data\n\n\nGoal\nNegatively affect model’s performance\nCause incorrect predictions\n\n\nMethod\nInsert malicious examples into training data, often with incorrect labels\nAdd carefully crafted noise to input data\n\n\nExample\nAdding images of cats labeled as dogs to a dataset used for training an image classification model\nAdding a small amount of noise to an image in a way that causes a face recognition system to misidentify a person\n\n\nPotential Effects\nModel learns incorrect patterns and makes incorrect predictions\nImmediate and potentially dangerous incorrect predictions\n\n\nApplications Affected\nAny ML model\nAutonomous vehicles, security systems, etc.\n\n\n\n\n\n\n\n\n12.6.3 Model Inversion\nModel inversion attacks are a privacy threat to on-device machine learning models trained on sensitive user data (Nguyen et al. 2023). Understanding this attack vector and mitigation strategies will be important for building secure and ethical on-device AI. For example, imagine an iPhone app that uses on-device learning to categorize photos in your camera roll into groups like “beach,” “food,” or “selfies” for easier searching.\n\nNguyen, Ngoc-Bao, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, and Ngai-Man Cheung. 2023. “Re-Thinking Model Inversion Attacks Against Deep Neural Networks.” In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 16384–93. IEEE. https://doi.org/10.1109/cvpr52729.2023.01572.\nThe on-device model may be trained by Apple on a dataset of iCloud photos from consenting users. A malicious attacker could attempt to extract parts of those original iCloud training photos using model inversion. Specifically, the attacker feeds crafted synthetic inputs into the on-device photo classifier. By tweaking the synthetic inputs and observing how the model categorizes them, they can refine the inputs until they reconstruct copies of the original training data - like a beach photo from a user’s iCloud. Now, the attacker has breached that user’s privacy by obtaining one of their photos without consent. This demonstrates why model inversion is dangerous - it can potentially leak highly sensitive training data.\nPhotos are an especially high-risk data type because they often contain identifiable people, location information, and private moments. However, the same attack methodology could apply to other personal data, such as audio recordings, text messages, or users’ health data.\nTo defend against model inversion, one would need to take precautions like adding noise to the model outputs or using privacy-preserving machine learning techniques like federated learning to train the on-device model. The goal is to prevent attackers from being able to reconstruct the original training data.\n\n\n12.6.4 On-Device Learning Security Concerns\nWhile data poisoning and adversarial attacks are common concerns for ML models in general, on-device learning introduces unique security risks. When on-device variants of large-scale models are published, adversaries can exploit these smaller models to attack their larger counterparts. Research has demonstrated that as on-device models and full-scale models become more similar, the vulnerability of the original large-scale models increases significantly. For instance, evaluations across 19 Deep Neural Networks (DNNs) revealed that exploiting on-device models could increase the vulnerability of the original large-scale models by up to 100 times.\nThere are three primary types of security risks specific to on-device learning:\n\nTransfer-Based Attacks: These attacks exploit the transferability property between a surrogate model (an approximation of the target model, similar to an on-device model) and a remote target model (the original full-scale model). Attackers generate adversarial examples using the surrogate model, which can then be used to deceive the target model. For example, imagine an on-device model designed to identify spam emails. An attacker could use this model to generate a spam email that is not detected by the larger, full-scale filtering system.\nOptimization-Based Attacks: These attacks generate adversarial examples for transfer-based attacks using some form of the objective function and iteratively modify inputs to achieve the desired outcome. Gradient estimation attacks, for example, approximate the model’s gradient using query outputs (such as softmax confidence scores), while gradient-free attacks use the model’s final decision (the predicted class) to approximate the gradient, albeit requiring many more queries.\nQuery Attacks with Transfer Priors: These attacks combine elements of transfer-based and optimization-based attacks. They reverse engineer on-device models to serve as surrogates for the target full-scale model. In other words, attackers use the smaller on-device model to understand how the larger model works and then use this knowledge to attack the full-scale model.\n\nBy understanding these specific risks associated with on-device learning, we can develop more robust security protocols to protect both on-device and full-scale models from potential attacks.\n\n\n12.6.5 Mitigation of On-Device Learning Risks\nVarious methods can be employed to mitigate the numerous security risks associated with on-device learning. These methods may be specific to the type of attack or serve as a general tool to bolster security.\nOne strategy to reduce security risks is to diminish the similarity between on-device models and full-scale models, thereby reducing transferability by up to 90%. This method, known as similarity-unpairing, addresses the problem that arises when adversaries exploit the input-gradient similarity between the two models. By finetuning the full-scale model to create a new version with similar accuracy but different input gradients, we can construct the on-device model by quantizing this updated full-scale model. This unpairing reduces the vulnerability of on-device models by limiting the exposure of the original full-scale model. Importantly, the order of finetuning and quantization can be varied while still achieving risk mitigation (Hong, Carlini, and Kurakin 2023).\nTo tackle data poisoning, it is imperative to source datasets from trusted and reliable vendors.\nSeveral strategies can be employed to combat adversarial attacks. A proactive approach involves generating adversarial examples and incorporating them into the model’s training dataset, thereby fortifying the model against such attacks. Tools like CleverHans, an open-source training library, are instrumental in creating adversarial examples. Defense distillation is another effective strategy, wherein the on-device model outputs probabilities of different classifications rather than definitive decisions (Hong, Carlini, and Kurakin 2023), making it more challenging for adversarial examples to exploit the model.\n\nHong, Sanghyun, Nicholas Carlini, and Alexey Kurakin. 2023. “Publishing Efficient on-Device Models Increases Adversarial Vulnerability.” In 2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML), 271–90. IEEE; IEEE. https://doi.org/10.1109/satml54575.2023.00026.\nThe theft of intellectual property is another significant concern when deploying on-device models. Intellectual property theft is a concern when deploying on-device models, as adversaries may attempt to reverse-engineer the model to steal the underlying technology. To safeguard against intellectual property theft, the binary executable of the trained model should be stored on a microcontroller unit with encrypted software and secured physical interfaces of the chip. Furthermore, the final dataset used for training the model should be kept private.\nFurthermore, on-device models often use well-known or open-source datasets, such as MobileNet’s Visual Wake Words. As such, it is important to maintain the privacy of the final dataset used for training the model. Additionally, protecting the data augmentation process and incorporating specific use cases can minimize the risk of reverse-engineering an on-device model.\nLastly, the Adversarial Threat Landscape for Artificial Intelligence Systems (ATLAS) serves as a valuable matrix tool that helps assess the risk profile of on-device models, empowering developers to identify and mitigate potential risks proactively.\n\n\n12.6.6 Securing Training Data\nThere are various ways to secure on-device training data. Each concept is really deep and could be worth a class by itself. So here, we’ll briefly allude to those concepts so you’re aware of what to learn further.\n\nEncryption\nEncryption serves as the first line of defense for training data. This involves implementing end-to-end encryption for local storage on devices and communication channels to prevent unauthorized access to raw training data. Trusted execution environments, such as Intel SGX and ARM TrustZone, are essential for facilitating secure training on encrypted data.\nAdditionally, when aggregating updates from multiple devices, secure multi-party computation protocols can be employed to improve security (Kairouz, Oh, and Viswanath 2015); a practical application of this is in collaborative on-device learning, where cryptographic privacy-preserving aggregation of user model updates can be implemented. This technique effectively hides individual user data even during the aggregation phase.\n\nKairouz, Peter, Sewoong Oh, and Pramod Viswanath. 2015. “Secure Multi-Party Differential Privacy.” In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, edited by Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett, 2008–16. https://proceedings.neurips.cc/paper/2015/hash/a01610228fe998f515a72dd730294d87-Abstract.html.\n\n\nDifferential Privacy\nDifferential privacy is another crucial strategy for protecting training data. By injecting calibrated statistical noise into the data, we can mask individual records while still extracting valuable population patterns (Dwork and Roth 2013). Managing the privacy budget across multiple training iterations and reducing noise as the model converges is also vital (Abadi et al. 2016). Methods such as formally provable differential privacy, which may include adding Laplace or Gaussian noise scaled to the dataset’s sensitivity, can be employed.\n\nDwork, Cynthia, and Aaron Roth. 2013. “The Algorithmic Foundations of Differential Privacy.” Foundations and Trends in Theoretical Computer Science 9 (3-4): 211–407. https://doi.org/10.1561/0400000042.\n\nAbadi, Martin, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. “Deep Learning with Differential Privacy.” In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–18. CCS ’16. New York, NY, USA: ACM. https://doi.org/10.1145/2976749.2978318.\n\n\nAnomaly Detection\nAnomaly detection plays an important role in identifying and mitigating potential data poisoning attacks. This can be achieved through statistical analyses like Principal Component Analysis (PCA) and clustering, which help to detect deviations in aggregated training data. Time-series methods such as Cumulative Sum (CUSUM) charts are useful for identifying shifts indicative of potential poisoning. Comparing current data distributions with previously seen clean data distributions can also help to flag anomalies. Moreover, suspected poisoned batches should be removed from the training update aggregation process. For example, spot checks on subsets of training images on devices can be conducted using photoDNA hashes to identify poisoned inputs.\n\n\nInput Data Validation\nLastly, input data validation is essential for ensuring the integrity and validity of input data before it is fed into the training model, thereby protecting against adversarial payloads. Similarity measures, such as cosine distance, can be employed to catch inputs that deviate significantly from the expected distribution. Suspicious inputs that may contain adversarial payloads should be quarantined and sanitized. Furthermore, parser access to training data should be restricted to validated code paths only. Leveraging hardware security features, such as ARM Pointer Authentication, can prevent memory corruption (ARM Limited, 2023). An example of this is implementing input integrity checks on audio training data used by smart speakers before processing by the speech recognition model (Z. Chen and Xu 2023).\n\nChen, Zhiyong, and Shugong Xu. 2023. “Learning Domain-Heterogeneous Speaker Recognition Systems with Personalized Continual Federated Learning.” EURASIP Journal on Audio, Speech, and Music Processing 2023 (1): 33. https://doi.org/10.1186/s13636-023-00299-2.", "crumbs": [ "Deployment", "12  On-Device Learning" @@ -1379,7 +1379,7 @@ "href": "contents/ops/ops.html#historical-context", "title": "13  ML Operations", "section": "13.2 Historical Context", - "text": "13.2 Historical Context\nMLOps has its roots in DevOps, a set of practices combining software development (Dev) and IT operations (Ops) to shorten the development lifecycle and provide continuous delivery of high-quality software. The parallels between MLOps and DevOps are evident in their focus on automation, collaboration, and continuous improvement. In both cases, the goal is to break down silos between different teams (developers, operations, and, in the case of MLOps, data scientists and ML engineers) and to create a more streamlined and efficient process. It is useful to understand the history of this evolution better to understand MLOps in the context of traditional systems.\n\n13.2.1 DevOps\nThe term “DevOps” was first coined in 2009 by Patrick Debois, a consultant and Agile practitioner. Debois organized the first DevOpsDays conference in Ghent, Belgium, in 2009. The conference brought together development and operations professionals to discuss ways to improve collaboration and automate processes.\nDevOps has its roots in the Agile movement, which began in the early 2000s. Agile provided the foundation for a more collaborative approach to software development and emphasized small, iterative releases. However, Agile primarily focuses on collaboration between development teams. As Agile methodologies became more popular, organizations realized the need to extend this collaboration to operations teams.\nThe siloed nature of development and operations teams often led to inefficiencies, conflicts, and delays in software delivery. This need for better collaboration and integration between these teams led to the DevOps movement. DevOps can be seen as an extension of the Agile principles, including operations teams.\nThe key principles of DevOps include collaboration, automation, continuous integration, delivery, and feedback. DevOps focuses on automating the entire software delivery pipeline, from development to deployment. It aims to improve the collaboration between development and operations teams, utilizing tools like Jenkins, Docker, and Kubernetes to streamline the development lifecycle.\nWhile Agile and DevOps share common principles around collaboration and feedback, DevOps specifically targets integrating development and IT operations - expanding Agile beyond just development teams. It introduces practices and tools to automate software delivery and improve the speed and quality of software releases.\n\n\n13.2.2 MLOps\nMLOps, on the other hand, stands for Machine Learning Operations, and it extends the principles of DevOps to the ML lifecycle. MLOps aims to automate and streamline the end-to-end ML lifecycle, from data preparation and model development to deployment and monitoring. The main focus of MLOps is to facilitate collaboration between data scientists, data engineers, and IT operations and to automate the deployment, monitoring, and management of ML models. Some key factors led to the rise of MLOps.\n\nData drift: Data drift degrades model performance over time, motivating the need for rigorous monitoring and automated retraining procedures provided by MLOps.\nReproducibility: The lack of reproducibility in machine learning experiments motivated MLOps systems to track code, data, and environment variables to enable reproducible ML workflows.\nExplainability: The black box nature and lack of explainability of complex models motivated the need for MLOps capabilities to increase model transparency and explainability.\nMonitoring: The inability to reliably monitor model performance post-deployment highlighted the need for MLOps solutions with robust model performance instrumentation and alerting.\nFriction: The friction in manually retraining and deploying models motivated the need for MLOps systems that automate machine learning deployment pipelines.\nOptimization: The complexity of configuring machine learning infrastructure motivated the need for MLOps platforms with optimized, ready-made ML infrastructure.\n\nWhile DevOps and MLOps share the common goal of automating and streamlining processes, they differ significantly in their focus and challenges. DevOps primarily deals with software development and IT operations. It enables collaboration between these teams and automate software delivery. In contrast, MLOps focuses on the machine learning lifecycle. It addresses additional complexities such as data versioning, model versioning, and model monitoring. MLOps requires collaboration among a broader range of stakeholders, including data scientists, data engineers, and IT operations. It goes beyond the scope of traditional DevOps by incorporating the unique challenges of managing ML models throughout their lifecycle. Table 13.1 provides a side-by-side comparison of DevOps and MLOps, highlighting their key differences and similarities.\n\n\n\nTable 13.1: Comparison of DevOps and MLOps.\n\n\n\n\n\n\n\n\n\n\nAspect\nDevOps\nMLOps\n\n\n\n\nObjective\nStreamlining software development and operations processes\nOptimizing the lifecycle of machine learning models\n\n\nMethodology\nContinuous Integration and Continuous Delivery (CI/CD) for software development\nSimilar to CI/CD but focuses on machine learning workflows\n\n\nPrimary Tools\nVersion control (Git), CI/CD tools (Jenkins, Travis CI), Configuration management (Ansible, Puppet)\nData versioning tools, Model training and deployment tools, CI/CD pipelines tailored for ML\n\n\nPrimary Concerns\nCode integration, Testing, Release management, Automation, Infrastructure as code\nData management, Model versioning, Experiment tracking, Model deployment, Scalability of ML workflows\n\n\nTypical Outcomes\nFaster and more reliable software releases, Improved collaboration between development and operations teams\nEfficient management and deployment of machine learning models, Enhanced collaboration between data scientists and engineers\n\n\n\n\n\n\nLearn more about ML Lifecycles through a case study featuring speech recognition in Video 13.1.\n\n\n\n\n\n\nVideo 13.1: MLOps", + "text": "13.2 Historical Context\nMLOps has its roots in DevOps, a set of practices combining software development (Dev) and IT operations (Ops) to shorten the development lifecycle and provide continuous delivery of high-quality software. The parallels between MLOps and DevOps are evident in their focus on automation, collaboration, and continuous improvement. In both cases, the goal is to break down silos between different teams (developers, operations, and, in the case of MLOps, data scientists and ML engineers) and to create a more streamlined and efficient process. It is useful to understand the history of this evolution better to understand MLOps in the context of traditional systems.\n\n13.2.1 DevOps\nThe term “DevOps” was first coined in 2009 by Patrick Debois, a consultant and Agile practitioner. Debois organized the first DevOpsDays conference in Ghent, Belgium, in 2009. The conference brought together development and operations professionals to discuss ways to improve collaboration and automate processes.\nDevOps has its roots in the Agile movement, which began in the early 2000s. Agile provided the foundation for a more collaborative approach to software development and emphasized small, iterative releases. However, Agile primarily focuses on collaboration between development teams. As Agile methodologies became more popular, organizations realized the need to extend this collaboration to operations teams.\nThe siloed nature of development and operations teams often led to inefficiencies, conflicts, and delays in software delivery. This need for better collaboration and integration between these teams led to the DevOps movement. DevOps can be seen as an extension of the Agile principles, including operations teams.\nThe key principles of DevOps include collaboration, automation, continuous integration, delivery, and feedback. DevOps focuses on automating the entire software delivery pipeline, from development to deployment. It improves the collaboration between development and operations teams, utilizing tools like Jenkins, Docker, and Kubernetes to streamline the development lifecycle.\nWhile Agile and DevOps share common principles around collaboration and feedback, DevOps specifically targets integrating development and IT operations - expanding Agile beyond just development teams. It introduces practices and tools to automate software delivery and improve the speed and quality of software releases.\n\n\n13.2.2 MLOps\nMLOps, on the other hand, stands for Machine Learning Operations, and it extends the principles of DevOps to the ML lifecycle. MLOps automates and streamlines the end-to-end ML lifecycle, from data preparation and model development to deployment and monitoring. The main focus of MLOps is to facilitate collaboration between data scientists, data engineers, and IT operations and to automate the deployment, monitoring, and management of ML models. Some key factors led to the rise of MLOps.\n\nData drift: Data drift degrades model performance over time, motivating the need for rigorous monitoring and automated retraining procedures provided by MLOps.\nReproducibility: The lack of reproducibility in machine learning experiments motivated MLOps systems to track code, data, and environment variables to enable reproducible ML workflows.\nExplainability: The black box nature and lack of explainability of complex models motivated the need for MLOps capabilities to increase model transparency and explainability.\nMonitoring: The inability to reliably monitor model performance post-deployment highlighted the need for MLOps solutions with robust model performance instrumentation and alerting.\nFriction: The friction in manually retraining and deploying models motivated the need for MLOps systems that automate machine learning deployment pipelines.\nOptimization: The complexity of configuring machine learning infrastructure motivated the need for MLOps platforms with optimized, ready-made ML infrastructure.\n\nWhile DevOps and MLOps share the common goal of automating and streamlining processes, they differ significantly in their focus and challenges. DevOps primarily deals with software development and IT operations. It enables collaboration between these teams and automate software delivery. In contrast, MLOps focuses on the machine learning lifecycle. It addresses additional complexities such as data versioning, model versioning, and model monitoring. MLOps requires collaboration among a broader range of stakeholders, including data scientists, data engineers, and IT operations. It goes beyond the scope of traditional DevOps by incorporating the unique challenges of managing ML models throughout their lifecycle. Table 13.1 provides a side-by-side comparison of DevOps and MLOps, highlighting their key differences and similarities.\n\n\n\nTable 13.1: Comparison of DevOps and MLOps.\n\n\n\n\n\n\n\n\n\n\nAspect\nDevOps\nMLOps\n\n\n\n\nObjective\nStreamlining software development and operations processes\nOptimizing the lifecycle of machine learning models\n\n\nMethodology\nContinuous Integration and Continuous Delivery (CI/CD) for software development\nSimilar to CI/CD but focuses on machine learning workflows\n\n\nPrimary Tools\nVersion control (Git), CI/CD tools (Jenkins, Travis CI), Configuration management (Ansible, Puppet)\nData versioning tools, Model training and deployment tools, CI/CD pipelines tailored for ML\n\n\nPrimary Concerns\nCode integration, Testing, Release management, Automation, Infrastructure as code\nData management, Model versioning, Experiment tracking, Model deployment, Scalability of ML workflows\n\n\nTypical Outcomes\nFaster and more reliable software releases, Improved collaboration between development and operations teams\nEfficient management and deployment of machine learning models, Enhanced collaboration between data scientists and engineers\n\n\n\n\n\n\nLearn more about ML Lifecycles through a case study featuring speech recognition in Video 13.1.\n\n\n\n\n\n\nVideo 13.1: MLOps", "crumbs": [ "Deployment", "13  ML Operations" @@ -1434,7 +1434,7 @@ "href": "contents/ops/ops.html#traditional-mlops-vs.-embedded-mlops", "title": "13  ML Operations", "section": "13.7 Traditional MLOps vs. Embedded MLOps", - "text": "13.7 Traditional MLOps vs. Embedded MLOps\nIn traditional MLOps, ML models are typically deployed in cloud-based or server environments, with abundant resources like computing power and memory. These environments facilitate the smooth operation of complex models that require significant computational resources. For instance, a cloud-based image recognition model might be used by a social media platform to tag photos with relevant labels automatically. In this case, the model can leverage the extensive resources available in the cloud to efficiently process vast amounts of data.\nOn the other hand, embedded MLOps involves deploying ML models on embedded systems, specialized computing systems designed to perform specific functions within larger systems. Embedded systems are typically characterized by their limited computational resources and power. For example, an ML model might be embedded in a smart thermostat to optimize heating and cooling based on the user’s preferences and habits. The model must be optimized to run efficiently on the thermostat’s limited hardware without compromising its performance or accuracy.\nThe key difference between traditional and embedded MLOps lies in the embedded system’s resource constraints. While traditional MLOps can leverage abundant cloud or server resources, embedded MLOps must contend with the hardware limitations on which the model is deployed. This requires careful optimization and fine-tuning of the model to ensure it can deliver accurate and valuable insights within the embedded system’s constraints.\nFurthermore, embedded MLOps must consider the unique challenges posed by integrating ML models with other embedded system components. For example, the model must be compatible with the system’s software and hardware and must be able to interface seamlessly with other components, such as sensors or actuators. This requires a deep understanding of both ML and embedded systems and close collaboration between data scientists, engineers, and other stakeholders.\nSo, while traditional MLOps and embedded MLOps share the common goal of deploying and maintaining ML models in production environments, the unique challenges posed by embedded systems require a specialized approach. Embedded MLOps must carefully balance the need for model accuracy and performance with the constraints of the hardware on which the model is deployed. This requires a deep understanding of both ML and embedded systems and close collaboration between various stakeholders to ensure the successful integration of ML models into embedded systems.\nThis time, we will group the subtopics under broader categories to streamline the structure of our thought process on MLOps. This structure will help you understand how different aspects of MLOps are interconnected and why each is important for the efficient operation of ML systems as we discuss the challenges in the context of embedded systems.\n\nModel Lifecycle Management\n\nData Management: Handling data ingestion, validation, and version control.\nModel Training: Techniques and practices for effective and scalable model training.\nModel Evaluation: Strategies for testing and validating model performance.\nModel Deployment: Approaches for deploying models into production environments.\n\nDevelopment and Operations Integration\n\nCI/CD Pipelines: Integrating ML models into continuous integration and deployment pipelines.\nInfrastructure Management: Setting up and maintaining the infrastructure required for training and deploying models.\nCommunication & Collaboration: Ensuring smooth communication and collaboration between data scientists, ML engineers, and operations teams.\n\nOperational Excellence\n\nMonitoring: Techniques for monitoring model performance, data drift, and operational health.\nGovernance: Implementing policies for model auditability, compliance, and ethical considerations.\n\n\n\n13.7.1 Model Lifecycle Management\n\nData Management\nIn traditional centralized MLOps, data is aggregated into large datasets and data lakes, then processed on cloud or on-prem servers. However, embedded MLOps relies on decentralized data from local on-device sensors. Devices collect smaller batches of incremental data, often noisy and unstructured. With connectivity constraints, this data cannot always be instantly transmitted to the cloud and needs to be intelligently cached and processed at the edge.\nDue to limited on-device computing, embedded devices can only preprocess and clean data minimally before transmission. Early filtering and processing occur at edge gateways to reduce transmission loads. While leveraging cloud storage, more processing and storage happen at the edge to account for intermittent connectivity. Devices identify and transmit only the most critical subsets of data to the cloud.\nLabeling also needs centralized data access, requiring more automated techniques like federated learning, where devices collaboratively label peers’ data. With personal edge devices, data privacy and regulations are critical concerns. Data collection, transmission, and storage must be secure and compliant.\nFor instance, a smartwatch may collect the day’s step count, heart rate, and GPS coordinates. This data is cached locally and transmitted to an edge gateway when WiFi is available—the gateway processes and filters data before syncing relevant subsets with the cloud platform to retrain models.\n\n\nModel Training\nIn traditional centralized MLOps, models are trained using abundant data via deep learning on high-powered cloud GPU servers. However, embedded MLOps need more support in model complexity, data availability, and computing resources for training.\nThe volume of aggregated data is much lower, often requiring techniques like federated learning across devices to create training sets. The specialized nature of edge data also limits public datasets for pre-training. With privacy concerns, data samples must be tightly controlled and anonymized where possible.\nFurthermore, the models must use simplified architectures optimized for low-power edge hardware. Given the computing limitations, high-end GPUs are inaccessible for intensive deep learning. Training leverages lower-powered edge servers and clusters with distributed approaches to spread load.\nTransfer learning emerges as a crucial strategy to address data scarcity and irregularity in machine learning, particularly in edge computing scenarios. As illustrated in Figure 13.5, this approach involves pre-training models on large public datasets and then fine-tuning them on limited domain-specific edge data. The figure depicts a neural network where initial layers (W_{A1} to W_{A4}), responsible for general feature extraction, are frozen (indicated by a green dashed line). These layers retain knowledge from previous tasks, accelerating learning and reducing resource requirements. The latter layers (W_{A5} to W_{A7}), beyond the blue dashed line, are fine-tuned for the specific task, focusing on task-specific feature learning.\n\n\n\n\n\n\nFigure 13.5: Transfer learning in MLOps. Source: HarvardX.\n\n\n\nThis method not only mitigates data scarcity but also accommodates the decentralized nature of embedded data. Furthermore, techniques like incremental on-device learning can further customize models to specific use cases. The lack of broad labeled data in many domains also motivates the use of semi-supervised techniques, complementing the transfer learning approach. By leveraging pre-existing knowledge and adapting it to specialized tasks, transfer learning within an MLOps framework enables models to achieve higher performance with fewer resources, even in data-constrained environments.\nFor example, a smart home assistant may pre-train an audio recognition model on public YouTube clips, which helps bootstrap with general knowledge. It then transfers learning to a small sample of home data to classify customized appliances and events, specializing in the model. The model transforms into a lightweight neural network optimized for microphone-enabled devices across the home.\nSo, embedded MLOps face acute challenges in constructing training datasets, designing efficient models, and distributing compute for model development compared to traditional settings. Given the embedded constraints, careful adaptation, such as transfer learning and distributed training, is required to train models.\n\n\nModel Evaluation\nIn traditional centralized MLOps, models are evaluated primarily using accuracy metrics and holdout test datasets. However, embedded MLOps require a more holistic evaluation that accounts for system constraints beyond accuracy.\nModels must be tested early and often on deployed edge hardware covering diverse configurations. In addition to accuracy, factors like latency, CPU usage, memory footprint, and power consumption are critical evaluation criteria. Models are selected based on tradeoffs between these metrics to meet edge device constraints.\nData drift must also be monitored - where models trained on cloud data degrade in accuracy over time on local edge data. Embedded data often has more variability than centralized training sets. Evaluating models across diverse operational edge data samples is key. But sometimes, getting the data for monitoring the drift can be challenging if these devices are in the wild and communication is a barrier.\nOngoing monitoring provides visibility into real-world performance post-deployment, revealing bottlenecks not caught during testing. For instance, a smart camera model update may be canary tested on 100 cameras first and rolled back if degraded accuracy is observed before expanding to all 5000 cameras.\n\n\nModel Deployment\nIn traditional MLOps, new model versions are directly deployed onto servers via API endpoints. However, embedded devices require optimized delivery mechanisms to receive updated models. Over-the-air (OTA) updates provide a standardized approach to wirelessly distributing new software or firmware releases to embedded devices. Rather than direct API access, OTA packages allow remote deploying models and dependencies as pre-built bundles. Alternatively, federated learning allows model updates without direct access to raw training data. This decentralized approach has the potential for continuous model improvement but needs robust MLOps platforms.\nModel delivery relies on physical interfaces like USB or UART serial connections for deeply embedded devices lacking connectivity. The model packaging still follows similar principles to OTA updates, but the deployment mechanism is tailored to the capabilities of the edge hardware. Moreover, specialized OTA protocols optimized for IoT networks are often used rather than standard WiFi or Bluetooth protocols. Key factors include efficiency, reliability, security, and telemetry, such as progress tracking—solutions like Mender. Io provides embedded-focused OTA services handling differential updates across device fleets.\nFigure 13.6 presents an overview of Model Lifecycle Management in an MLOps context, illustrating the flow from development (top left) to deployment and monitoring (bottom right). The process begins with ML Development, where code and configurations are version-controlled. Data and model management are central to the process, involving datasets and feature repositories. Continuous training, model conversion, and model registry are key stages in the operationalization of training. The model deployment includes serving the model and managing serving logs. Alerting mechanisms are in place to flag issues, which feed into continuous monitoring to ensure model performance and reliability over time. This integrated approach ensures that models are developed and maintained effectively throughout their lifecycle.\n\n\n\n\n\n\nFigure 13.6: Model lifecycle management. Source: HarvardX.\n\n\n\n\n\n\n13.7.2 Development and Operations Integration\n\nCI/CD Pipelines\nIn traditional MLOps, robust CI/CD infrastructure like Jenkins and Kubernetes enables pipeline automation for large-scale model deployment. However, embedded MLOps need this centralized infrastructure and more tailored CI/CD workflows for edge devices.\nBuilding CI/CD pipelines has to account for a fragmented landscape of diverse hardware, firmware versions, and connectivity constraints. There is no standard platform to orchestrate pipelines, and tooling support is more limited.\nTesting must cover this wide spectrum of target embedded devices early, which is difficult without centralized access. Companies must invest significant effort into acquiring and managing test infrastructure across the heterogeneous embedded ecosystem.\nOver-the-air updates require setting up specialized servers to distribute model bundles securely to devices in the field. Rollout and rollback procedures must also be carefully tailored for particular device families.\nWith traditional CI/CD tools less applicable, embedded MLOps rely more on custom scripts and integration. Companies take varied approaches, from open-source frameworks to fully in-house solutions. Tight integration between developers, edge engineers, and end customers establishes trusted release processes.\nTherefore, embedded MLOps can’t leverage centralized cloud infrastructure for CI/CD. Companies combine custom pipelines, testing infrastructure, and OTA delivery to deploy models across fragmented and disconnected edge systems.\n\n\nInfrastructure Management\nIn traditional centralized MLOps, infrastructure entails provisioning cloud servers, GPUs, and high-bandwidth networks for intensive workloads like model training and serving predictions at scale. However, embedded MLOps require more heterogeneous infrastructure spanning edge devices, gateways, and the cloud.\nEdge devices like sensors capture and preprocess data locally before intermittent transmission to avoid overloading networks—gateways aggregate and process device data before sending select subsets to the cloud for training and analysis. The cloud provides centralized management and supplemental computing.\nThis infrastructure needs tight integration and balancing processing and communication loads. Network bandwidth is limited, requiring careful data filtering and compression. Edge computing capabilities are modest compared to the cloud, imposing optimization constraints.\nManaging secure OTA updates across large device fleets presents challenges at the edge. Rollouts must be incremental and rollback-ready for quick mitigation. Given decentralized environments, updating edge infrastructure requires coordination.\nFor example, an industrial plant may perform basic signal processing on sensors before sending data to an on-prem gateway. The gateway handles data aggregation, infrastructure monitoring, and OTA updates. Only curated data is transmitted to the cloud for advanced analytics and model retraining.\nEmbedded MLOps requires holistic management of distributed infrastructure spanning constrained edge, gateways, and centralized cloud. Workloads are balanced across tiers while accounting for connectivity, computing, and security challenges.\n\n\nCommunication & Collaboration\nIn traditional MLOps, collaboration tends to center around data scientists, ML engineers, and DevOps teams. However, embedded MLOps require tighter cross-functional coordination between additional roles to address system constraints.\nEdge engineers optimize model architectures for target hardware environments. They provide feedback to data scientists during development so models fit device capabilities early on. Similarly, product teams define operational requirements informed by end-user contexts.\nWith more stakeholders across the embedded ecosystem, communication channels must facilitate information sharing between centralized and remote teams. Issue tracking and project management ensure alignment.\nCollaborative tools optimize models for particular devices. Data scientists can log issues replicated from field devices so models specialize in niche data. Remote device access aids debugging and data collection.\nFor example, data scientists may collaborate with field teams managing fleets of wind turbines to retrieve operational data samples. This data is used to specialize models detecting anomalies specific to that turbine class. Model updates are tested in simulations and reviewed by engineers before field deployment.\nEmbedded MLOps mandates continuous coordination between data scientists, engineers, end customers, and other stakeholders throughout the ML lifecycle. Through close collaboration, models can be tailored and optimized for targeted edge devices.\n\n\n\n13.7.3 Operational Excellence\n\nMonitoring\nTraditional MLOps monitoring focuses on centrally tracking model accuracy, performance metrics, and data drift. However, embedded MLOps must account for decentralized monitoring across diverse edge devices and environments.\nEdge devices require optimized data collection to transmit key monitoring metrics without overloading networks. Metrics help assess model performance, data patterns, resource usage, and other behaviors on remote devices.\nWith limited connectivity, more analysis occurs at the edge before aggregating insights centrally. Gateways play a key role in monitoring fleet health and coordinating software updates. Confirmed indicators are eventually propagated to the cloud.\nBroad device coverage is challenging but critical. Issues specific to certain device types may arise, so monitoring needs to cover the full spectrum. Canary deployments help trial monitoring processes before scaling.\nAnomaly detection identifies incidents requiring rolling back models or retraining on new data. However, interpreting alerts requires understanding unique device contexts based on input from engineers and customers.\nFor example, an automaker may monitor autonomous vehicles for indicators of model degradation using caching, aggregation, and real-time streams. Engineers assess when identified anomalies warrant OTA updates to improve models based on factors like location and vehicle age.\nEmbedded MLOps monitoring provides observability into model and system performance across decentralized edge environments. Careful data collection, analysis, and collaboration deliver meaningful insights to maintain reliability.\n\n\nGovernance\nIn traditional MLOps, governance focuses on model explainability, fairness, and compliance for centralized systems. However, embedded MLOps must also address device-level governance challenges related to data privacy, security, and safety.\nWith sensors collecting personal and sensitive data, local data governance on devices is critical. Data access controls, anonymization, and encrypted caching help address privacy risks and compliance like HIPAA and GDPR. Updates must maintain security patches and settings.\nSafety governance considers the physical impacts of flawed device behavior. Failures could cause unsafe conditions in vehicles, factories, and critical systems. Redundancy, fail-safes, and warning systems help mitigate risks.\nTraditional governance, such as bias monitoring and model explainability, remains imperative but is harder to implement for embedded AI. Peeking into black-box models on low-power devices also poses challenges.\nFor example, a medical device may scrub personal data on the device before transmission. Strict data governance protocols approve model updates. Model explainability is limited, but the focus is on detecting anomalous behavior. Backup systems prevent failures.\nEmbedded MLOps governance must encompass privacy, security, safety, transparency, and ethics. Specialized techniques and team collaboration are needed to help establish trust and accountability within decentralized environments.\n\n\n\n13.7.4 Comparison\nTable 13.2 highlights the similarities and differences between Traditional MLOps and Embedded MLOps based on all the things we have learned thus far:\n\n\n\nTable 13.2: Comparison of Traditional MLOps and Embedded MLOps practices.\n\n\n\n\n\n\n\n\n\n\nArea\nTraditional MLOps\nEmbedded MLOps\n\n\n\n\nData Management\nLarge datasets, data lakes, feature stores\nOn-device data capture, edge caching and processing\n\n\nModel Development\nLeverage deep learning, complex neural nets, GPU training\nConstraints on model complexity, need for optimization\n\n\nDeployment\nServer clusters, cloud deployment, low latency at scale\nOTA deployment to devices, intermittent connectivity\n\n\nMonitoring\nDashboards, logs, alerts for cloud model performance\nOn-device monitoring of predictions, resource usage\n\n\nRetraining\nRetrain models on new data\nFederated learning from devices, edge retraining\n\n\nInfrastructure\nDynamic cloud infrastructure\nHeterogeneous edge/cloud infrastructure\n\n\nCollaboration\nShared experiment tracking and model registry\nCollaboration for device-specific optimization\n\n\n\n\n\n\nSo, while Embedded MLOps shares foundational MLOps principles, it faces unique constraints in tailoring workflows and infrastructure specifically for resource-constrained edge devices.\n\n\n13.7.5 Traditional MLOps\nGoogle, Microsoft, and Amazon offer their version of managed ML services. These include services that manage model training and experimentation, model hosting and scaling, and monitoring. These offerings are available via an API and client SDKs, as well as through web UIs. While it is possible to build your own end-to-end MLOps solutions using pieces from each, the greatest ease of use benefits come by staying within a single provider ecosystem to take advantage of interservice integrations.\nThe following sections present a quick overview of the services that fit into each part of the MLOps life cycle described above, providing examples of offerings from different providers. It’s important to note that the MLOps space is evolving rapidly; new companies and products are entering the scene at a swift pace. The examples mentioned are not meant to serve as endorsements of particular companies’ offerings but rather to illustrate the types of solutions available in the market.\n\nData Management\nData storage and versioning are table stakes for any commercial offering, and most take advantage of existing general-purpose storage solutions such as S3. Others use more specialized options such as git-based storage (Example: Hugging Face’s Dataset Hub). This is an area where providers make it easy to support their competitors’ data storage options, as they don’t want this to be a barrier for adoptions of the rest of their MLOps services. For example, Vertex AI’s training pipeline seamlessly supports datasets stored in S3, Google Cloud Buckets, or Hugging Face’s Dataset Hub.\n\n\nModel Training\nManaged training services are where cloud providers shine, as they provide on-demand access to hardware that is out of reach for most smaller companies. They bill only for hardware during training time, putting GPU-accelerated training within reach of even the smallest developer teams. The control developers have over their training workflow can vary widely depending on their needs. Some providers have services that provide little more than access to the resources and rely on the developer to manage the training loop, logging, and model storage themselves. Other services are as simple as pointing to a base model and a labeled data set to kick off a fully managed finetuning job (example: Vertex AI Fine Tuning).\nA word of warning: As of 2023, GPU hardware demand well exceeds supply, and as a result, cloud providers are rationing access to their GPUs. In some data center regions, GPUs may be unavailable or require long-term contracts.\n\n\nModel Evaluation\nModel evaluation tasks typically involve monitoring models’ accuracy, latency, and resource usage in both the testing and production phases. Unlike embedded systems, ML models deployed to the cloud benefit from constant internet connectivity and unlimited logging capacities. As a result, it is often feasible to capture and log every request and response. This makes replaying or generating synthetic requests to compare different models and versions tractable.\nSome providers also offer services that automate the experiment tracking of modifying model hyperparameters. They track the runs and performance and generate artifacts from these model training runs. Example: WeightsAndBiases\n\n\nModel Deployment\nEach provider typically has a service referred to as a “model registry,” where training models are stored and accessed. Often, these registries may also provide access to base models that are either open source or provided by larger technology companies (or, in some cases, like LLAMA, both!). These model registries are a common place to compare all the models and their versions to allow easy decision-making on which to pick for a given use case. Example: Vertex AI’s model registry\nFrom the model registry, deploying a model to an inference endpoint is quick and simple, and it handles the resource provisioning, model weight downloading, and hosting of a given model. These services typically give access to the model via a REST API where inference requests can be sent. Depending on the model type, specific resources can be configured, such as which type of GPU accelerator may be needed to hit the desired performance. Some providers may also offer serverless inference or batch inference options that do not need a persistent endpoint to access the model. Example: AWS SageMaker Inference\n\n\n\n13.7.6 Embedded MLOps\nDespite the proliferation of new ML Ops tools in response to the increase in demand, the challenges described earlier have constrained the availability of such tools in embedded systems environments. More recently, new tools such as Edge Impulse (Janapa Reddi et al. 2023) have made the development process somewhat easier, as described below.\n\nJanapa Reddi, Vijay, Alexander Elium, Shawn Hymel, David Tischler, Daniel Situnayake, Carl Ward, Louis Moreau, et al. 2023. “Edge Impulse: An MLOps Platform for Tiny Machine Learning.” Proceedings of Machine Learning and Systems 5.\n\nEdge Impulse\nEdge Impulse is an end-to-end development platform for creating and deploying machine learning models onto edge devices such as microcontrollers and small processors. It aims to make embedded machine learning more accessible to software developers through its easy-to-use web interface and integrated tools for data collection, model development, optimization, and deployment. Its key capabilities include the following:\n\nIntuitive drag-and-drop workflow for building ML models without coding required\nTools for acquiring, labeling, visualizing, and preprocessing data from sensors\nChoice of model architectures, including neural networks and unsupervised learning\nModel optimization techniques to balance performance metrics and hardware constraints\n\nSeamless deployment onto edge devices through compilation, SDKs, and benchmarks\nCollaboration features for teams and integration with other platforms\n\nWith Edge Impulse, developers with limited data science expertise can develop specialized ML models that run efficiently within small computing environments. It provides a comprehensive solution for creating embedded intelligence and advancing machine learning.\n\nUser Interface\nEdge Impulse was designed with seven key principles: accessibility, end-to-end capabilities, a data-centric approach, interactiveness, extensibility, team orientation, and community support. The intuitive user interface, shown in Figure 13.7, guides developers at all experience levels through uploading data, selecting a model architecture, training the model, and deploying it across relevant hardware platforms. It should be noted that, like any tool, Edge Impulse is intended to assist with, not replace, foundational considerations such as determining if ML is an appropriate solution or acquiring the requisite domain expertise for a given application.\n\n\n\n\n\n\nFigure 13.7: Screenshot of Edge Impulse user interface for building workflows from input data to output features.\n\n\n\nWhat makes Edge Impulse notable is its comprehensive yet intuitive end-to-end workflow. Developers start by uploading their data via the graphical user interface (GUI) or command line interface (CLI) tools, after which they can examine raw samples and visualize the data distribution in the training and test splits. Next, users can pick from various preprocessing “blocks” to facilitate digital signal processing (DSP). While default parameter values are provided, users can customize the parameters as needed, with considerations around memory and latency displayed. Users can easily choose their neural network architecture - without any code needed.\nThanks to the platform’s visual editor, users can customize the architecture’s components and specific parameters while ensuring that the model is still trainable. Users can also leverage unsupervised learning algorithms, such as K-means clustering and Gaussian mixture models (GMM).\n\n\nOptimizations\nTo accommodate the resource constraints of TinyML applications, Edge Impulse provides a confusion matrix summarizing key performance metrics, including per-class accuracy and F1 scores. The platform elucidates the tradeoffs between model performance, size, and latency using simulations in Renode and device-specific benchmarking. For streaming data use cases, a performance calibration tool leverages a genetic algorithm to find ideal post-processing configurations balancing false acceptance and false rejection rates. Techniques like quantization, code optimization, and device-specific optimization are available to optimize models. For deployment, models can be compiled in appropriate formats for target edge devices. Native firmware SDKs also enable direct data collection on devices.\nIn addition to streamlining development, Edge Impulse scales the modeling process itself. A key capability is the EON Tuner, an automated machine learning (AutoML) tool that assists users in hyperparameter tuning based on system constraints. It runs a random search to generate configurations for digital signal processing and training steps quickly. The resulting models are displayed for the user to select based on relevant performance, memory, and latency metrics. For data, active learning facilitates training on a small labeled subset, followed by manually or automatically labeling new samples based on proximity to existing classes. This expands data efficiency.\n\n\nUse Cases\nBeyond the accessibility of the platform itself, the Edge Impulse team has expanded the knowledge base of the embedded ML ecosystem. The platform lends itself to academic environments, having been used in online courses and on-site workshops globally. Numerous case studies featuring industry and research use cases have been published, most notably Oura Ring, which uses ML to identify sleep patterns. The team has made repositories open source on GitHub, facilitating community growth. Users can also make projects public to share techniques and download libraries to share via Apache. Organization-level access enables collaboration on workflows.\nOverall, Edge Impulse is uniquely comprehensive and integrateable for developer workflows. Larger platforms like Google and Microsoft focus more on cloud versus embedded systems. TinyMLOps frameworks such as Neuton AI and Latent AI offer some functionality but lack Edge Impulse’s end-to-end capabilities. TensorFlow Lite Micro is the standard inference engine due to flexibility, open source status, and TensorFlow integration, but it uses more memory and storage than Edge Impulse’s EON Compiler. Other platforms need to be updated, academic-focused, or more versatile. In summary, Edge Impulse aims to streamline and scale embedded ML through an accessible, automated platform.\n\n\n\nLimitations\nWhile Edge Impulse provides an accessible pipeline for embedded ML, important limitations and risks remain. A key challenge is data quality and availability - the models are only as good as the data used to train them. Users must have sufficient labeled samples that capture the breadth of expected operating conditions and failure modes. Labeled anomalies and outliers are critical yet time-consuming to collect and identify. Insufficient or biased data leads to poor model performance regardless of the tool’s capabilities.\nDeploying low-powered devices also presents inherent challenges. Optimized models may still need to be more resource-intensive for ultra-low-power MCUs. Striking the right balance of compression versus accuracy takes some experimentation. The tool simplifies but still needs to eliminate the need for foundational ML and signal processing expertise. Embedded environments also constrain debugging and interpretability compared to the cloud.\nWhile impressive results are achievable, users shouldn’t view Edge Impulse as a “Push Button ML” solution. Careful project scoping, data collection, model evaluation, and testing are still essential. As with any development tool, reasonable expectations and diligence in application are advised. However, Edge Impulse can accelerate embedded ML prototyping and deployment for developers willing to invest the requisite data science and engineering effort.\n\n\n\n\n\n\nExercise 13.1: Edge Impulse\n\n\n\n\n\nReady to level up your tiny machine-learning projects? Let’s combine the power of Edge Impulse with the awesome visualizations of Weights & Biases (WandB). In this Colab, you’ll learn to track your model’s training progress like a pro! Imagine seeing cool graphs of your model getting smarter, comparing different versions, and ensuring your AI performs its best even on tiny devices.", + "text": "13.7 Traditional MLOps vs. Embedded MLOps\nIn traditional MLOps, ML models are typically deployed in cloud-based or server environments, with abundant resources like computing power and memory. These environments facilitate the smooth operation of complex models that require significant computational resources. For instance, a cloud-based image recognition model might be used by a social media platform to tag photos with relevant labels automatically. In this case, the model can leverage the extensive resources available in the cloud to efficiently process vast amounts of data.\nOn the other hand, embedded MLOps involves deploying ML models on embedded systems, specialized computing systems designed to perform specific functions within larger systems. Embedded systems are typically characterized by their limited computational resources and power. For example, an ML model might be embedded in a smart thermostat to optimize heating and cooling based on the user’s preferences and habits. The model must be optimized to run efficiently on the thermostat’s limited hardware without compromising its performance or accuracy.\nThe key difference between traditional and embedded MLOps lies in the embedded system’s resource constraints. While traditional MLOps can leverage abundant cloud or server resources, embedded MLOps must contend with the hardware limitations on which the model is deployed. This requires careful optimization and fine-tuning of the model to ensure it can deliver accurate and valuable insights within the embedded system’s constraints.\nFurthermore, embedded MLOps must consider the unique challenges posed by integrating ML models with other embedded system components. For example, the model must be compatible with the system’s software and hardware and must be able to interface seamlessly with other components, such as sensors or actuators. This requires a deep understanding of both ML and embedded systems and close collaboration between data scientists, engineers, and other stakeholders.\nSo, while traditional MLOps and embedded MLOps share the common goal of deploying and maintaining ML models in production environments, the unique challenges posed by embedded systems require a specialized approach. Embedded MLOps must carefully balance the need for model accuracy and performance with the constraints of the hardware on which the model is deployed. This requires a deep understanding of both ML and embedded systems and close collaboration between various stakeholders to ensure the successful integration of ML models into embedded systems.\nThis time, we will group the subtopics under broader categories to streamline the structure of our thought process on MLOps. This structure will help you understand how different aspects of MLOps are interconnected and why each is important for the efficient operation of ML systems as we discuss the challenges in the context of embedded systems.\n\nModel Lifecycle Management\n\nData Management: Handling data ingestion, validation, and version control.\nModel Training: Techniques and practices for effective and scalable model training.\nModel Evaluation: Strategies for testing and validating model performance.\nModel Deployment: Approaches for deploying models into production environments.\n\nDevelopment and Operations Integration\n\nCI/CD Pipelines: Integrating ML models into continuous integration and deployment pipelines.\nInfrastructure Management: Setting up and maintaining the infrastructure required for training and deploying models.\nCommunication & Collaboration: Ensuring smooth communication and collaboration between data scientists, ML engineers, and operations teams.\n\nOperational Excellence\n\nMonitoring: Techniques for monitoring model performance, data drift, and operational health.\nGovernance: Implementing policies for model auditability, compliance, and ethical considerations.\n\n\n\n13.7.1 Model Lifecycle Management\n\nData Management\nIn traditional centralized MLOps, data is aggregated into large datasets and data lakes, then processed on cloud or on-prem servers. However, embedded MLOps relies on decentralized data from local on-device sensors. Devices collect smaller batches of incremental data, often noisy and unstructured. With connectivity constraints, this data cannot always be instantly transmitted to the cloud and needs to be intelligently cached and processed at the edge.\nDue to limited on-device computing, embedded devices can only preprocess and clean data minimally before transmission. Early filtering and processing occur at edge gateways to reduce transmission loads. While leveraging cloud storage, more processing and storage happen at the edge to account for intermittent connectivity. Devices identify and transmit only the most critical subsets of data to the cloud.\nLabeling also needs centralized data access, requiring more automated techniques like federated learning, where devices collaboratively label peers’ data. With personal edge devices, data privacy and regulations are critical concerns. Data collection, transmission, and storage must be secure and compliant.\nFor instance, a smartwatch may collect the day’s step count, heart rate, and GPS coordinates. This data is cached locally and transmitted to an edge gateway when WiFi is available—the gateway processes and filters data before syncing relevant subsets with the cloud platform to retrain models.\n\n\nModel Training\nIn traditional centralized MLOps, models are trained using abundant data via deep learning on high-powered cloud GPU servers. However, embedded MLOps need more support in model complexity, data availability, and computing resources for training.\nThe volume of aggregated data is much lower, often requiring techniques like federated learning across devices to create training sets. The specialized nature of edge data also limits public datasets for pre-training. With privacy concerns, data samples must be tightly controlled and anonymized where possible.\nFurthermore, the models must use simplified architectures optimized for low-power edge hardware. Given the computing limitations, high-end GPUs are inaccessible for intensive deep learning. Training leverages lower-powered edge servers and clusters with distributed approaches to spread load.\nTransfer learning emerges as a crucial strategy to address data scarcity and irregularity in machine learning, particularly in edge computing scenarios. As illustrated in Figure 13.5, this approach involves pre-training models on large public datasets and then fine-tuning them on limited domain-specific edge data. The figure depicts a neural network where initial layers (W_{A1} to W_{A4}), responsible for general feature extraction, are frozen (indicated by a green dashed line). These layers retain knowledge from previous tasks, accelerating learning and reducing resource requirements. The latter layers (W_{A5} to W_{A7}), beyond the blue dashed line, are fine-tuned for the specific task, focusing on task-specific feature learning.\n\n\n\n\n\n\nFigure 13.5: Transfer learning in MLOps. Source: HarvardX.\n\n\n\nThis method not only mitigates data scarcity but also accommodates the decentralized nature of embedded data. Furthermore, techniques like incremental on-device learning can further customize models to specific use cases. The lack of broad labeled data in many domains also motivates the use of semi-supervised techniques, complementing the transfer learning approach. By leveraging pre-existing knowledge and adapting it to specialized tasks, transfer learning within an MLOps framework enables models to achieve higher performance with fewer resources, even in data-constrained environments.\nFor example, a smart home assistant may pre-train an audio recognition model on public YouTube clips, which helps bootstrap with general knowledge. It then transfers learning to a small sample of home data to classify customized appliances and events, specializing in the model. The model transforms into a lightweight neural network optimized for microphone-enabled devices across the home.\nSo, embedded MLOps face acute challenges in constructing training datasets, designing efficient models, and distributing compute for model development compared to traditional settings. Given the embedded constraints, careful adaptation, such as transfer learning and distributed training, is required to train models.\n\n\nModel Evaluation\nIn traditional centralized MLOps, models are evaluated primarily using accuracy metrics and holdout test datasets. However, embedded MLOps require a more holistic evaluation that accounts for system constraints beyond accuracy.\nModels must be tested early and often on deployed edge hardware covering diverse configurations. In addition to accuracy, factors like latency, CPU usage, memory footprint, and power consumption are critical evaluation criteria. Models are selected based on tradeoffs between these metrics to meet edge device constraints.\nData drift must also be monitored - where models trained on cloud data degrade in accuracy over time on local edge data. Embedded data often has more variability than centralized training sets. Evaluating models across diverse operational edge data samples is key. But sometimes, getting the data for monitoring the drift can be challenging if these devices are in the wild and communication is a barrier.\nOngoing monitoring provides visibility into real-world performance post-deployment, revealing bottlenecks not caught during testing. For instance, a smart camera model update may be canary tested on 100 cameras first and rolled back if degraded accuracy is observed before expanding to all 5000 cameras.\n\n\nModel Deployment\nIn traditional MLOps, new model versions are directly deployed onto servers via API endpoints. However, embedded devices require optimized delivery mechanisms to receive updated models. Over-the-air (OTA) updates provide a standardized approach to wirelessly distributing new software or firmware releases to embedded devices. Rather than direct API access, OTA packages allow remote deploying models and dependencies as pre-built bundles. Alternatively, federated learning allows model updates without direct access to raw training data. This decentralized approach has the potential for continuous model improvement but needs robust MLOps platforms.\nModel delivery relies on physical interfaces like USB or UART serial connections for deeply embedded devices lacking connectivity. The model packaging still follows similar principles to OTA updates, but the deployment mechanism is tailored to the capabilities of the edge hardware. Moreover, specialized OTA protocols optimized for IoT networks are often used rather than standard WiFi or Bluetooth protocols. Key factors include efficiency, reliability, security, and telemetry, such as progress tracking—solutions like Mender. Io provides embedded-focused OTA services handling differential updates across device fleets.\nFigure 13.6 presents an overview of Model Lifecycle Management in an MLOps context, illustrating the flow from development (top left) to deployment and monitoring (bottom right). The process begins with ML Development, where code and configurations are version-controlled. Data and model management are central to the process, involving datasets and feature repositories. Continuous training, model conversion, and model registry are key stages in the operationalization of training. The model deployment includes serving the model and managing serving logs. Alerting mechanisms are in place to flag issues, which feed into continuous monitoring to ensure model performance and reliability over time. This integrated approach ensures that models are developed and maintained effectively throughout their lifecycle.\n\n\n\n\n\n\nFigure 13.6: Model lifecycle management. Source: HarvardX.\n\n\n\n\n\n\n13.7.2 Development and Operations Integration\n\nCI/CD Pipelines\nIn traditional MLOps, robust CI/CD infrastructure like Jenkins and Kubernetes enables pipeline automation for large-scale model deployment. However, embedded MLOps need this centralized infrastructure and more tailored CI/CD workflows for edge devices.\nBuilding CI/CD pipelines has to account for a fragmented landscape of diverse hardware, firmware versions, and connectivity constraints. There is no standard platform to orchestrate pipelines, and tooling support is more limited.\nTesting must cover this wide spectrum of target embedded devices early, which is difficult without centralized access. Companies must invest significant effort into acquiring and managing test infrastructure across the heterogeneous embedded ecosystem.\nOver-the-air updates require setting up specialized servers to distribute model bundles securely to devices in the field. Rollout and rollback procedures must also be carefully tailored for particular device families.\nWith traditional CI/CD tools less applicable, embedded MLOps rely more on custom scripts and integration. Companies take varied approaches, from open-source frameworks to fully in-house solutions. Tight integration between developers, edge engineers, and end customers establishes trusted release processes.\nTherefore, embedded MLOps can’t leverage centralized cloud infrastructure for CI/CD. Companies combine custom pipelines, testing infrastructure, and OTA delivery to deploy models across fragmented and disconnected edge systems.\n\n\nInfrastructure Management\nIn traditional centralized MLOps, infrastructure entails provisioning cloud servers, GPUs, and high-bandwidth networks for intensive workloads like model training and serving predictions at scale. However, embedded MLOps require more heterogeneous infrastructure spanning edge devices, gateways, and the cloud.\nEdge devices like sensors capture and preprocess data locally before intermittent transmission to avoid overloading networks—gateways aggregate and process device data before sending select subsets to the cloud for training and analysis. The cloud provides centralized management and supplemental computing.\nThis infrastructure needs tight integration and balancing processing and communication loads. Network bandwidth is limited, requiring careful data filtering and compression. Edge computing capabilities are modest compared to the cloud, imposing optimization constraints.\nManaging secure OTA updates across large device fleets presents challenges at the edge. Rollouts must be incremental and rollback-ready for quick mitigation. Given decentralized environments, updating edge infrastructure requires coordination.\nFor example, an industrial plant may perform basic signal processing on sensors before sending data to an on-prem gateway. The gateway handles data aggregation, infrastructure monitoring, and OTA updates. Only curated data is transmitted to the cloud for advanced analytics and model retraining.\nEmbedded MLOps requires holistic management of distributed infrastructure spanning constrained edge, gateways, and centralized cloud. Workloads are balanced across tiers while accounting for connectivity, computing, and security challenges.\n\n\nCommunication & Collaboration\nIn traditional MLOps, collaboration tends to center around data scientists, ML engineers, and DevOps teams. However, embedded MLOps require tighter cross-functional coordination between additional roles to address system constraints.\nEdge engineers optimize model architectures for target hardware environments. They provide feedback to data scientists during development so models fit device capabilities early on. Similarly, product teams define operational requirements informed by end-user contexts.\nWith more stakeholders across the embedded ecosystem, communication channels must facilitate information sharing between centralized and remote teams. Issue tracking and project management ensure alignment.\nCollaborative tools optimize models for particular devices. Data scientists can log issues replicated from field devices so models specialize in niche data. Remote device access aids debugging and data collection.\nFor example, data scientists may collaborate with field teams managing fleets of wind turbines to retrieve operational data samples. This data is used to specialize models detecting anomalies specific to that turbine class. Model updates are tested in simulations and reviewed by engineers before field deployment.\nEmbedded MLOps mandates continuous coordination between data scientists, engineers, end customers, and other stakeholders throughout the ML lifecycle. Through close collaboration, models can be tailored and optimized for targeted edge devices.\n\n\n\n13.7.3 Operational Excellence\n\nMonitoring\nTraditional MLOps monitoring focuses on centrally tracking model accuracy, performance metrics, and data drift. However, embedded MLOps must account for decentralized monitoring across diverse edge devices and environments.\nEdge devices require optimized data collection to transmit key monitoring metrics without overloading networks. Metrics help assess model performance, data patterns, resource usage, and other behaviors on remote devices.\nWith limited connectivity, more analysis occurs at the edge before aggregating insights centrally. Gateways play a key role in monitoring fleet health and coordinating software updates. Confirmed indicators are eventually propagated to the cloud.\nBroad device coverage is challenging but critical. Issues specific to certain device types may arise, so monitoring needs to cover the full spectrum. Canary deployments help trial monitoring processes before scaling.\nAnomaly detection identifies incidents requiring rolling back models or retraining on new data. However, interpreting alerts requires understanding unique device contexts based on input from engineers and customers.\nFor example, an automaker may monitor autonomous vehicles for indicators of model degradation using caching, aggregation, and real-time streams. Engineers assess when identified anomalies warrant OTA updates to improve models based on factors like location and vehicle age.\nEmbedded MLOps monitoring provides observability into model and system performance across decentralized edge environments. Careful data collection, analysis, and collaboration deliver meaningful insights to maintain reliability.\n\n\nGovernance\nIn traditional MLOps, governance focuses on model explainability, fairness, and compliance for centralized systems. However, embedded MLOps must also address device-level governance challenges related to data privacy, security, and safety.\nWith sensors collecting personal and sensitive data, local data governance on devices is critical. Data access controls, anonymization, and encrypted caching help address privacy risks and compliance like HIPAA and GDPR. Updates must maintain security patches and settings.\nSafety governance considers the physical impacts of flawed device behavior. Failures could cause unsafe conditions in vehicles, factories, and critical systems. Redundancy, fail-safes, and warning systems help mitigate risks.\nTraditional governance, such as bias monitoring and model explainability, remains imperative but is harder to implement for embedded AI. Peeking into black-box models on low-power devices also poses challenges.\nFor example, a medical device may scrub personal data on the device before transmission. Strict data governance protocols approve model updates. Model explainability is limited, but the focus is on detecting anomalous behavior. Backup systems prevent failures.\nEmbedded MLOps governance must encompass privacy, security, safety, transparency, and ethics. Specialized techniques and team collaboration are needed to help establish trust and accountability within decentralized environments.\n\n\n\n13.7.4 Comparison\nTable 13.2 highlights the similarities and differences between Traditional MLOps and Embedded MLOps based on all the things we have learned thus far:\n\n\n\nTable 13.2: Comparison of Traditional MLOps and Embedded MLOps practices.\n\n\n\n\n\n\n\n\n\n\nArea\nTraditional MLOps\nEmbedded MLOps\n\n\n\n\nData Management\nLarge datasets, data lakes, feature stores\nOn-device data capture, edge caching and processing\n\n\nModel Development\nLeverage deep learning, complex neural nets, GPU training\nConstraints on model complexity, need for optimization\n\n\nDeployment\nServer clusters, cloud deployment, low latency at scale\nOTA deployment to devices, intermittent connectivity\n\n\nMonitoring\nDashboards, logs, alerts for cloud model performance\nOn-device monitoring of predictions, resource usage\n\n\nRetraining\nRetrain models on new data\nFederated learning from devices, edge retraining\n\n\nInfrastructure\nDynamic cloud infrastructure\nHeterogeneous edge/cloud infrastructure\n\n\nCollaboration\nShared experiment tracking and model registry\nCollaboration for device-specific optimization\n\n\n\n\n\n\nSo, while Embedded MLOps shares foundational MLOps principles, it faces unique constraints in tailoring workflows and infrastructure specifically for resource-constrained edge devices.\n\n\n13.7.5 Traditional MLOps\nGoogle, Microsoft, and Amazon offer their version of managed ML services. These include services that manage model training and experimentation, model hosting and scaling, and monitoring. These offerings are available via an API and client SDKs, as well as through web UIs. While it is possible to build your own end-to-end MLOps solutions using pieces from each, the greatest ease of use benefits come by staying within a single provider ecosystem to take advantage of interservice integrations.\nThe following sections present a quick overview of the services that fit into each part of the MLOps life cycle described above, providing examples of offerings from different providers. It’s important to note that the MLOps space is evolving rapidly; new companies and products are entering the scene at a swift pace. The examples mentioned are not meant to serve as endorsements of particular companies’ offerings but rather to illustrate the types of solutions available in the market.\n\nData Management\nData storage and versioning are table stakes for any commercial offering, and most take advantage of existing general-purpose storage solutions such as S3. Others use more specialized options such as git-based storage (Example: Hugging Face’s Dataset Hub). This is an area where providers make it easy to support their competitors’ data storage options, as they don’t want this to be a barrier for adoptions of the rest of their MLOps services. For example, Vertex AI’s training pipeline seamlessly supports datasets stored in S3, Google Cloud Buckets, or Hugging Face’s Dataset Hub.\n\n\nModel Training\nManaged training services are where cloud providers shine, as they provide on-demand access to hardware that is out of reach for most smaller companies. They bill only for hardware during training time, putting GPU-accelerated training within reach of even the smallest developer teams. The control developers have over their training workflow can vary widely depending on their needs. Some providers have services that provide little more than access to the resources and rely on the developer to manage the training loop, logging, and model storage themselves. Other services are as simple as pointing to a base model and a labeled data set to kick off a fully managed finetuning job (example: Vertex AI Fine Tuning).\nA word of warning: As of 2023, GPU hardware demand well exceeds supply, and as a result, cloud providers are rationing access to their GPUs. In some data center regions, GPUs may be unavailable or require long-term contracts.\n\n\nModel Evaluation\nModel evaluation tasks typically involve monitoring models’ accuracy, latency, and resource usage in both the testing and production phases. Unlike embedded systems, ML models deployed to the cloud benefit from constant internet connectivity and unlimited logging capacities. As a result, it is often feasible to capture and log every request and response. This makes replaying or generating synthetic requests to compare different models and versions tractable.\nSome providers also offer services that automate the experiment tracking of modifying model hyperparameters. They track the runs and performance and generate artifacts from these model training runs. Example: WeightsAndBiases\n\n\nModel Deployment\nEach provider typically has a service referred to as a “model registry,” where training models are stored and accessed. Often, these registries may also provide access to base models that are either open source or provided by larger technology companies (or, in some cases, like LLAMA, both!). These model registries are a common place to compare all the models and their versions to allow easy decision-making on which to pick for a given use case. Example: Vertex AI’s model registry\nFrom the model registry, deploying a model to an inference endpoint is quick and simple, and it handles the resource provisioning, model weight downloading, and hosting of a given model. These services typically give access to the model via a REST API where inference requests can be sent. Depending on the model type, specific resources can be configured, such as which type of GPU accelerator may be needed to hit the desired performance. Some providers may also offer serverless inference or batch inference options that do not need a persistent endpoint to access the model. Example: AWS SageMaker Inference\n\n\n\n13.7.6 Embedded MLOps\nDespite the proliferation of new ML Ops tools in response to the increase in demand, the challenges described earlier have constrained the availability of such tools in embedded systems environments. More recently, new tools such as Edge Impulse (Janapa Reddi et al. 2023) have made the development process somewhat easier, as described below.\n\nJanapa Reddi, Vijay, Alexander Elium, Shawn Hymel, David Tischler, Daniel Situnayake, Carl Ward, Louis Moreau, et al. 2023. “Edge Impulse: An MLOps Platform for Tiny Machine Learning.” Proceedings of Machine Learning and Systems 5.\n\nEdge Impulse\nEdge Impulse is an end-to-end development platform for creating and deploying machine learning models onto edge devices such as microcontrollers and small processors. It makes embedded machine learning more accessible to software developers through its easy-to-use web interface and integrated tools for data collection, model development, optimization, and deployment. Its key capabilities include the following:\n\nIntuitive drag-and-drop workflow for building ML models without coding required\nTools for acquiring, labeling, visualizing, and preprocessing data from sensors\nChoice of model architectures, including neural networks and unsupervised learning\nModel optimization techniques to balance performance metrics and hardware constraints\n\nSeamless deployment onto edge devices through compilation, SDKs, and benchmarks\nCollaboration features for teams and integration with other platforms\n\nWith Edge Impulse, developers with limited data science expertise can develop specialized ML models that run efficiently within small computing environments. It provides a comprehensive solution for creating embedded intelligence and advancing machine learning.\n\nUser Interface\nEdge Impulse was designed with seven key principles: accessibility, end-to-end capabilities, a data-centric approach, interactiveness, extensibility, team orientation, and community support. The intuitive user interface, shown in Figure 13.7, guides developers at all experience levels through uploading data, selecting a model architecture, training the model, and deploying it across relevant hardware platforms. It should be noted that, like any tool, Edge Impulse is intended to assist with, not replace, foundational considerations such as determining if ML is an appropriate solution or acquiring the requisite domain expertise for a given application.\n\n\n\n\n\n\nFigure 13.7: Screenshot of Edge Impulse user interface for building workflows from input data to output features.\n\n\n\nWhat makes Edge Impulse notable is its comprehensive yet intuitive end-to-end workflow. Developers start by uploading their data via the graphical user interface (GUI) or command line interface (CLI) tools, after which they can examine raw samples and visualize the data distribution in the training and test splits. Next, users can pick from various preprocessing “blocks” to facilitate digital signal processing (DSP). While default parameter values are provided, users can customize the parameters as needed, with considerations around memory and latency displayed. Users can easily choose their neural network architecture - without any code needed.\nThanks to the platform’s visual editor, users can customize the architecture’s components and specific parameters while ensuring that the model is still trainable. Users can also leverage unsupervised learning algorithms, such as K-means clustering and Gaussian mixture models (GMM).\n\n\nOptimizations\nTo accommodate the resource constraints of TinyML applications, Edge Impulse provides a confusion matrix summarizing key performance metrics, including per-class accuracy and F1 scores. The platform elucidates the tradeoffs between model performance, size, and latency using simulations in Renode and device-specific benchmarking. For streaming data use cases, a performance calibration tool leverages a genetic algorithm to find ideal post-processing configurations balancing false acceptance and false rejection rates. Techniques like quantization, code optimization, and device-specific optimization are available to optimize models. For deployment, models can be compiled in appropriate formats for target edge devices. Native firmware SDKs also enable direct data collection on devices.\nIn addition to streamlining development, Edge Impulse scales the modeling process itself. A key capability is the EON Tuner, an automated machine learning (AutoML) tool that assists users in hyperparameter tuning based on system constraints. It runs a random search to generate configurations for digital signal processing and training steps quickly. The resulting models are displayed for the user to select based on relevant performance, memory, and latency metrics. For data, active learning facilitates training on a small labeled subset, followed by manually or automatically labeling new samples based on proximity to existing classes. This expands data efficiency.\n\n\nUse Cases\nBeyond the accessibility of the platform itself, the Edge Impulse team has expanded the knowledge base of the embedded ML ecosystem. The platform lends itself to academic environments, having been used in online courses and on-site workshops globally. Numerous case studies featuring industry and research use cases have been published, most notably Oura Ring, which uses ML to identify sleep patterns. The team has made repositories open source on GitHub, facilitating community growth. Users can also make projects public to share techniques and download libraries to share via Apache. Organization-level access enables collaboration on workflows.\nOverall, Edge Impulse is uniquely comprehensive and integrateable for developer workflows. Larger platforms like Google and Microsoft focus more on cloud versus embedded systems. TinyMLOps frameworks such as Neuton AI and Latent AI offer some functionality but lack Edge Impulse’s end-to-end capabilities. TensorFlow Lite Micro is the standard inference engine due to flexibility, open source status, and TensorFlow integration, but it uses more memory and storage than Edge Impulse’s EON Compiler. Other platforms need to be updated, academic-focused, or more versatile. In summary, Edge Impulse streamlines and scale embedded ML through an accessible, automated platform.\n\n\n\nLimitations\nWhile Edge Impulse provides an accessible pipeline for embedded ML, important limitations and risks remain. A key challenge is data quality and availability - the models are only as good as the data used to train them. Users must have sufficient labeled samples that capture the breadth of expected operating conditions and failure modes. Labeled anomalies and outliers are critical yet time-consuming to collect and identify. Insufficient or biased data leads to poor model performance regardless of the tool’s capabilities.\nDeploying low-powered devices also presents inherent challenges. Optimized models may still need to be more resource-intensive for ultra-low-power MCUs. Striking the right balance of compression versus accuracy takes some experimentation. The tool simplifies but still needs to eliminate the need for foundational ML and signal processing expertise. Embedded environments also constrain debugging and interpretability compared to the cloud.\nWhile impressive results are achievable, users shouldn’t view Edge Impulse as a “Push Button ML” solution. Careful project scoping, data collection, model evaluation, and testing are still essential. As with any development tool, reasonable expectations and diligence in application are advised. However, Edge Impulse can accelerate embedded ML prototyping and deployment for developers willing to invest the requisite data science and engineering effort.\n\n\n\n\n\n\nExercise 13.1: Edge Impulse\n\n\n\n\n\nReady to level up your tiny machine-learning projects? Let’s combine the power of Edge Impulse with the awesome visualizations of Weights & Biases (WandB). In this Colab, you’ll learn to track your model’s training progress like a pro! Imagine seeing cool graphs of your model getting smarter, comparing different versions, and ensuring your AI performs its best even on tiny devices.", "crumbs": [ "Deployment", "13  ML Operations" @@ -1445,7 +1445,7 @@ "href": "contents/ops/ops.html#case-studies", "title": "13  ML Operations", "section": "13.8 Case Studies", - "text": "13.8 Case Studies\n\n13.8.1 Oura Ring\nThe Oura Ring is a wearable that can measure activity, sleep, and recovery when placed on the user’s finger. Using sensors to track physiological metrics, the device uses embedded ML to predict the stages of sleep. To establish a baseline of legitimacy in the industry, Oura conducted a correlation experiment to evaluate the device’s success in predicting sleep stages against a baseline study. This resulted in a solid 62% correlation compared to the 82-83% baseline. Thus, the team set out to determine how to improve their performance even further.\nThe first challenge was to obtain better data in terms of both quantity and quality. They could host a larger study to get a more comprehensive data set, but the data would be so noisy and large that it would be difficult to aggregate, scrub, and analyze. This is where Edge Impulse comes in.\nWe hosted a massive sleep study of 100 men and women between the ages of 15 and 73 across three continents (Asia, Europe, and North America). In addition to wearing the Oura Ring, participants were responsible for undergoing the industry standard PSG testing, which provided a “label” for this data set. With 440 nights of sleep from 106 participants, the data set totaled 3,444 hours in length across Ring and PSG data. With Edge Impulse, Oura could easily upload and consolidate data from different sources into a private S3 bucket. They were also able to set up a Data Pipeline to merge data samples into individual files and preprocess the data without having to conduct manual scrubbing.\nBecause of the time saved on data processing thanks to Edge Impulse, the Oura team could focus on the key drivers of their prediction. They only extracted three types of sensor data: heart rate, motion, and body temperature. After partitioning the data using five-fold cross-validation and classifying sleep stages, the team achieved a correlation of 79% - just a few percentage points off the standard. They readily deployed two types of sleep detection models: one simplified using just the ring’s accelerometer and one more comprehensive leveraging Autonomic Nervous System (ANS)-mediated peripheral signals and circadian features. With Edge Impulse, they plan to conduct further analyses of different activity types and leverage the platform’s scalability to continue experimenting with different data sources and subsets of extracted features.\nWhile most ML research focuses on model-dominant steps such as training and finetuning, this case study underscores the importance of a holistic approach to ML Ops, where even the initial steps of data aggregation and preprocessing fundamentally impact successful outcomes.\n\n\n13.8.2 ClinAIOps\nLet’s look at MLOps in the context of medical health monitoring to better understand how MLOps “matures” in a real-world deployment. Specifically, let’s consider continuous therapeutic monitoring (CTM) enabled by wearable devices and sensors. CTM captures detailed physiological data from patients, providing the opportunity for more frequent and personalized adjustments to treatments.\nWearable ML-enabled sensors enable continuous physiological and activity monitoring outside clinics, opening up possibilities for timely, data-driven therapy adjustments. For example, wearable insulin biosensors (Psoma and Kanthou 2023) and wrist-worn ECG sensors for glucose monitoring (J. Li et al. 2021) can automate insulin dosing for diabetes, wrist-worn ECG and PPG sensors can adjust blood thinners based on atrial fibrillation patterns (Attia et al. 2018; Guo et al. 2019), and accelerometers tracking gait can trigger preventative care for declining mobility in the elderly (Liu et al. 2022). The variety of signals that can now be captured passively and continuously allows therapy titration and optimization tailored to each patient’s changing needs. By closing the loop between physiological sensing and therapeutic response with TinyML and on-device learning, wearables are poised to transform many areas of personalized medicine.\n\nPsoma, Sotiria D., and Chryso Kanthou. 2023. “Wearable Insulin Biosensors for Diabetes Management: Advances and Challenges.” Biosensors 13 (7): 719. https://doi.org/10.3390/bios13070719.\n\nLi, Jingzhen, Igbe Tobore, Yuhang Liu, Abhishek Kandwal, Lei Wang, and Zedong Nie. 2021. “Non-Invasive Monitoring of Three Glucose Ranges Based on ECG by Using DBSCAN-CNN.” IEEE Journal of Biomedical and Health Informatics 25 (9): 3340–50. https://doi.org/10.1109/jbhi.2021.3072628.\n\nAttia, Zachi I., Alan Sugrue, Samuel J. Asirvatham, Michael J. Ackerman, Suraj Kapa, Paul A. Friedman, and Peter A. Noseworthy. 2018. “Noninvasive Assessment of Dofetilide Plasma Concentration Using a Deep Learning (Neural Network) Analysis of the Surface Electrocardiogram: A Proof of Concept Study.” PLOS ONE 13 (8): e0201059. https://doi.org/10.1371/journal.pone.0201059.\n\nGuo, Yutao, Hao Wang, Hui Zhang, Tong Liu, Zhaoguang Liang, Yunlong Xia, Li Yan, et al. 2019. “Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation.” Journal of the American College of Cardiology 74 (19): 2365–75. https://doi.org/10.1016/j.jacc.2019.08.019.\n\nLiu, Yingcheng, Guo Zhang, Christopher G. Tarolli, Rumen Hristov, Stella Jensen-Roberts, Emma M. Waddell, Taylor L. Myers, et al. 2022. “Monitoring Gait at Home with Radio Waves in Parkinson’s Disease: A Marker of Severity, Progression, and Medication Response.” Science Translational Medicine 14 (663): eadc9669. https://doi.org/10.1126/scitranslmed.adc9669.\nML holds great promise in analyzing CTM data to provide data-driven recommendations for therapy adjustments. But simply deploying AI models in silos, without integrating them properly into clinical workflows and decision-making, can lead to poor adoption or suboptimal outcomes. In other words, thinking about MLOps alone is insufficient to make them useful in practice. This study shows that frameworks are needed to incorporate AI and CTM into real-world clinical practice seamlessly.\nThis case study analyzes “ClinAIOps” as a model for embedded ML operations in complex clinical environments (Chen et al. 2023). We provide an overview of the framework and why it’s needed, walk through an application example, and discuss key implementation challenges related to model monitoring, workflow integration, and stakeholder incentives. Analyzing real-world examples like ClinAIOps illuminates crucial principles and best practices for reliable and effective AI Ops across many domains.\nTraditional MLOps frameworks are insufficient for integrating continuous therapeutic monitoring (CTM) and AI in clinical settings for a few key reasons:\n\nMLOps focuses on the ML model lifecycle—training, deployment, monitoring. But healthcare involves coordinating multiple human stakeholders—patients and clinicians—not just models.\nMLOps aims to automate IT system monitoring and management. However, optimizing patient health requires personalized care and human oversight, not just automation.\nCTM and healthcare delivery are complex sociotechnical systems with many moving parts. MLOps doesn’t provide a framework for coordinating human and AI decision-making.\nEthical considerations regarding healthcare AI require human judgment, oversight, and accountability. MLOps frameworks lack processes for ethical oversight.\nPatient health data is highly sensitive and regulated. MLOps alone doesn’t ensure the handling of protected health information to privacy and regulatory standards.\nClinical validation of AI-guided treatment plans is essential for provider adoption. MLOps doesn’t incorporate domain-specific evaluation of model recommendations.\nOptimizing healthcare metrics like patient outcomes requires aligning stakeholder incentives and workflows, which pure tech-focused MLOps overlooks.\n\nThus, effectively integrating AI/ML and CTM in clinical practice requires more than just model and data pipelines; it requires coordinating complex human-AI collaborative decision-making, which ClinAIOps aims to address via its multi-stakeholder feedback loops.\n\nFeedback Loops\nThe ClinAIOps framework, shown in Figure 13.8, provides these mechanisms through three feedback loops. The loops are useful for coordinating the insights from continuous physiological monitoring, clinician expertise, and AI guidance via feedback loops, enabling data-driven precision medicine while maintaining human accountability. ClinAIOps provides a model for effective human-AI symbiosis in healthcare: the patient is at the center, providing health challenges and goals that inform the therapy regimen; the clinician oversees this regimen, giving inputs for adjustments based on continuous monitoring data and health reports from the patient; whereas AI developers play a crucial role by creating systems that generate alerts for therapy updates, which the clinician then vets.\nThese feedback loops, which we will discuss below, help maintain clinician responsibility and control over treatment plans by reviewing AI suggestions before they impact patients. They help dynamically customize AI model behavior and outputs to each patient’s changing health status. They help improve model accuracy and clinical utility over time by learning from clinician and patient responses. They facilitate shared decision-making and personalized care during patient-clinician interactions. They enable rapid optimization of therapies based on frequent patient data that clinicians cannot manually analyze.\n\n\n\n\n\n\nFigure 13.8: ClinAIOps cycle. Source: Chen et al. (2023).\n\n\n\n\nPatient-AI Loop\nThe patient-AI loop enables frequent therapy optimization driven by continuous physiological monitoring. Patients are prescribed wearables like smartwatches or skin patches to collect relevant health signals passively. For example, a diabetic patient could have a continuous glucose monitor, or a heart disease patient may wear an ECG patch. An AI model analyzes the patient’s longitudinal health data streams in the context of their electronic medical records - their diagnoses, lab tests, medications, and demographics. The AI model suggests adjustments to the treatment regimen tailored to that individual, like changing a medication dose or administration schedule. Minor adjustments within a pre-approved safe range can be made by the patient independently, while major changes are reviewed by the clinician first. This tight feedback between the patient’s physiology and AI-guided therapy allows data-driven, timely optimizations like automated insulin dosing recommendations based on real-time glucose levels for diabetes patients.\n\n\nClinician-AI Loop\nThe clinician-AI loop allows clinical oversight over AI-generated recommendations to ensure safety and accountability. The AI model provides the clinician with treatment recommendations and easily reviewed summaries of the relevant patient data on which the suggestions are based. For instance, an AI may suggest lowering a hypertension patient’s blood pressure medication dose based on continuously low readings. The clinician can accept, reject, or modify the AI’s proposed prescription changes. This clinician feedback further trains and improves the model. Additionally, the clinician sets the bounds for the types and extent of treatment changes the AI can autonomously recommend to patients. By reviewing AI suggestions, the clinician maintains ultimate treatment authority based on their clinical judgment and accountability. This loop allows them to oversee patient cases with AI assistance efficiently.\n\n\nPatient-Clinician Loop\nInstead of routine data collection, the clinician can focus on interpreting high-level data patterns and collaborating with the patient to set health goals and priorities. The AI assistance will also free up clinicians’ time, allowing them to focus more deeply on listening to patients’ stories and concerns. For instance, the clinician may discuss diet and exercise changes with a diabetes patient to improve their glucose control based on their continuous monitoring data. Appointment frequency can also be dynamically adjusted based on patient progress rather than following a fixed calendar. Freed from basic data gathering, the clinician can provide coaching and care customized to each patient informed by their continuous health data. The patient-clinician relationship is made more productive and personalized.\n\n\n\nHypertension Example\nLet’s consider an example. According to the Centers for Disease Control and Prevention, nearly half of adults have hypertension (48.1%, 119.9 million). Hypertension can be managed through ClinAIOps with the help of wearable sensors using the following approach:\n\nData Collection\nThe data collected would include continuous blood pressure monitoring using a wrist-worn device equipped with photoplethysmography (PPG) and electrocardiography (ECG) sensors to estimate blood pressure (Q. Zhang, Zhou, and Zeng 2017). The wearable would also track the patient’s physical activity via embedded accelerometers. The patient would log any antihypertensive medications they take, along with the time and dose. The patient’s demographic details and medical history from their electronic health record (EHR) would also be incorporated. This multimodal real-world data provides valuable context for the AI model to analyze the patient’s blood pressure patterns, activity levels, medication adherence, and responses to therapy.\n\nZhang, Qingxue, Dian Zhou, and Xuan Zeng. 2017. “Highly Wearable Cuff-Less Blood Pressure and Heart Rate Monitoring with Single-Arm Electrocardiogram and Photoplethysmogram Signals.” BioMedical Engineering OnLine 16 (1): 23. https://doi.org/10.1186/s12938-017-0317-z.\n\n\nAI Model\nThe on-device AI model would analyze the patient’s continuous blood pressure trends, circadian patterns, physical activity levels, medication adherence behaviors, and other contexts. It would use ML to predict optimal antihypertensive medication doses and timing to control the individual’s blood pressure. The model would send dosage change recommendations directly to the patient for minor adjustments or to the reviewing clinician for approval for more significant modifications. By observing clinician feedback on its recommendations and evaluating the resulting blood pressure outcomes in patients, the AI model could be continually retrained to improve performance. The goal is fully personalized blood pressure management optimized for each patient’s needs and responses.\n\n\nPatient-AI Loop\nIn the Patient-AI loop, the hypertensive patient would receive notifications on their wearable device or tethered smartphone app recommending adjustments to their antihypertensive medications. For minor dose changes within a pre-defined safe range, the patient could independently implement the AI model’s suggested adjustment to their regimen. However, the patient must obtain clinician approval before changing their dosage for more significant modifications. Providing personalized and timely medication recommendations automates an element of hypertension self-management for the patient. It can improve their adherence to the regimen as well as treatment outcomes. The patient is empowered to leverage AI insights to control their blood pressure better.\n\n\nClinician-AI Loop\nIn the Clinician-AI loop, the provider would receive summaries of the patient’s continuous blood pressure trends and visualizations of their medication-taking patterns and adherence. They review the AI model’s suggested antihypertensive dosage changes and decide whether to approve, reject, or modify the recommendations before they reach the patient. The clinician also specifies the boundaries for how much the AI can independently recommend changing dosages without clinician oversight. If the patient’s blood pressure is trending at dangerous levels, the system alerts the clinician so they can promptly intervene and adjust medications or request an emergency room visit. This loop maintains accountability and safety while allowing the clinician to harness AI insights by keeping the clinician in charge of approving major treatment changes.\n\n\nPatient-Clinician Loop\nIn the Patient-Clinician loop, shown in Figure 13.9, the in-person visits would focus less on collecting data or basic medication adjustments. Instead, the clinician could interpret high-level trends and patterns in the patient’s continuous monitoring data and have focused discussions about diet, exercise, stress management, and other lifestyle changes to improve their blood pressure control holistically. The frequency of appointments could be dynamically optimized based on the patient’s stability rather than following a fixed calendar. Since the clinician would not need to review all the granular data, they could concentrate on delivering personalized care and recommendations during visits. With continuous monitoring and AI-assisted optimization of medications between visits, the clinician-patient relationship focuses on overall wellness goals and becomes more impactful. This proactive and tailored data-driven approach can help avoid hypertension complications like stroke, heart failure, and other threats to patient health and well-being.\n\n\n\n\n\n\nFigure 13.9: ClinAIOps interactive loop. Source: Chen et al. (2023).\n\n\nChen, Emma, Shvetank Prakash, Vijay Janapa Reddi, David Kim, and Pranav Rajpurkar. 2023. “A Framework for Integrating Artificial Intelligence for Clinical Care with Continuous Therapeutic Monitoring.” Nature Biomedical Engineering, November. https://doi.org/10.1038/s41551-023-01115-0.\n\n\n\n\n\nMLOps vs. ClinAIOps\nThe hypertension example illustrates well why traditional MLOps are insufficient for many real-world AI applications and why frameworks like ClinAIOps are needed instead.\nWith hypertension, simply developing and deploying an ML model for adjusting medications would only succeed if it considered the broader clinical context. The patient, clinician, and health system have concerns about shaping adoption. The AI model cannot optimize blood pressure outcomes alone—it requires integrating with workflows, behaviors, and incentives.\n\nSome key gaps the example highlights in a pure MLOps approach:\nThe model itself would lack the real-world patient data at scale to recommend treatments reliably. ClinAIOps enables this by collecting feedback from clinicians and patients via continuous monitoring.\nClinicians would only trust model recommendations with transparency, explainability, and accountability. ClinAIOps keeps the clinician in the loop to build confidence.\nPatients need personalized coaching and motivation - not just AI notifications. The ClinAIOps patient-clinician loop facilitates this.\nSensor reliability and data accuracy would only be sufficient with clinical oversight. ClinAIOps validates recommendations.\nLiability for treatment outcomes must be clarified with just an ML model. ClinAIOps maintains human accountability.\nHealth systems would need to demonstrate value to change workflows. ClinAIOps aligns stakeholders.\n\nThe hypertension case clearly shows the need to look beyond training and deploying a performant ML model to consider the entire human-AI sociotechnical system. This is the key gap ClinAIOps aims to address over traditional MLOps. Traditional MLOps is overly tech-focused on automating ML model development and deployment, while ClinAIOps incorporates clinical context and human-AI coordination through multi-stakeholder feedback loops.\nTable 13.3 compares them. This table highlights how, when MLOps is implemented, we need to consider more than just ML models.\n\n\n\nTable 13.3: Comparison of MLOps versus AI operations for clinical use.\n\n\n\n\n\n\n\n\n\n\n\nTraditional MLOps\nClinAIOps\n\n\n\n\nFocus\nML model development and deployment\nCoordinating human and AI decision-making\n\n\nStakeholders\nData scientists, IT engineers\nPatients, clinicians, AI developers\n\n\nFeedback loops\nModel retraining, monitoring\nPatient-AI, clinician-AI, patient-clinician\n\n\nObjective\nOperationalize ML deployments\nOptimize patient health outcomes\n\n\nProcesses\nAutomated pipelines and infrastructure\nIntegrates clinical workflows and oversight\n\n\nData considerations\nBuilding training datasets\nPrivacy, ethics, protected health information\n\n\nModel validation\nTesting model performance metrics\nClinical evaluation of recommendations\n\n\nImplementation\nFocuses on technical integration\nAligns incentives of human stakeholders\n\n\n\n\n\n\n\n\nSummary\nIn complex domains like healthcare, successfully deploying AI requires moving beyond a narrow focus on training and deploying performant ML models. As illustrated through the hypertension example, real-world integration of AI necessitates coordinating diverse stakeholders, aligning incentives, validating recommendations, and maintaining accountability. Frameworks like ClinAIOps, which facilitate collaborative human-AI decision-making through integrated feedback loops, are needed to address these multifaceted challenges. Rather than just automating tasks, AI must augment human capabilities and clinical workflows. This allows AI to positively impact patient outcomes, population health, and healthcare efficiency.", + "text": "13.8 Case Studies\n\n13.8.1 Oura Ring\nThe Oura Ring is a wearable that can measure activity, sleep, and recovery when placed on the user’s finger. Using sensors to track physiological metrics, the device uses embedded ML to predict the stages of sleep. To establish a baseline of legitimacy in the industry, Oura conducted a correlation experiment to evaluate the device’s success in predicting sleep stages against a baseline study. This resulted in a solid 62% correlation compared to the 82-83% baseline. Thus, the team set out to determine how to improve their performance even further.\nThe first challenge was to obtain better data in terms of both quantity and quality. They could host a larger study to get a more comprehensive data set, but the data would be so noisy and large that it would be difficult to aggregate, scrub, and analyze. This is where Edge Impulse comes in.\nWe hosted a massive sleep study of 100 men and women between the ages of 15 and 73 across three continents (Asia, Europe, and North America). In addition to wearing the Oura Ring, participants were responsible for undergoing the industry standard PSG testing, which provided a “label” for this data set. With 440 nights of sleep from 106 participants, the data set totaled 3,444 hours in length across Ring and PSG data. With Edge Impulse, Oura could easily upload and consolidate data from different sources into a private S3 bucket. They were also able to set up a Data Pipeline to merge data samples into individual files and preprocess the data without having to conduct manual scrubbing.\nBecause of the time saved on data processing thanks to Edge Impulse, the Oura team could focus on the key drivers of their prediction. They only extracted three types of sensor data: heart rate, motion, and body temperature. After partitioning the data using five-fold cross-validation and classifying sleep stages, the team achieved a correlation of 79% - just a few percentage points off the standard. They readily deployed two types of sleep detection models: one simplified using just the ring’s accelerometer and one more comprehensive leveraging Autonomic Nervous System (ANS)-mediated peripheral signals and circadian features. With Edge Impulse, they plan to conduct further analyses of different activity types and leverage the platform’s scalability to continue experimenting with different data sources and subsets of extracted features.\nWhile most ML research focuses on model-dominant steps such as training and finetuning, this case study underscores the importance of a holistic approach to ML Ops, where even the initial steps of data aggregation and preprocessing fundamentally impact successful outcomes.\n\n\n13.8.2 ClinAIOps\nLet’s look at MLOps in the context of medical health monitoring to better understand how MLOps “matures” in a real-world deployment. Specifically, let’s consider continuous therapeutic monitoring (CTM) enabled by wearable devices and sensors. CTM captures detailed physiological data from patients, providing the opportunity for more frequent and personalized adjustments to treatments.\nWearable ML-enabled sensors enable continuous physiological and activity monitoring outside clinics, opening up possibilities for timely, data-driven therapy adjustments. For example, wearable insulin biosensors (Psoma and Kanthou 2023) and wrist-worn ECG sensors for glucose monitoring (J. Li et al. 2021) can automate insulin dosing for diabetes, wrist-worn ECG and PPG sensors can adjust blood thinners based on atrial fibrillation patterns (Attia et al. 2018; Guo et al. 2019), and accelerometers tracking gait can trigger preventative care for declining mobility in the elderly (Liu et al. 2022). The variety of signals that can now be captured passively and continuously allows therapy titration and optimization tailored to each patient’s changing needs. By closing the loop between physiological sensing and therapeutic response with TinyML and on-device learning, wearables are poised to transform many areas of personalized medicine.\n\nPsoma, Sotiria D., and Chryso Kanthou. 2023. “Wearable Insulin Biosensors for Diabetes Management: Advances and Challenges.” Biosensors 13 (7): 719. https://doi.org/10.3390/bios13070719.\n\nLi, Jingzhen, Igbe Tobore, Yuhang Liu, Abhishek Kandwal, Lei Wang, and Zedong Nie. 2021. “Non-Invasive Monitoring of Three Glucose Ranges Based on ECG by Using DBSCAN-CNN.” IEEE Journal of Biomedical and Health Informatics 25 (9): 3340–50. https://doi.org/10.1109/jbhi.2021.3072628.\n\nAttia, Zachi I., Alan Sugrue, Samuel J. Asirvatham, Michael J. Ackerman, Suraj Kapa, Paul A. Friedman, and Peter A. Noseworthy. 2018. “Noninvasive Assessment of Dofetilide Plasma Concentration Using a Deep Learning (Neural Network) Analysis of the Surface Electrocardiogram: A Proof of Concept Study.” PLOS ONE 13 (8): e0201059. https://doi.org/10.1371/journal.pone.0201059.\n\nGuo, Yutao, Hao Wang, Hui Zhang, Tong Liu, Zhaoguang Liang, Yunlong Xia, Li Yan, et al. 2019. “Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation.” Journal of the American College of Cardiology 74 (19): 2365–75. https://doi.org/10.1016/j.jacc.2019.08.019.\n\nLiu, Yingcheng, Guo Zhang, Christopher G. Tarolli, Rumen Hristov, Stella Jensen-Roberts, Emma M. Waddell, Taylor L. Myers, et al. 2022. “Monitoring Gait at Home with Radio Waves in Parkinson’s Disease: A Marker of Severity, Progression, and Medication Response.” Science Translational Medicine 14 (663): eadc9669. https://doi.org/10.1126/scitranslmed.adc9669.\nML holds great promise in analyzing CTM data to provide data-driven recommendations for therapy adjustments. But simply deploying AI models in silos, without integrating them properly into clinical workflows and decision-making, can lead to poor adoption or suboptimal outcomes. In other words, thinking about MLOps alone is insufficient to make them useful in practice. This study shows that frameworks are needed to incorporate AI and CTM into real-world clinical practice seamlessly.\nThis case study analyzes “ClinAIOps” as a model for embedded ML operations in complex clinical environments (Chen et al. 2023). We provide an overview of the framework and why it’s needed, walk through an application example, and discuss key implementation challenges related to model monitoring, workflow integration, and stakeholder incentives. Analyzing real-world examples like ClinAIOps illuminates crucial principles and best practices for reliable and effective AI Ops across many domains.\nTraditional MLOps frameworks are insufficient for integrating continuous therapeutic monitoring (CTM) and AI in clinical settings for a few key reasons:\n\nMLOps focuses on the ML model lifecycle—training, deployment, monitoring. But healthcare involves coordinating multiple human stakeholders—patients and clinicians—not just models.\nMLOps automates IT system monitoring and management. However, optimizing patient health requires personalized care and human oversight, not just automation.\nCTM and healthcare delivery are complex sociotechnical systems with many moving parts. MLOps doesn’t provide a framework for coordinating human and AI decision-making.\nEthical considerations regarding healthcare AI require human judgment, oversight, and accountability. MLOps frameworks lack processes for ethical oversight.\nPatient health data is highly sensitive and regulated. MLOps alone doesn’t ensure the handling of protected health information to privacy and regulatory standards.\nClinical validation of AI-guided treatment plans is essential for provider adoption. MLOps doesn’t incorporate domain-specific evaluation of model recommendations.\nOptimizing healthcare metrics like patient outcomes requires aligning stakeholder incentives and workflows, which pure tech-focused MLOps overlooks.\n\nThus, effectively integrating AI/ML and CTM in clinical practice requires more than just model and data pipelines; it requires coordinating complex human-AI collaborative decision-making, which ClinAIOps addresses via its multi-stakeholder feedback loops.\n\nFeedback Loops\nThe ClinAIOps framework, shown in Figure 13.8, provides these mechanisms through three feedback loops. The loops are useful for coordinating the insights from continuous physiological monitoring, clinician expertise, and AI guidance via feedback loops, enabling data-driven precision medicine while maintaining human accountability. ClinAIOps provides a model for effective human-AI symbiosis in healthcare: the patient is at the center, providing health challenges and goals that inform the therapy regimen; the clinician oversees this regimen, giving inputs for adjustments based on continuous monitoring data and health reports from the patient; whereas AI developers play a crucial role by creating systems that generate alerts for therapy updates, which the clinician then vets.\nThese feedback loops, which we will discuss below, help maintain clinician responsibility and control over treatment plans by reviewing AI suggestions before they impact patients. They help dynamically customize AI model behavior and outputs to each patient’s changing health status. They help improve model accuracy and clinical utility over time by learning from clinician and patient responses. They facilitate shared decision-making and personalized care during patient-clinician interactions. They enable rapid optimization of therapies based on frequent patient data that clinicians cannot manually analyze.\n\n\n\n\n\n\nFigure 13.8: ClinAIOps cycle. Source: Chen et al. (2023).\n\n\n\n\nPatient-AI Loop\nThe patient-AI loop enables frequent therapy optimization driven by continuous physiological monitoring. Patients are prescribed wearables like smartwatches or skin patches to collect relevant health signals passively. For example, a diabetic patient could have a continuous glucose monitor, or a heart disease patient may wear an ECG patch. An AI model analyzes the patient’s longitudinal health data streams in the context of their electronic medical records - their diagnoses, lab tests, medications, and demographics. The AI model suggests adjustments to the treatment regimen tailored to that individual, like changing a medication dose or administration schedule. Minor adjustments within a pre-approved safe range can be made by the patient independently, while major changes are reviewed by the clinician first. This tight feedback between the patient’s physiology and AI-guided therapy allows data-driven, timely optimizations like automated insulin dosing recommendations based on real-time glucose levels for diabetes patients.\n\n\nClinician-AI Loop\nThe clinician-AI loop allows clinical oversight over AI-generated recommendations to ensure safety and accountability. The AI model provides the clinician with treatment recommendations and easily reviewed summaries of the relevant patient data on which the suggestions are based. For instance, an AI may suggest lowering a hypertension patient’s blood pressure medication dose based on continuously low readings. The clinician can accept, reject, or modify the AI’s proposed prescription changes. This clinician feedback further trains and improves the model. Additionally, the clinician sets the bounds for the types and extent of treatment changes the AI can autonomously recommend to patients. By reviewing AI suggestions, the clinician maintains ultimate treatment authority based on their clinical judgment and accountability. This loop allows them to oversee patient cases with AI assistance efficiently.\n\n\nPatient-Clinician Loop\nInstead of routine data collection, the clinician can focus on interpreting high-level data patterns and collaborating with the patient to set health goals and priorities. The AI assistance will also free up clinicians’ time, allowing them to focus more deeply on listening to patients’ stories and concerns. For instance, the clinician may discuss diet and exercise changes with a diabetes patient to improve their glucose control based on their continuous monitoring data. Appointment frequency can also be dynamically adjusted based on patient progress rather than following a fixed calendar. Freed from basic data gathering, the clinician can provide coaching and care customized to each patient informed by their continuous health data. The patient-clinician relationship is made more productive and personalized.\n\n\n\nHypertension Example\nLet’s consider an example. According to the Centers for Disease Control and Prevention, nearly half of adults have hypertension (48.1%, 119.9 million). Hypertension can be managed through ClinAIOps with the help of wearable sensors using the following approach:\n\nData Collection\nThe data collected would include continuous blood pressure monitoring using a wrist-worn device equipped with photoplethysmography (PPG) and electrocardiography (ECG) sensors to estimate blood pressure (Q. Zhang, Zhou, and Zeng 2017). The wearable would also track the patient’s physical activity via embedded accelerometers. The patient would log any antihypertensive medications they take, along with the time and dose. The patient’s demographic details and medical history from their electronic health record (EHR) would also be incorporated. This multimodal real-world data provides valuable context for the AI model to analyze the patient’s blood pressure patterns, activity levels, medication adherence, and responses to therapy.\n\nZhang, Qingxue, Dian Zhou, and Xuan Zeng. 2017. “Highly Wearable Cuff-Less Blood Pressure and Heart Rate Monitoring with Single-Arm Electrocardiogram and Photoplethysmogram Signals.” BioMedical Engineering OnLine 16 (1): 23. https://doi.org/10.1186/s12938-017-0317-z.\n\n\nAI Model\nThe on-device AI model would analyze the patient’s continuous blood pressure trends, circadian patterns, physical activity levels, medication adherence behaviors, and other contexts. It would use ML to predict optimal antihypertensive medication doses and timing to control the individual’s blood pressure. The model would send dosage change recommendations directly to the patient for minor adjustments or to the reviewing clinician for approval for more significant modifications. By observing clinician feedback on its recommendations and evaluating the resulting blood pressure outcomes in patients, the AI model could be continually retrained to improve performance. The goal is fully personalized blood pressure management optimized for each patient’s needs and responses.\n\n\nPatient-AI Loop\nIn the Patient-AI loop, the hypertensive patient would receive notifications on their wearable device or tethered smartphone app recommending adjustments to their antihypertensive medications. For minor dose changes within a pre-defined safe range, the patient could independently implement the AI model’s suggested adjustment to their regimen. However, the patient must obtain clinician approval before changing their dosage for more significant modifications. Providing personalized and timely medication recommendations automates an element of hypertension self-management for the patient. It can improve their adherence to the regimen as well as treatment outcomes. The patient is empowered to leverage AI insights to control their blood pressure better.\n\n\nClinician-AI Loop\nIn the Clinician-AI loop, the provider would receive summaries of the patient’s continuous blood pressure trends and visualizations of their medication-taking patterns and adherence. They review the AI model’s suggested antihypertensive dosage changes and decide whether to approve, reject, or modify the recommendations before they reach the patient. The clinician also specifies the boundaries for how much the AI can independently recommend changing dosages without clinician oversight. If the patient’s blood pressure is trending at dangerous levels, the system alerts the clinician so they can promptly intervene and adjust medications or request an emergency room visit. This loop maintains accountability and safety while allowing the clinician to harness AI insights by keeping the clinician in charge of approving major treatment changes.\n\n\nPatient-Clinician Loop\nIn the Patient-Clinician loop, shown in Figure 13.9, the in-person visits would focus less on collecting data or basic medication adjustments. Instead, the clinician could interpret high-level trends and patterns in the patient’s continuous monitoring data and have focused discussions about diet, exercise, stress management, and other lifestyle changes to improve their blood pressure control holistically. The frequency of appointments could be dynamically optimized based on the patient’s stability rather than following a fixed calendar. Since the clinician would not need to review all the granular data, they could concentrate on delivering personalized care and recommendations during visits. With continuous monitoring and AI-assisted optimization of medications between visits, the clinician-patient relationship focuses on overall wellness goals and becomes more impactful. This proactive and tailored data-driven approach can help avoid hypertension complications like stroke, heart failure, and other threats to patient health and well-being.\n\n\n\n\n\n\nFigure 13.9: ClinAIOps interactive loop. Source: Chen et al. (2023).\n\n\nChen, Emma, Shvetank Prakash, Vijay Janapa Reddi, David Kim, and Pranav Rajpurkar. 2023. “A Framework for Integrating Artificial Intelligence for Clinical Care with Continuous Therapeutic Monitoring.” Nature Biomedical Engineering, November. https://doi.org/10.1038/s41551-023-01115-0.\n\n\n\n\n\nMLOps vs. ClinAIOps\nThe hypertension example illustrates well why traditional MLOps are insufficient for many real-world AI applications and why frameworks like ClinAIOps are needed instead.\nWith hypertension, simply developing and deploying an ML model for adjusting medications would only succeed if it considered the broader clinical context. The patient, clinician, and health system have concerns about shaping adoption. The AI model cannot optimize blood pressure outcomes alone—it requires integrating with workflows, behaviors, and incentives.\n\nSome key gaps the example highlights in a pure MLOps approach:\nThe model itself would lack the real-world patient data at scale to recommend treatments reliably. ClinAIOps enables this by collecting feedback from clinicians and patients via continuous monitoring.\nClinicians would only trust model recommendations with transparency, explainability, and accountability. ClinAIOps keeps the clinician in the loop to build confidence.\nPatients need personalized coaching and motivation - not just AI notifications. The ClinAIOps patient-clinician loop facilitates this.\nSensor reliability and data accuracy would only be sufficient with clinical oversight. ClinAIOps validates recommendations.\nLiability for treatment outcomes must be clarified with just an ML model. ClinAIOps maintains human accountability.\nHealth systems would need to demonstrate value to change workflows. ClinAIOps aligns stakeholders.\n\nThe hypertension case clearly shows the need to look beyond training and deploying a performant ML model to consider the entire human-AI sociotechnical system. This is the key gap ClinAIOps addresses over traditional MLOps. Traditional MLOps is overly tech-focused on automating ML model development and deployment, while ClinAIOps incorporates clinical context and human-AI coordination through multi-stakeholder feedback loops.\nTable 13.3 compares them. This table highlights how, when MLOps is implemented, we need to consider more than just ML models.\n\n\n\nTable 13.3: Comparison of MLOps versus AI operations for clinical use.\n\n\n\n\n\n\n\n\n\n\n\nTraditional MLOps\nClinAIOps\n\n\n\n\nFocus\nML model development and deployment\nCoordinating human and AI decision-making\n\n\nStakeholders\nData scientists, IT engineers\nPatients, clinicians, AI developers\n\n\nFeedback loops\nModel retraining, monitoring\nPatient-AI, clinician-AI, patient-clinician\n\n\nObjective\nOperationalize ML deployments\nOptimize patient health outcomes\n\n\nProcesses\nAutomated pipelines and infrastructure\nIntegrates clinical workflows and oversight\n\n\nData considerations\nBuilding training datasets\nPrivacy, ethics, protected health information\n\n\nModel validation\nTesting model performance metrics\nClinical evaluation of recommendations\n\n\nImplementation\nFocuses on technical integration\nAligns incentives of human stakeholders\n\n\n\n\n\n\n\n\nSummary\nIn complex domains like healthcare, successfully deploying AI requires moving beyond a narrow focus on training and deploying performant ML models. As illustrated through the hypertension example, real-world integration of AI necessitates coordinating diverse stakeholders, aligning incentives, validating recommendations, and maintaining accountability. Frameworks like ClinAIOps, which facilitate collaborative human-AI decision-making through integrated feedback loops, are needed to address these multifaceted challenges. Rather than just automating tasks, AI must augment human capabilities and clinical workflows. This allows AI to positively impact patient outcomes, population health, and healthcare efficiency.", "crumbs": [ "Deployment", "13  ML Operations" @@ -1522,7 +1522,7 @@ "href": "contents/privacy_security/privacy_security.html#security-threats-to-ml-models", "title": "14  Security & Privacy", "section": "14.4 Security Threats to ML Models", - "text": "14.4 Security Threats to ML Models\nML models face security risks that can undermine their integrity, performance, and trustworthiness if not adequately addressed. While there are several different threats, the primary threats include: Model theft, where adversaries steal the proprietary model parameters and the sensitive data they contain. Data poisoning, which compromises models through data tampering. Adversarial attacks deceive the model to make incorrect or unwanted predictions.\n\n14.4.1 Model Theft\nModel theft occurs when an attacker gains unauthorized access to a deployed ML model. The concern here is the theft of the model’s structure and trained parameters and the proprietary data it contains (Ateniese et al. 2015). Model theft is a real and growing threat, as demonstrated by cases like ex-Google engineer Anthony Levandowski, who allegedly stole Waymo’s self-driving car designs and started a competing company. Beyond economic impacts, model theft can seriously undermine privacy and enable further attacks.\n\nAteniese, Giuseppe, Luigi V. Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, and Giovanni Felici. 2015. “Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers.” Int. J. Secur. Netw. 10 (3): 137. https://doi.org/10.1504/ijsn.2015.071829.\nFor instance, consider an ML model developed for personalized recommendations in an e-commerce application. If a competitor steals this model, they gain insights into business analytics, customer preferences, and even trade secrets embedded within the model’s data. Attackers could leverage stolen models to craft more effective inputs for model inversion attacks, deducing private details about the model’s training data. A cloned e-commerce recommendation model could reveal customer purchase behaviors and demographics.\nTo understand model inversion attacks, consider a facial recognition system used to grant access to secured facilities. The system is trained on a dataset of employee photos. An attacker could infer features of the original dataset by observing the model’s output to various inputs. For example, suppose the model’s confidence level for a particular face is significantly higher for a given set of features. In that case, an attacker might deduce that someone with those features is likely in the training dataset.\nThe methodology of model inversion typically involves the following steps:\n\nAccessing Model Outputs: The attacker queries the ML model with input data and observes the outputs. This is often done through a legitimate interface, like a public API.\nAnalyzing Confidence Scores: For each input, the model provides a confidence score that reflects how similar the input is to the training data.\nReverse-Engineering: By analyzing the confidence scores or output probabilities, attackers can use optimization techniques to reconstruct what they believe is close to the original input data.\n\nOne historical example of such a vulnerability being explored was the research on inversion attacks against the U.S. Netflix Prize dataset, where researchers demonstrated that it was possible to learn about an individual’s movie preferences, which could lead to privacy breaches (Narayanan and Shmatikov 2006).\n\nNarayanan, Arvind, and Vitaly Shmatikov. 2006. “How to Break Anonymity of the Netflix Prize Dataset.” arXiv Preprint Cs/0610105.\nModel theft implies that it could lead to economic losses, undermine competitive advantage, and violate user privacy. There’s also the risk of model inversion attacks, where an adversary could input various data into the stolen model to infer sensitive information about the training data.\nBased on the desired asset, model theft attacks can be divided into two categories: exact model properties and approximate model behavior.\n\nStealing Exact Model Properties\nIn these attacks, the objective is to extract information about concrete metrics, such as a network’s learned parameters, fine-tuned hyperparameters, and the model’s internal layer architecture (Oliynyk, Mayer, and Rauber 2023).\n\nLearned Parameters: Adversaries aim to steal a model’s learned knowledge (weights and biases) to replicate it. Parameter theft is generally used with other attacks, such as architecture theft, which lacks parameter knowledge.\nFine-Tuned Hyperparameters: Training is costly, and identifying the optimal configuration of hyperparameters (such as learning rate and regularization) can be time-consuming and resource-intensive. Consequently, stealing a model’s optimized hyperparameters enables adversaries to replicate the model without incurring the exact development costs.\nModel Architecture: This attack concerns the specific design and structure of the model, such as layers, neurons, and connectivity patterns. Beyond reducing associated training costs, this theft poses a severe risk to intellectual property, potentially undermining a company’s competitive advantage. Architecture theft can be achieved by exploiting side-channel attacks (discussed later).\n\n\n\nStealing Approximate Model Behavior\nInstead of extracting exact numerical values of the model’s parameters, these attacks aim to reproduce the model’s behavior (predictions and effectiveness), decision-making, and high-level characteristics (Oliynyk, Mayer, and Rauber 2023). These techniques aim to achieve similar outcomes while allowing for internal deviations in parameters and architecture. Types of approximate behavior theft include gaining the same level of effectiveness and obtaining prediction consistency.\n\nOliynyk, Daryna, Rudolf Mayer, and Andreas Rauber. 2023. “I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences.” ACM Comput. Surv. 55 (14s): 1–41. https://doi.org/10.1145/3595292.\n\nLevel of Effectiveness: Attackers aim to replicate the model’s decision-making capabilities rather than focus on the precise parameter values. This is done through understanding the overall behavior of the model. Consider a scenario where an attacker wants to copy the behavior of an image classification model. By analyzing the model’s decision boundaries, the attack tunes its model to reach an effectiveness comparable to the original model. This could entail analyzing 1) the confusion matrix to understand the balance of prediction metrics (true positive, true negative, false positive, false negative) and 2) other performance metrics, such as F1 score and precision, to ensure that the two models are comparable.\nPrediction Consistency: The attacker tries to align their model’s prediction patterns with the target model’s. This involves matching prediction outputs (both positive and negative) on the same set of inputs and ensuring distributional consistency across different classes. For instance, consider a natural language processing (NLP) model that generates sentiment analysis for move reviews (labels reviews as positive, neutral, or negative). The attacker will try to fine-tune their model to match the prediction of the original models on the same set of movie reviews. This includes ensuring that the model makes the same mistakes (mispredictions) that the targeted model makes.\n\n\n\nCase Study\nIn 2018, Tesla filed a lawsuit against self-driving car startup Zoox, alleging former employees stole confidential data and trade secrets related to Tesla’s autonomous driving assistance system.\nTesla claimed that several of its former employees took over 10 G.B. of proprietary data, including ML models and source code, before joining Zoox. This allegedly included one of Tesla’s crucial image recognition models for identifying objects.\nThe theft of this sensitive proprietary model could help Zoox shortcut years of ML development and duplicate Tesla’s capabilities. Tesla argued this theft of I.P. caused significant financial and competitive harm. There were also concerns it could allow model inversion attacks to infer private details about Tesla’s testing data.\nThe Zoox employees denied stealing any proprietary information. However, the case highlights the significant risks of model theft—enabling the cloning of commercial models, causing economic impacts, and opening the door for further data privacy violations.\n\n\n\n14.4.2 Data Poisoning\nData poisoning is an attack where the training data is tampered with, leading to a compromised model (Biggio, Nelson, and Laskov 2012). Attackers can modify existing training examples, insert new malicious data points, or influence the data collection process. The poisoned data is labeled in such a way as to skew the model’s learned behavior. This can be particularly damaging in applications where ML models make automated decisions based on learned patterns. Beyond training sets, poisoning tests and validation data can allow adversaries to boost reported model performance artificially.\n\nBiggio, Battista, Blaine Nelson, and Pavel Laskov. 2012. “Poisoning Attacks Against Support Vector Machines.” In Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012. icml.cc / Omnipress. http://icml.cc/2012/papers/880.pdf.\nThe process usually involves the following steps:\n\nInjection: The attacker adds incorrect or misleading examples into the training set. These examples are often designed to look normal to cursory inspection but have been carefully crafted to disrupt the learning process.\nTraining: The ML model trains on this manipulated dataset and develops skewed understandings of the data patterns.\nDeployment: Once the model is deployed, the corrupted training leads to flawed decision-making or predictable vulnerabilities the attacker can exploit.\n\nThe impacts of data poisoning extend beyond just classification errors or accuracy drops. For instance, if incorrect or malicious data is introduced into a traffic sign recognition system’s training set, the model may learn to misclassify stop signs as yield signs, which can have dangerous real-world consequences, especially in embedded autonomous systems like autonomous vehicles.\nData poisoning can degrade a model’s accuracy, force it to make incorrect predictions or cause it to behave unpredictably. In critical applications like healthcare, such alterations can lead to significant trust and safety issues.\nThere are six main categories of data poisoning (Oprea, Singhal, and Vassilev 2022):\n\nOprea, Alina, Anoop Singhal, and Apostol Vassilev. 2022. “Poisoning Attacks Against Machine Learning: Can Machine Learning Be Trustworthy?” Computer 55 (11): 94–99. https://doi.org/10.1109/mc.2022.3190787.\n\nAvailability Attacks: These attacks seek to compromise a model’s overall functionality. They cause it to misclassify most testing samples, rendering the model unusable for practical applications. An example is label flipping, where labels of a specific, targeted class are replaced with labels from a different one.\nTargeted Attacks: Unlike availability attacks, targeted attacks aim to compromise a small number of the testing samples. So, the effect is localized to a limited number of classes, while the model maintains the same original level of accuracy on most of the classes. The targeted nature of the attack requires the attacker to possess knowledge of the model’s classes, making detecting these attacks more challenging.\nBackdoor Attacks: In these attacks, an adversary targets specific patterns in the data. The attacker introduces a backdoor (a malicious, hidden trigger or pattern) into the training data, such as altering certain features in structured data or a pattern of pixels at a fixed position. This causes the model to associate the malicious pattern with specific labels. As a result, when the model encounters test samples that contain a malicious pattern, it makes false predictions, highlighting the importance of caution and prevention in the role of data security professionals.\nSubpopulation Attacks: Attackers selectively choose to compromise a subset of the testing samples while maintaining accuracy on the rest of the samples. You can think of these attacks as a combination of availability and targeted attacks: performing availability attacks (performance degradation) within the scope of a targeted subset. Although subpopulation attacks may seem very similar to targeted attacks, the two have clear differences:\nScope: While targeted attacks target a selected set of samples, subpopulation attacks target a general subpopulation with similar feature representations. For example, in a targeted attack, an actor inserts manipulated images of a ‘speed bump’ warning sign (with carefully crafted perturbation or patterns), which causes an autonomous car to fail to recognize such a sign and slow down. On the other hand, manipulating all samples of people with a British accent so that a speech recognition model would misclassify a British person’s speech is an example of a subpopulation attack.\nKnowledge: While targeted attacks require a high degree of familiarity with the data, subpopulation attacks require less intimate knowledge to be effective.\n\n\nCase Study 1\nIn 2017, researchers demonstrated a data poisoning attack against a popular toxicity classification model called Perspective (Hosseini et al. 2017). This ML model detects toxic comments online.\n\nHosseini, Hossein, Sreeram Kannan, Baosen Zhang, and Radha Poovendran. 2017. “Deceiving Google’s Perspective Api Built for Detecting Toxic Comments.” ArXiv Preprint abs/1702.08138. https://arxiv.org/abs/1702.08138.\nThe researchers added synthetically generated toxic comments with slight misspellings and grammatical errors to the model’s training data. This slowly corrupted the model, causing it to misclassify increasing numbers of severely toxic inputs as non-toxic over time.\nAfter retraining on the poisoned data, the model’s false negative rate increased from 1.4% to 27% - allowing extremely toxic comments to bypass detection. The researchers warned this stealthy data poisoning could enable the spread of hate speech, harassment, and abuse if deployed against real moderation systems.\nThis case highlights how data poisoning can degrade model accuracy and reliability. For social media platforms, a poisoning attack that impairs toxicity detection could lead to the proliferation of harmful content and distrust of ML moderation systems. The example demonstrates why securing training data integrity and monitoring for poisoning is critical across application domains.\n\n\nCase Study 2\nInterestingly enough, data poisoning attacks are not always malicious (Shan et al. 2023). Nightshade, a tool developed by a team led by Professor Ben Zhao at the University of Chicago, utilizes data poisoning to help artists protect their art against scraping and copyright violations by generative A.I. models. Artists can use the tool to modify their images subtly before uploading them online.\nWhile these changes are imperceptible to the human eye, they can significantly degrade the performance of generative AI models when integrated into the training data. Generative models can be manipulated to produce unrealistic or nonsensical outputs. For example, with just 300 corrupted images, the University of Chicago researchers could deceive the latest Stable Diffusion model into generating images of canines resembling felines or bovines when prompted for automobiles.\nAs the quantity of corrupted images online grows, the efficacy of models trained on scraped data will decline exponentially. Initially, identifying corrupted data is challenging and necessitates manual intervention. Subsequently, contamination spreads rapidly to related concepts as generative models establish connections between words and their visual representations. Consequently, a corrupted image of a “car” could propagate into generated images linked to terms such as “truck,” “train,” and “bus.”\nOn the other hand, this tool can be used maliciously and affect legitimate generative model applications. This shows the very challenging and novel nature of machine learning attacks.\nFigure 17.26 demonstrates the effects of different levels of data poisoning (50 samples, 100 samples, and 300 samples of poisoned images) on generating images in various categories. Notice how the images start deforming and deviating from the desired category. For example, after 300 poison samples, a car prompt generates a cow.\n\n\n\n\n\n\nFigure 14.1: Data poisoning. Source: Shan et al. (2023).\n\n\nShan, Shawn, Wenxin Ding, Josephine Passananti, Haitao Zheng, and Ben Y Zhao. 2023. “Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models.” ArXiv Preprint abs/2310.13828. https://arxiv.org/abs/2310.13828.\n\n\n\n\n\n14.4.3 Adversarial Attacks\nAdversarial attacks aim to trick models into making incorrect predictions by providing them with specially crafted, deceptive inputs (called adversarial examples) (Parrish et al. 2023). By adding slight perturbations to input data, adversaries can “hack” a model’s pattern recognition and deceive it. These are sophisticated techniques where slight, often imperceptible alterations to input data can trick an ML model into making a wrong prediction.\n\nParrish, Alicia, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Max Bartolo, Oana Inel, Juan Ciro, et al. 2023. “Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models.” ArXiv Preprint abs/2305.14384. https://arxiv.org/abs/2305.14384.\n\nRamesh, Aditya, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. “Zero-Shot Text-to-Image Generation.” In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, edited by Marina Meila and Tong Zhang, 139:8821–31. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v139/ramesh21a.html.\n\nRombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer. 2022. “High-Resolution Image Synthesis with Latent Diffusion Models.” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. https://doi.org/10.1109/cvpr52688.2022.01042.\nOne can generate prompts that lead to unsafe images in text-to-image models like DALLE (Ramesh et al. 2021) or Stable Diffusion (Rombach et al. 2022). For example, by altering the pixel values of an image, attackers can deceive a facial recognition system into identifying a face as a different person.\nAdversarial attacks exploit the way ML models learn and make decisions during inference. These models work on the principle of recognizing patterns in data. An adversary crafts malicious inputs with perturbations to mislead the model’s pattern recognition—essentially ‘hacking’ the model’s perceptions.\nAdversarial attacks fall under different scenarios:\n\nWhitebox Attacks: The attacker has comprehensive knowledge of the target model’s internal workings, including the training data, parameters, and architecture. This extensive access facilitates the exploitation of the model’s vulnerabilities. The attacker can leverage specific and subtle weaknesses to construct highly effective adversarial examples.\nBlackbox Attacks: In contrast to whitebox attacks, in blackbox attacks, the attacker has little to no knowledge of the target model. The adversarial actor must carefully observe the model’s output behavior to carry out the attack.\nGreybox Attacks: These attacks occupy a spectrum between black-box and white-box attacks. The adversary possesses partial knowledge of the target model’s internal structure. For instance, the attacker might know the training data but lack information about the model’s architecture or parameters. In practical scenarios, most attacks fall within this grey area.\n\nThe landscape of machine learning models is complex and broad, especially given their relatively recent integration into commercial applications. This rapid adoption, while transformative, has brought to light numerous vulnerabilities within these models. Consequently, various adversarial attack methods have emerged, each strategically exploiting different aspects of different models. Below, we highlight a subset of these methods, showcasing the multifaceted nature of adversarial attacks on machine learning models:\n\nGenerative Adversarial Networks (GANs) are deep learning models consisting of two networks competing against each other: a generator and a discriminator (Goodfellow et al. 2020). The generator tries to synthesize realistic data while the discriminator evaluates whether they are real or fake. GANs can be used to craft adversarial examples. The generator network is trained to produce inputs that the target model misclassifies. These GAN-generated images can then attack a target classifier or detection model. The generator and the target model are engaged in a competitive process, with the generator continually improving its ability to create deceptive examples and the target model enhancing its resistance to such examples. GANs provide a robust framework for crafting complex and diverse adversarial inputs, illustrating the adaptability of generative models in the adversarial landscape.\nTransfer Learning Adversarial Attacks exploit the knowledge transferred from a pre-trained model to a target model, creating adversarial examples that can deceive both models. These attacks pose a growing concern, particularly when adversaries have knowledge of the feature extractor but lack access to the classification head (the part or layer responsible for making the final classifications). Referred to as “headless attacks,” these transferable adversarial strategies leverage the expressive capabilities of feature extractors to craft perturbations while oblivious to the label space or training data. The existence of such attacks underscores the importance of developing robust defenses for transfer learning applications, especially since pre-trained models are commonly used (Abdelkader et al. 2020).\n\n\nGoodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. “Generative Adversarial Networks.” Commun. ACM 63 (11): 139–44. https://doi.org/10.1145/3422622.\n\nAbdelkader, Ahmed, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, and Chen Zhu. 2020. “Headless Horseman: Adversarial Attacks on Transfer Learning Models.” In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3087–91. IEEE. https://doi.org/10.1109/icassp40776.2020.9053181.\n\nCase Study\nIn 2017, researchers conducted experiments by placing small black and white stickers on stop signs (Eykholt et al. 2017). When viewed by a normal human eye, the stickers did not obscure the sign or prevent interpretability. However, when images of the stickers stop signs were fed into standard traffic sign classification ML models, they were misclassified as speed limit signs over 85% of the time.\n\nEykholt, Kevin, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. 2017. “Robust Physical-World Attacks on Deep Learning Models.” ArXiv Preprint abs/1707.08945. https://arxiv.org/abs/1707.08945.\nThis demonstration showed how simple adversarial stickers could trick ML systems into misreading critical road signs. If deployed realistically, these attacks could endanger public safety, causing autonomous vehicles to misinterpret stop signs as speed limits. Researchers warned this could potentially cause dangerous rolling stops or acceleration into intersections.\nThis case study provides a concrete illustration of how adversarial examples exploit the pattern recognition mechanisms of ML models. By subtly altering the input data, attackers can induce incorrect predictions and pose significant risks to safety-critical applications like self-driving cars. The attack’s simplicity demonstrates how even minor, imperceptible changes can lead models astray. Consequently, developers must implement robust defenses against such threats.", + "text": "14.4 Security Threats to ML Models\nML models face security risks that can undermine their integrity, performance, and trustworthiness if not adequately addressed. While there are several different threats, the primary threats include: Model theft, where adversaries steal the proprietary model parameters and the sensitive data they contain. Data poisoning, which compromises models through data tampering. Adversarial attacks deceive the model to make incorrect or unwanted predictions.\n\n14.4.1 Model Theft\nModel theft occurs when an attacker gains unauthorized access to a deployed ML model. The concern here is the theft of the model’s structure and trained parameters and the proprietary data it contains (Ateniese et al. 2015). Model theft is a real and growing threat, as demonstrated by cases like ex-Google engineer Anthony Levandowski, who allegedly stole Waymo’s self-driving car designs and started a competing company. Beyond economic impacts, model theft can seriously undermine privacy and enable further attacks.\n\nAteniese, Giuseppe, Luigi V. Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali, and Giovanni Felici. 2015. “Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers.” Int. J. Secur. Netw. 10 (3): 137. https://doi.org/10.1504/ijsn.2015.071829.\nFor instance, consider an ML model developed for personalized recommendations in an e-commerce application. If a competitor steals this model, they gain insights into business analytics, customer preferences, and even trade secrets embedded within the model’s data. Attackers could leverage stolen models to craft more effective inputs for model inversion attacks, deducing private details about the model’s training data. A cloned e-commerce recommendation model could reveal customer purchase behaviors and demographics.\nTo understand model inversion attacks, consider a facial recognition system used to grant access to secured facilities. The system is trained on a dataset of employee photos. An attacker could infer features of the original dataset by observing the model’s output to various inputs. For example, suppose the model’s confidence level for a particular face is significantly higher for a given set of features. In that case, an attacker might deduce that someone with those features is likely in the training dataset.\nThe methodology of model inversion typically involves the following steps:\n\nAccessing Model Outputs: The attacker queries the ML model with input data and observes the outputs. This is often done through a legitimate interface, like a public API.\nAnalyzing Confidence Scores: For each input, the model provides a confidence score that reflects how similar the input is to the training data.\nReverse-Engineering: By analyzing the confidence scores or output probabilities, attackers can use optimization techniques to reconstruct what they believe is close to the original input data.\n\nOne historical example of such a vulnerability being explored was the research on inversion attacks against the U.S. Netflix Prize dataset, where researchers demonstrated that it was possible to learn about an individual’s movie preferences, which could lead to privacy breaches (Narayanan and Shmatikov 2006).\n\nNarayanan, Arvind, and Vitaly Shmatikov. 2006. “How to Break Anonymity of the Netflix Prize Dataset.” arXiv Preprint Cs/0610105.\nModel theft implies that it could lead to economic losses, undermine competitive advantage, and violate user privacy. There’s also the risk of model inversion attacks, where an adversary could input various data into the stolen model to infer sensitive information about the training data.\nBased on the desired asset, model theft attacks can be divided into two categories: exact model properties and approximate model behavior.\n\nStealing Exact Model Properties\nIn these attacks, the objective is to extract information about concrete metrics, such as a network’s learned parameters, fine-tuned hyperparameters, and the model’s internal layer architecture (Oliynyk, Mayer, and Rauber 2023).\n\nLearned Parameters: Adversaries aim to steal a model’s learned knowledge (weights and biases) to replicate it. Parameter theft is generally used with other attacks, such as architecture theft, which lacks parameter knowledge.\nFine-Tuned Hyperparameters: Training is costly, and identifying the optimal configuration of hyperparameters (such as learning rate and regularization) can be time-consuming and resource-intensive. Consequently, stealing a model’s optimized hyperparameters enables adversaries to replicate the model without incurring the exact development costs.\nModel Architecture: This attack concerns the specific design and structure of the model, such as layers, neurons, and connectivity patterns. Beyond reducing associated training costs, this theft poses a severe risk to intellectual property, potentially undermining a company’s competitive advantage. Architecture theft can be achieved by exploiting side-channel attacks (discussed later).\n\n\n\nStealing Approximate Model Behavior\nInstead of extracting exact numerical values of the model’s parameters, these attacks aim to reproduce the model’s behavior (predictions and effectiveness), decision-making, and high-level characteristics (Oliynyk, Mayer, and Rauber 2023). These techniques aim to achieve similar outcomes while allowing for internal deviations in parameters and architecture. Types of approximate behavior theft include gaining the same level of effectiveness and obtaining prediction consistency.\n\nOliynyk, Daryna, Rudolf Mayer, and Andreas Rauber. 2023. “I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences.” ACM Comput. Surv. 55 (14s): 1–41. https://doi.org/10.1145/3595292.\n\nLevel of Effectiveness: Attackers aim to replicate the model’s decision-making capabilities rather than focus on the precise parameter values. This is done through understanding the overall behavior of the model. Consider a scenario where an attacker wants to copy the behavior of an image classification model. By analyzing the model’s decision boundaries, the attack tunes its model to reach an effectiveness comparable to the original model. This could entail analyzing 1) the confusion matrix to understand the balance of prediction metrics (true positive, true negative, false positive, false negative) and 2) other performance metrics, such as F1 score and precision, to ensure that the two models are comparable.\nPrediction Consistency: The attacker tries to align their model’s prediction patterns with the target model’s. This involves matching prediction outputs (both positive and negative) on the same set of inputs and ensuring distributional consistency across different classes. For instance, consider a natural language processing (NLP) model that generates sentiment analysis for movie reviews (labels reviews as positive, neutral, or negative). The attacker will try to fine-tune their model to match the prediction of the original models on the same set of movie reviews. This includes ensuring that the model makes the same mistakes (mispredictions) that the targeted model makes.\n\n\n\nCase Study\nIn 2018, Tesla filed a lawsuit against self-driving car startup Zoox, alleging former employees stole confidential data and trade secrets related to Tesla’s autonomous driving assistance system.\nTesla claimed that several of its former employees took over 10 G.B. of proprietary data, including ML models and source code, before joining Zoox. This allegedly included one of Tesla’s crucial image recognition models for identifying objects.\nThe theft of this sensitive proprietary model could help Zoox shortcut years of ML development and duplicate Tesla’s capabilities. Tesla argued this theft of I.P. caused significant financial and competitive harm. There were also concerns it could allow model inversion attacks to infer private details about Tesla’s testing data.\nThe Zoox employees denied stealing any proprietary information. However, the case highlights the significant risks of model theft—enabling the cloning of commercial models, causing economic impacts, and opening the door for further data privacy violations.\n\n\n\n14.4.2 Data Poisoning\nData poisoning is an attack where the training data is tampered with, leading to a compromised model (Biggio, Nelson, and Laskov 2012). Attackers can modify existing training examples, insert new malicious data points, or influence the data collection process. The poisoned data is labeled in such a way as to skew the model’s learned behavior. This can be particularly damaging in applications where ML models make automated decisions based on learned patterns. Beyond training sets, poisoning tests and validation data can allow adversaries to boost reported model performance artificially.\n\nBiggio, Battista, Blaine Nelson, and Pavel Laskov. 2012. “Poisoning Attacks Against Support Vector Machines.” In Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012. icml.cc / Omnipress. http://icml.cc/2012/papers/880.pdf.\nThe process usually involves the following steps:\n\nInjection: The attacker adds incorrect or misleading examples into the training set. These examples are often designed to look normal to cursory inspection but have been carefully crafted to disrupt the learning process.\nTraining: The ML model trains on this manipulated dataset and develops skewed understandings of the data patterns.\nDeployment: Once the model is deployed, the corrupted training leads to flawed decision-making or predictable vulnerabilities the attacker can exploit.\n\nThe impacts of data poisoning extend beyond just classification errors or accuracy drops. For instance, if incorrect or malicious data is introduced into a traffic sign recognition system’s training set, the model may learn to misclassify stop signs as yield signs, which can have dangerous real-world consequences, especially in embedded autonomous systems like autonomous vehicles.\nData poisoning can degrade a model’s accuracy, force it to make incorrect predictions or cause it to behave unpredictably. In critical applications like healthcare, such alterations can lead to significant trust and safety issues.\nThere are six main categories of data poisoning (Oprea, Singhal, and Vassilev 2022):\n\nOprea, Alina, Anoop Singhal, and Apostol Vassilev. 2022. “Poisoning Attacks Against Machine Learning: Can Machine Learning Be Trustworthy?” Computer 55 (11): 94–99. https://doi.org/10.1109/mc.2022.3190787.\n\nAvailability Attacks: These attacks seek to compromise a model’s overall functionality. They cause it to misclassify most testing samples, rendering the model unusable for practical applications. An example is label flipping, where labels of a specific, targeted class are replaced with labels from a different one.\nTargeted Attacks: Unlike availability attacks, targeted attacks aim to compromise a small number of the testing samples. So, the effect is localized to a limited number of classes, while the model maintains the same original level of accuracy on most of the classes. The targeted nature of the attack requires the attacker to possess knowledge of the model’s classes, making detecting these attacks more challenging.\nBackdoor Attacks: In these attacks, an adversary targets specific patterns in the data. The attacker introduces a backdoor (a malicious, hidden trigger or pattern) into the training data, such as altering certain features in structured data or a pattern of pixels at a fixed position. This causes the model to associate the malicious pattern with specific labels. As a result, when the model encounters test samples that contain a malicious pattern, it makes false predictions, highlighting the importance of caution and prevention in the role of data security professionals.\nSubpopulation Attacks: Attackers selectively choose to compromise a subset of the testing samples while maintaining accuracy on the rest of the samples. You can think of these attacks as a combination of availability and targeted attacks: performing availability attacks (performance degradation) within the scope of a targeted subset. Although subpopulation attacks may seem very similar to targeted attacks, the two have clear differences:\nScope: While targeted attacks target a selected set of samples, subpopulation attacks target a general subpopulation with similar feature representations. For example, in a targeted attack, an actor inserts manipulated images of a ‘speed bump’ warning sign (with carefully crafted perturbation or patterns), which causes an autonomous car to fail to recognize such a sign and slow down. On the other hand, manipulating all samples of people with a British accent so that a speech recognition model would misclassify a British person’s speech is an example of a subpopulation attack.\nKnowledge: While targeted attacks require a high degree of familiarity with the data, subpopulation attacks require less intimate knowledge to be effective.\n\n\nCase Study 1\nIn 2017, researchers demonstrated a data poisoning attack against a popular toxicity classification model called Perspective (Hosseini et al. 2017). This ML model detects toxic comments online.\n\nHosseini, Hossein, Sreeram Kannan, Baosen Zhang, and Radha Poovendran. 2017. “Deceiving Google’s Perspective Api Built for Detecting Toxic Comments.” ArXiv Preprint abs/1702.08138. https://arxiv.org/abs/1702.08138.\nThe researchers added synthetically generated toxic comments with slight misspellings and grammatical errors to the model’s training data. This slowly corrupted the model, causing it to misclassify increasing numbers of severely toxic inputs as non-toxic over time.\nAfter retraining on the poisoned data, the model’s false negative rate increased from 1.4% to 27% - allowing extremely toxic comments to bypass detection. The researchers warned this stealthy data poisoning could enable the spread of hate speech, harassment, and abuse if deployed against real moderation systems.\nThis case highlights how data poisoning can degrade model accuracy and reliability. For social media platforms, a poisoning attack that impairs toxicity detection could lead to the proliferation of harmful content and distrust of ML moderation systems. The example demonstrates why securing training data integrity and monitoring for poisoning is critical across application domains.\n\n\nCase Study 2\nInterestingly enough, data poisoning attacks are not always malicious (Shan et al. 2023). Nightshade, a tool developed by a team led by Professor Ben Zhao at the University of Chicago, utilizes data poisoning to help artists protect their art against scraping and copyright violations by generative A.I. models. Artists can use the tool to modify their images subtly before uploading them online.\nWhile these changes are imperceptible to the human eye, they can significantly degrade the performance of generative AI models when integrated into the training data. Generative models can be manipulated to produce unrealistic or nonsensical outputs. For example, with just 300 corrupted images, the University of Chicago researchers could deceive the latest Stable Diffusion model into generating images of canines resembling felines or bovines when prompted for automobiles.\nAs the quantity of corrupted images online grows, the efficacy of models trained on scraped data will decline exponentially. Initially, identifying corrupted data is challenging and necessitates manual intervention. Subsequently, contamination spreads rapidly to related concepts as generative models establish connections between words and their visual representations. Consequently, a corrupted image of a “car” could propagate into generated images linked to terms such as “truck,” “train,” and “bus.”\nOn the other hand, this tool can be used maliciously and affect legitimate generative model applications. This shows the very challenging and novel nature of machine learning attacks.\nFigure 17.26 demonstrates the effects of different levels of data poisoning (50 samples, 100 samples, and 300 samples of poisoned images) on generating images in various categories. Notice how the images start deforming and deviating from the desired category. For example, after 300 poison samples, a car prompt generates a cow.\n\n\n\n\n\n\nFigure 14.1: Data poisoning. Source: Shan et al. (2023).\n\n\nShan, Shawn, Wenxin Ding, Josephine Passananti, Haitao Zheng, and Ben Y Zhao. 2023. “Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models.” ArXiv Preprint abs/2310.13828. https://arxiv.org/abs/2310.13828.\n\n\n\n\n\n14.4.3 Adversarial Attacks\nAdversarial attacks aim to trick models into making incorrect predictions by providing them with specially crafted, deceptive inputs (called adversarial examples) (Parrish et al. 2023). By adding slight perturbations to input data, adversaries can “hack” a model’s pattern recognition and deceive it. These are sophisticated techniques where slight, often imperceptible alterations to input data can trick an ML model into making a wrong prediction.\n\nParrish, Alicia, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Max Bartolo, Oana Inel, Juan Ciro, et al. 2023. “Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models.” ArXiv Preprint abs/2305.14384. https://arxiv.org/abs/2305.14384.\n\nRamesh, Aditya, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. “Zero-Shot Text-to-Image Generation.” In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, edited by Marina Meila and Tong Zhang, 139:8821–31. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v139/ramesh21a.html.\n\nRombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer. 2022. “High-Resolution Image Synthesis with Latent Diffusion Models.” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. https://doi.org/10.1109/cvpr52688.2022.01042.\nOne can generate prompts that lead to unsafe images in text-to-image models like DALLE (Ramesh et al. 2021) or Stable Diffusion (Rombach et al. 2022). For example, by altering the pixel values of an image, attackers can deceive a facial recognition system into identifying a face as a different person.\nAdversarial attacks exploit the way ML models learn and make decisions during inference. These models work on the principle of recognizing patterns in data. An adversary crafts malicious inputs with perturbations to mislead the model’s pattern recognition—essentially ‘hacking’ the model’s perceptions.\nAdversarial attacks fall under different scenarios:\n\nWhitebox Attacks: The attacker has comprehensive knowledge of the target model’s internal workings, including the training data, parameters, and architecture. This extensive access facilitates the exploitation of the model’s vulnerabilities. The attacker can leverage specific and subtle weaknesses to construct highly effective adversarial examples.\nBlackbox Attacks: In contrast to whitebox attacks, in blackbox attacks, the attacker has little to no knowledge of the target model. The adversarial actor must carefully observe the model’s output behavior to carry out the attack.\nGreybox Attacks: These attacks occupy a spectrum between black-box and white-box attacks. The adversary possesses partial knowledge of the target model’s internal structure. For instance, the attacker might know the training data but lack information about the model’s architecture or parameters. In practical scenarios, most attacks fall within this grey area.\n\nThe landscape of machine learning models is complex and broad, especially given their relatively recent integration into commercial applications. This rapid adoption, while transformative, has brought to light numerous vulnerabilities within these models. Consequently, various adversarial attack methods have emerged, each strategically exploiting different aspects of different models. Below, we highlight a subset of these methods, showcasing the multifaceted nature of adversarial attacks on machine learning models:\n\nGenerative Adversarial Networks (GANs) are deep learning models consisting of two networks competing against each other: a generator and a discriminator (Goodfellow et al. 2020). The generator tries to synthesize realistic data while the discriminator evaluates whether they are real or fake. GANs can be used to craft adversarial examples. The generator network is trained to produce inputs that the target model misclassifies. These GAN-generated images can then attack a target classifier or detection model. The generator and the target model are engaged in a competitive process, with the generator continually improving its ability to create deceptive examples and the target model enhancing its resistance to such examples. GANs provide a robust framework for crafting complex and diverse adversarial inputs, illustrating the adaptability of generative models in the adversarial landscape.\nTransfer Learning Adversarial Attacks exploit the knowledge transferred from a pre-trained model to a target model, creating adversarial examples that can deceive both models. These attacks pose a growing concern, particularly when adversaries have knowledge of the feature extractor but lack access to the classification head (the part or layer responsible for making the final classifications). Referred to as “headless attacks,” these transferable adversarial strategies leverage the expressive capabilities of feature extractors to craft perturbations while oblivious to the label space or training data. The existence of such attacks underscores the importance of developing robust defenses for transfer learning applications, especially since pre-trained models are commonly used (Abdelkader et al. 2020).\n\n\nGoodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. “Generative Adversarial Networks.” Commun. ACM 63 (11): 139–44. https://doi.org/10.1145/3422622.\n\nAbdelkader, Ahmed, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, and Chen Zhu. 2020. “Headless Horseman: Adversarial Attacks on Transfer Learning Models.” In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3087–91. IEEE. https://doi.org/10.1109/icassp40776.2020.9053181.\n\nCase Study\nIn 2017, researchers conducted experiments by placing small black and white stickers on stop signs (Eykholt et al. 2017). When viewed by a normal human eye, the stickers did not obscure the sign or prevent interpretability. However, when images of the stickers stop signs were fed into standard traffic sign classification ML models, they were misclassified as speed limit signs over 85% of the time.\n\nEykholt, Kevin, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. 2017. “Robust Physical-World Attacks on Deep Learning Models.” ArXiv Preprint abs/1707.08945. https://arxiv.org/abs/1707.08945.\nThis demonstration showed how simple adversarial stickers could trick ML systems into misreading critical road signs. If deployed realistically, these attacks could endanger public safety, causing autonomous vehicles to misinterpret stop signs as speed limits. Researchers warned this could potentially cause dangerous rolling stops or acceleration into intersections.\nThis case study provides a concrete illustration of how adversarial examples exploit the pattern recognition mechanisms of ML models. By subtly altering the input data, attackers can induce incorrect predictions and pose significant risks to safety-critical applications like self-driving cars. The attack’s simplicity demonstrates how even minor, imperceptible changes can lead models astray. Consequently, developers must implement robust defenses against such threats.", "crumbs": [ "Advanced Topics", "14  Security & Privacy" @@ -1533,7 +1533,7 @@ "href": "contents/privacy_security/privacy_security.html#security-threats-to-ml-hardware", "title": "14  Security & Privacy", "section": "14.5 Security Threats to ML Hardware", - "text": "14.5 Security Threats to ML Hardware\nA systematic examination of security threats to embedded machine learning hardware is essential to comprehensively understanding potential vulnerabilities in ML systems. Initially, hardware vulnerabilities arising from intrinsic design flaws that can be exploited will be explored. This foundational knowledge is crucial for recognizing the origins of hardware weaknesses. Following this, physical attacks will be examined, representing the most direct and overt methods of compromising hardware integrity. Building on this, fault injection attacks will be analyzed, demonstrating how deliberate manipulations can induce system failures.\nAdvancing to side-channel attacks next will show the increasing complexity, as these rely on exploiting indirect information leakages, requiring a nuanced understanding of hardware operations and environmental interactions. Leaky interfaces will show how external communication channels can become vulnerable, leading to accidental data exposures. Counterfeit hardware discussions benefit from prior explorations of hardware integrity and exploitation techniques, as they often compound these issues with additional risks due to their questionable provenance. Finally, supply chain risks encompass all concerns above and frame them within the context of the hardware’s journey from production to deployment, highlighting the multifaceted nature of hardware security and the need for vigilance at every stage.\nTable 14.1 overview table summarizing the topics:\n\n\n\nTable 14.1: Threat types on hardware security.\n\n\n\n\n\n\n\n\n\n\nThreat Type\nDescription\nRelevance to ML Hardware Security\n\n\n\n\nHardware Bugs\nIntrinsic flaws in hardware designs that can compromise system integrity.\nFoundation of hardware vulnerability.\n\n\nPhysical Attacks\nDirect exploitation of hardware through physical access or manipulation.\nBasic and overt threat model.\n\n\nFault-injection Attacks\nInduction of faults to cause errors in hardware operation, leading to potential system crashes.\nSystematic manipulation leading to failure.\n\n\nSide-Channel Attacks\nExploitation of leaked information from hardware operation to extract sensitive data.\nIndirect attack via environmental observation.\n\n\nLeaky Interfaces\nVulnerabilities arising from interfaces that expose data unintentionally.\nData exposure through communication channels.\n\n\nCounterfeit Hardware\nUse of unauthorized hardware components that may have security flaws.\nCompounded vulnerability issues.\n\n\nSupply Chain Risks\nRisks introduced through the hardware lifecycle, from production to deployment.\nCumulative & multifaceted security challenges.\n\n\n\n\n\n\n\n14.5.1 Hardware Bugs\nHardware is not immune to the pervasive issue of design flaws or bugs. Attackers can exploit these vulnerabilities to access, manipulate, or extract sensitive data, breaching the confidentiality and integrity that users and services depend on. An example of such vulnerabilities came to light with the discovery of Meltdown and Spectre—two hardware vulnerabilities that exploit critical vulnerabilities in modern processors. These bugs allow attackers to bypass the hardware barrier that separates applications, allowing a malicious program to read the memory of other programs and the operating system.\nMeltdown (Kocher et al. 2019a) and Spectre (Kocher et al. 2019b) work by taking advantage of optimizations in modern CPUs that allow them to speculatively execute instructions out of order before validity checks have been completed. This reveals data that should be inaccessible, which the attack captures through side channels like caches. The technical complexity demonstrates the difficulty of eliminating vulnerabilities even with extensive validation.\n\n———, et al. 2019a. “Spectre Attacks: Exploiting Speculative Execution.” In 2019 IEEE Symposium on Security and Privacy (SP). IEEE. https://doi.org/10.1109/sp.2019.00002.\n\nKocher, Paul, Jann Horn, Anders Fogh, Daniel Genkin, Daniel Gruss, Werner Haas, Mike Hamburg, et al. 2019b. “Spectre Attacks: Exploiting Speculative Execution.” In 2019 IEEE Symposium on Security and Privacy (SP). IEEE. https://doi.org/10.1109/sp.2019.00002.\nIf an ML system is processing sensitive data, such as personal user information or proprietary business analytics, Meltdown and Spectre represent a real and present danger to data security. Consider the case of an ML accelerator card designed to speed up machine learning processes, such as the ones we discussed in the A.I. Hardware chapter. These accelerators work with the CPU to handle complex calculations, often related to data analytics, image recognition, and natural language processing. If such an accelerator card has a vulnerability akin to Meltdown or Spectre, it could leak the data it processes. An attacker could exploit this flaw not just to siphon off data but also to gain insights into the ML model’s workings, including potentially reverse-engineering the model itself (thus, going back to the issue of model theft.\nA real-world scenario where this could be devastating would be in the healthcare industry. ML systems routinely process highly sensitive patient data to help diagnose, plan treatment, and forecast outcomes. A bug in the system’s hardware could lead to the unauthorized disclosure of personal health information, violating patient privacy and contravening strict regulatory standards like the Health Insurance Portability and Accountability Act (HIPAA)\nThe Meltdown and Spectre vulnerabilities are stark reminders that hardware security is not just about preventing unauthorized physical access but also about ensuring that the hardware’s architecture does not become a conduit for data exposure. Similar hardware design flaws regularly emerge in CPUs, accelerators, memory, buses, and other components. This necessitates ongoing retroactive mitigations and performance trade-offs in deployed systems. Proactive solutions like confidential computing architectures could mitigate entire classes of vulnerabilities through fundamentally more secure hardware design. Thwarting hardware bugs requires rigor at every design stage, validation, and deployment.\n\n\n14.5.2 Physical Attacks\nPhysical tampering refers to the direct, unauthorized manipulation of physical computing resources to undermine the integrity of machine learning systems. It’s a particularly insidious attack because it circumvents traditional cybersecurity measures, which often focus more on software vulnerabilities than hardware threats.\nPhysical tampering can take many forms, from the relatively simple, such as someone inserting a USB device loaded with malicious software into a server, to the highly sophisticated, such as embedding a hardware Trojan during the manufacturing process of a microchip (discussed later in greater detail in the Supply Chain section). ML systems are susceptible to this attack because they rely on the accuracy and integrity of their hardware to process and analyze vast amounts of data correctly.\nConsider an ML-powered drone used for geographical mapping. The drone’s operation relies on a series of onboard systems, including a navigation module that processes inputs from various sensors to determine its path. If an attacker gains physical access to this drone, they could replace the genuine navigation module with a compromised one that includes a backdoor. This manipulated module could then alter the drone’s flight path to conduct surveillance over restricted areas or even smuggle contraband by flying undetected routes.\nAnother example is the physical tampering of biometric scanners used for access control in secure facilities. By introducing a modified sensor that transmits biometric data to an unauthorized receiver, an attacker can access personal identification data to authenticate individuals.\nThere are several ways that physical tampering can occur in ML hardware:\n\nManipulating sensors: Consider an autonomous vehicle equipped with cameras and LiDAR for environmental perception. A malicious actor could deliberately manipulate the physical alignment of these sensors to create occlusion zones or distort distance measurements. This could compromise object detection capabilities and potentially endanger vehicle occupants.\nHardware trojans: Malicious circuit modifications can introduce trojans designed to activate upon specific input conditions. For instance, an ML accelerator chip might operate as intended until encountering a predetermined trigger, at which point it behaves erratically.\nTampering with memory: Physically exposing and manipulating memory chips could allow the extraction of encrypted ML model parameters. Fault injection techniques can also corrupt model data to degrade accuracy.\nIntroducing backdoors: Gaining physical access to servers, an adversary could use hardware keyloggers to capture passwords and create backdoor accounts for persistent access. These could then be used to exfiltrate ML training data over time.\nSupply chain attacks: Manipulating third-party hardware components or compromising manufacturing and shipping channels creates systemic vulnerabilities that are difficult to detect and remediate.\n\n\n\n14.5.3 Fault-injection Attacks\nBy intentionally introducing faults into ML hardware, attackers can induce errors in the computational process, leading to incorrect outputs. This manipulation compromises the integrity of ML operations and can serve as a vector for further exploitation, such as system reverse engineering or security protocol bypass. Fault injection involves deliberately disrupting standard computational operations in a system through external interference (Joye and Tunstall 2012). By precisely triggering computational errors, adversaries can alter program execution in ways that degrade reliability or leak sensitive information.\n\nJoye, Marc, and Michael Tunstall. 2012. Fault Analysis in Cryptography. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-29656-7.\n\nBarenghi, Alessandro, Guido M. Bertoni, Luca Breveglieri, Mauro Pellicioli, and Gerardo Pelosi. 2010. “Low Voltage Fault Attacks to AES.” In 2010 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST), 7–12. IEEE; IEEE. https://doi.org/10.1109/hst.2010.5513121.\n\nHutter, Michael, Jorn-Marc Schmidt, and Thomas Plos. 2009. “Contact-Based Fault Injections and Power Analysis on RFID Tags.” In 2009 European Conference on Circuit Theory and Design, 409–12. IEEE; IEEE. https://doi.org/10.1109/ecctd.2009.5275012.\n\nAmiel, Frederic, Christophe Clavier, and Michael Tunstall. 2006. “Fault Analysis of DPA-Resistant Algorithms.” In International Workshop on Fault Diagnosis and Tolerance in Cryptography, 223–36. Springer.\n\nAgrawal, Dakshi, Selcuk Baktir, Deniz Karakoyunlu, Pankaj Rohatgi, and Berk Sunar. 2007. “Trojan Detection Using IC Fingerprinting.” In 2007 IEEE Symposium on Security and Privacy (SP ’07), 29–45. Springer; IEEE. https://doi.org/10.1109/sp.2007.36.\n\nSkorobogatov, Sergei. 2009. “Local Heating Attacks on Flash Memory Devices.” In 2009 IEEE International Workshop on Hardware-Oriented Security and Trust, 1–6. IEEE; IEEE. https://doi.org/10.1109/hst.2009.5225028.\n\nSkorobogatov, Sergei P, and Ross J Anderson. 2003. “Optical Fault Induction Attacks.” In Cryptographic Hardware and Embedded Systems-CHES 2002: 4th International Workshop Redwood Shores, CA, USA, August 1315, 2002 Revised Papers 4, 2–12. Springer.\nVarious physical tampering techniques can be used for fault injection. Low voltage (Barenghi et al. 2010), power spikes (Hutter, Schmidt, and Plos 2009), clock glitches (Amiel, Clavier, and Tunstall 2006), electromagnetic pulses (Agrawal et al. 2007), temperate increase (S. Skorobogatov 2009) and laser strikes (S. P. Skorobogatov and Anderson 2003) are common hardware attack vectors. They are precisely timed to induce faults like flipped bits or skipped instructions during critical operations.\nFor ML systems, consequences include impaired model accuracy, denial of service, extraction of private training data or model parameters, and reverse engineering of model architectures. Attackers could use fault injection to force misclassifications, disrupt autonomous systems, or steal intellectual property.\nFor example, in (Breier et al. 2018), the authors successfully injected a fault attack into a deep neural network deployed on a microcontroller. They used a laser to heat specific transistors, forcing them to switch states. In one instance, they used this method to attack a ReLU activation function, resulting in the function always outputting a value of 0, regardless of the input. In the assembly code in Figure 14.2, the attack caused the executing program always to skip the jmp end instruction on line 6. This means that HiddenLayerOutput[i] is always set to 0, overwriting any values written to it on lines 4 and 5. As a result, the targeted neurons are rendered inactive, resulting in misclassifications.\n\n\n\n\n\n\nFigure 14.2: Fault-injection demonstrated with assembly code. Source: Breier et al. (2018).\n\n\nBreier, Jakub, Xiaolu Hou, Dirmanto Jap, Lei Ma, Shivam Bhasin, and Yang Liu. 2018. “Deeplaser: Practical Fault Attack on Deep Neural Networks.” ArXiv Preprint abs/1806.05859. https://arxiv.org/abs/1806.05859.\n\n\nAn attacker’s strategy could be to infer information about the activation functions using side-channel attacks (discussed next). Then, the attacker could attempt to target multiple activation function computations by randomly injecting faults into the layers as close to the output layer as possible, increasing the likelihood and impact of the attack.\nEmbedded devices are particularly vulnerable due to limited physical hardening and resource constraints that restrict robust runtime defenses. Without tamper-resistant packaging, attacker access to system buses and memory enables precise fault strikes. Lightweight embedded ML models also lack redundancy to overcome errors.\nThese attacks can be particularly insidious because they bypass traditional software-based security measures, often not accounting for physical disruptions. Furthermore, because ML systems rely heavily on the accuracy and reliability of their hardware for tasks like pattern recognition, decision-making, and automated responses, any compromise in their operation due to fault injection can have severe and wide-ranging consequences.\nMitigating fault injection risks necessitates a multilayer approach. Physical hardening through tamper-proof enclosures and design obfuscation helps reduce access. Lightweight anomaly detection can identify unusual sensor inputs or erroneous model outputs (Hsiao et al. 2023). Error-correcting memories minimize disruption, while data encryption safeguards information. Emerging model watermarking techniques trace stolen parameters.\n\nHsiao, Yu-Shun, Zishen Wan, Tianyu Jia, Radhika Ghosal, Abdulrahman Mahmoud, Arijit Raychowdhury, David Brooks, Gu-Yeon Wei, and Vijay Janapa Reddi. 2023. “MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles.” In 2023 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 1–6. IEEE; IEEE. https://doi.org/10.23919/date56975.2023.10137246.\nHowever, balancing robust protections with embedded systems’ tight size and power limits remains challenging. Cryptography limits and lack of secure co-processors on cost-sensitive embedded hardware restrict options. Ultimately, fault injection resilience demands a cross-layer perspective spanning electrical, firmware, software, and physical design layers.\n\n\n14.5.4 Side-Channel Attacks\nSide-channel attacks constitute a class of security breaches that exploit information inadvertently revealed through the physical implementation of computing systems. In contrast to direct attacks targeting software or network vulnerabilities, these attacks leverage the system’s inherent hardware characteristics to extract sensitive information.\nThe fundamental premise of a side-channel attack is that a device’s operation can inadvertently reveal information. Such leaks can come from various sources, including the electrical power a device consumes (Kocher, Jaffe, and Jun 1999), the electromagnetic fields it emits (Gandolfi, Mourtel, and Olivier 2001), the time it takes to process certain operations, or even the sounds it produces. Each channel can indirectly glimpse the system’s internal processes, revealing information that can compromise security.\n\nKocher, Paul, Joshua Jaffe, and Benjamin Jun. 1999. “Differential Power Analysis.” In Advances in CryptologyCRYPTO’99: 19th Annual International Cryptology Conference Santa Barbara, California, USA, August 1519, 1999 Proceedings 19, 388–97. Springer.\n\nGandolfi, Karine, Christophe Mourtel, and Francis Olivier. 2001. “Electromagnetic Analysis: Concrete Results.” In Cryptographic Hardware and Embedded SystemsCHES 2001: Third International Workshop Paris, France, May 1416, 2001 Proceedings 3, 251–61. Springer.\n\nKocher, Paul, Joshua Jaffe, Benjamin Jun, and Pankaj Rohatgi. 2011. “Introduction to Differential Power Analysis.” Journal of Cryptographic Engineering 1 (1): 5–27. https://doi.org/10.1007/s13389-011-0006-y.\nFor instance, consider a machine learning system performing encrypted transactions. Encryption algorithms are supposed to secure data but require computational work to encrypt and decrypt information. An attacker can analyze the power consumption patterns of the device performing encryption to figure out the cryptographic key. With sophisticated statistical methods, small variations in power usage during the encryption process can be correlated with the data being processed, eventually revealing the key. Some differential analysis attack techniques are Differential Power Analysis (DPA) (Kocher et al. 2011), Differential Electromagnetic Analysis (DEMA), and Correlation Power Analysis (CPA).\nFor example, consider an attacker trying to break the AES encryption algorithm using a differential analysis attack. The attacker would first need to collect many power or electromagnetic traces (a trace is a record of consumptions or emissions) of the device while performing AES encryption.\nOnce the attacker has collected sufficient traces, they would use a statistical technique to identify correlations between the traces and the different values of the plaintext (original, unencrypted text) and ciphertext (encrypted text). These correlations would then be used to infer the value of a bit in the AES key and, eventually, the entire key. Differential analysis attacks are dangerous because they are low-cost, effective, and non-intrusive, allowing attackers to bypass algorithmic and hardware-level security measures. Compromises by these attacks are also hard to detect because they do not physically modify the device or break the encryption algorithm.\nBelow, a simplified visualization illustrates how analyzing the encryption device’s power consumption patterns can help extract information about the algorithm’s operations and, in turn, the secret data. Consider a device that takes a 5-byte password as input. The different voltage patterns measured while the encryption device performs operations on the input to authenticate the password will be analyzed and compared.\nFirst, the power analysis of the device’s operations after entering a correct password is shown in the first picture in Figure 14.3. The dense blue graph outputs the encryption device’s voltage measurement. What is significant here is the comparison between the different analysis charts rather than the specific details of what is happening in each scenario.\n\n\n\n\n\n\nFigure 14.3: Power analysis of an encryption device with a correct password. Source: Colin O’Flynn.\n\n\n\nWhen an incorrect password is entered, the power analysis chart is shown in Figure 14.4. The first three bytes of the password are correct. As a result, the voltage patterns are very similar or identical between the two charts, up to and including the fourth byte. After the device processes the fourth byte, a mismatch between the secret key and the attempted input is determined. A change in the pattern at the transition point between the fourth and fifth bytes is noticed: the voltage increases (the current decreases) because the device has stopped processing the rest of the input.\n\n\n\n\n\n\nFigure 14.4: Power analysis of an encryption device with a (partially) wrong password. Source: Colin O’Flynn.\n\n\n\nFigure 14.5 describes another chart of a completely wrong password. After the device finishes processing the first byte, it determines that it is incorrect and stops further processing - the voltage goes up and the current down.\n\n\n\n\n\n\nFigure 14.5: Power analysis of an encryption device with a wrong password. Source: Colin O’Flynn.\n\n\n\nThe example above demonstrates how information about the encryption process and the secret key can be inferred by analyzing different inputs and attempting to ‘eavesdrop’ on the device’s operations on each input byte. For a more detailed explanation, watch Video 14.3 below.\n\n\n\n\n\n\nVideo 14.3: Power Attack\n\n\n\n\n\n\nAnother example is an ML system for speech recognition, which processes voice commands to perform actions. By measuring the latency for the system to respond to commands or the power used during processing, an attacker could infer what commands are being processed and thus learn about the system’s operational patterns. Even more subtly, the sound emitted by a computer’s fan or hard drive could change in response to the workload, which a sensitive microphone could pick up and analyze to determine what kind of operations are being performed.\nIn real-world scenarios, side-channel attacks have effectively extracted encryption keys and compromised secure communications. One of the earliest recorded instances of such an attack occurred in the 1960s when the British intelligence agency MI5 confronted the challenge of deciphering encrypted communications from the Egyptian Embassy in London. Their cipher-breaking efforts were initially thwarted by the computational limitations of the time until an ingenious observation by MI5 agent Peter Wright altered the course of the operation.\nMI5 agent Peter Wright proposed using a microphone to capture the subtle acoustic signatures emitted from the embassy’s rotor cipher machine during encryption (Burnet and Thomas 1989). The distinct mechanical clicks of the rotors as operators configured them daily leaked critical information about the initial settings. This simple side channel of sound enabled MI5 to reduce the complexity of deciphering messages dramatically. This early acoustic leak attack highlights that side-channel attacks are not merely a digital age novelty but a continuation of age-old cryptanalytic principles. The notion that where there is a signal, there is an opportunity for interception remains foundational. From mechanical clicks to electrical fluctuations and beyond, side channels enable adversaries to extract secrets indirectly through careful signal analysis.\n\nBurnet, David, and Richard Thomas. 1989. “Spycatcher: The Commodification of Truth.” J. Law Soc. 16 (2): 210. https://doi.org/10.2307/1410360.\n\nAsonov, D., and R. Agrawal. 2004. “Keyboard Acoustic Emanations.” In IEEE Symposium on Security and Privacy, 2004. Proceedings. 2004, 3–11. IEEE; IEEE. https://doi.org/10.1109/secpri.2004.1301311.\n\nGnad, Dennis R. E., Fabian Oboril, and Mehdi B. Tahoori. 2017. “Voltage Drop-Based Fault Attacks on FPGAs Using Valid Bitstreams.” In 2017 27th International Conference on Field Programmable Logic and Applications (FPL), 1–7. IEEE; IEEE. https://doi.org/10.23919/fpl.2017.8056840.\n\nZhao, Mark, and G. Edward Suh. 2018. “FPGA-Based Remote Power Side-Channel Attacks.” In 2018 IEEE Symposium on Security and Privacy (SP), 229–44. IEEE; IEEE. https://doi.org/10.1109/sp.2018.00049.\nToday, acoustic cryptanalysis has evolved into attacks like keyboard eavesdropping (Asonov and Agrawal 2004). Electrical side channels range from power analysis on cryptographic hardware (Gnad, Oboril, and Tahoori 2017) to voltage fluctuations (Zhao and Suh 2018) on machine learning accelerators. Timing, electromagnetic emission, and even heat footprints can likewise be exploited. New and unexpected side channels often emerge as computing becomes more interconnected and miniaturized.\nJust as MI5’s analog acoustic leak transformed their codebreaking, modern side-channel attacks circumvent traditional boundaries of cyber defense. Understanding the creative spirit and historical persistence of side channel exploits is key knowledge for developers and defenders seeking to secure modern machine learning systems comprehensively against digital and physical threats.\n\n\n14.5.5 Leaky Interfaces\nLeaky interfaces in embedded systems are often overlooked backdoors that can become significant security vulnerabilities. While designed for legitimate purposes such as communication, maintenance, or debugging, these interfaces may inadvertently provide attackers with a window through which they can extract sensitive information or inject malicious data.\nAn interface becomes “leaky” when it exposes more information than it should, often due to a lack of stringent access controls or inadequate shielding of the transmitted data. Here are some real-world examples of leaky interface issues causing security problems in IoT and embedded devices:\n\nBaby Monitors: Many WiFi-enabled baby monitors have been found to have unsecured interfaces for remote access. This allowed attackers to gain live audio and video feeds from people’s homes, representing a major privacy violation.\nPacemakers: Interface vulnerabilities were discovered in some pacemakers that could allow attackers to manipulate cardiac functions if exploited. This presents a potentially life-threatening scenario.\nSmart Lightbulbs: A researcher found he could access unencrypted data from smart lightbulbs via a debug interface, including WiFi credentials, allowing him to gain access to the connected network (Greengard 2015).\nSmart Cars: If left unsecured, The OBD-II diagnostic port has been shown to provide an attack vector into automotive systems. Researchers could use it to control brakes and other components (Miller and Valasek 2015).\n\n\nGreengard, Samuel. 2015. The Internet of Things. The MIT Press. https://doi.org/10.7551/mitpress/10277.001.0001.\n\nMiller, Charlie, and Chris Valasek. 2015. “Remote Exploitation of an Unaltered Passenger Vehicle.” Black Hat USA 2015 (S 91): 1–91.\nWhile the above are not directly connected with ML, consider the example of a smart home system with an embedded ML component that controls home security based on behavior patterns it learns over time. The system includes a maintenance interface accessible via the local network for software updates and system checks. If this interface does not require strong authentication or the data transmitted through it is not encrypted, an attacker on the same network could gain access. They could then eavesdrop on the homeowner’s daily routines or reprogram the security settings by manipulating the firmware.\nSuch leaks are a privacy issue and a potential entry point for more damaging exploits. The exposure of training data, model parameters, or ML outputs from a leak could help adversaries construct adversarial examples or reverse-engineer models. Access through a leaky interface could also be used to alter an embedded device’s firmware, loading it with malicious code that could turn off the device, intercept data, or use it in botnet attacks.\nTo mitigate these risks, a multi-layered approach is necessary, spanning technical controls like authentication, encryption, anomaly detection, policies and processes like interface inventories, access controls, auditing, and secure development practices. Turning off unnecessary interfaces and compartmentalizing risks via a zero-trust model provide additional protection.\nAs designers of embedded ML systems, we should assess interfaces early in development and continually monitor them post-deployment as part of an end-to-end security lifecycle. Understanding and securing interfaces is crucial for ensuring the overall security of embedded ML.\n\n\n14.5.6 Counterfeit Hardware\nML systems are only as reliable as the underlying hardware. In an era where hardware components are global commodities, the rise of counterfeit or cloned hardware presents a significant challenge. Counterfeit hardware encompasses any components that are unauthorized reproductions of original parts. Counterfeit components infiltrate ML systems through complex supply chains that stretch across borders and involve numerous stages from manufacture to delivery.\nA single lapse in the supply chain’s integrity can result in the insertion of counterfeit parts designed to closely imitate the functions and appearance of genuine hardware. For instance, a facial recognition system for high-security access control may be compromised if equipped with counterfeit processors. These processors could fail to accurately process and verify biometric data, potentially allowing unauthorized individuals to access restricted areas.\nThe challenge with counterfeit hardware is multifaceted. It undermines the quality and reliability of ML systems, as these components may degrade faster or perform unpredictably due to substandard manufacturing. The security risks are also profound; counterfeit hardware can contain vulnerabilities ripe for exploitation by malicious actors. For example, a cloned network router in an ML data center might include a hidden backdoor, enabling data interception or network intrusion without detection.\nFurthermore, counterfeit hardware poses legal and compliance risks. Companies inadvertently utilizing counterfeit parts in their ML systems may face serious legal repercussions, including fines and sanctions for failing to comply with industry regulations and standards. This is particularly true for sectors where compliance with specific safety and privacy regulations is mandatory, such as healthcare and finance.\nThe issue of counterfeit hardware is exacerbated by economic pressures to reduce costs, which can compel businesses to source from lower-cost suppliers without stringent verification processes. This economizing can inadvertently introduce counterfeit parts into otherwise secure systems. Additionally, detecting these counterfeits is inherently difficult since they are created to pass as the original components, often requiring sophisticated equipment and expertise to identify.\nIn ML, where decisions are made in real time and based on complex computations, the consequences of hardware failure are inconvenient and potentially dangerous. Stakeholders in the field of ML need to understand these risks thoroughly. The issues presented by counterfeit hardware necessitate a deep dive into the current challenges facing ML system integrity and emphasize the importance of vigilant, informed management of the hardware life cycle within these advanced systems.\n\n\n14.5.7 Supply Chain Risks\nThe threat of counterfeit hardware is closely tied to broader supply chain vulnerabilities. Globalized, interconnected supply chains create multiple opportunities for compromised components to infiltrate a product’s lifecycle. Supply chains involve numerous entities, from design to manufacturing, assembly, distribution, and integration. A lack of transparency and oversight of each partner makes verifying integrity at every step challenging. Lapses anywhere along the chain can allow the insertion of counterfeit parts.\nFor example, a contracted manufacturer may unknowingly receive and incorporate recycled electronic waste containing dangerous counterfeits. An untrustworthy distributor could smuggle in cloned components. Insider threats at any vendor might deliberately mix counterfeits into legitimate shipments.\nOnce counterfeits enter the supply stream, they move quickly through multiple hands before ending up in ML systems where detection is difficult. Advanced counterfeits like refurbished parts or clones with repackaged externals can masquerade as authentic components, passing visual inspection.\nTo identify fakes, thorough technical profiling using micrography, X-ray screening, component forensics, and functional testing is often required. However, such costly analysis is impractical for large-volume procurement.\nStrategies like supply chain audits, screening suppliers, validating component provenance, and adding tamper-evident protections can help mitigate risks. However, given global supply chain security challenges, a zero-trust approach is prudent. Designing ML systems to use redundant checking, fail-safes, and continuous runtime monitoring provides resilience against component compromises.\nRigorous validation of hardware sources coupled with fault-tolerant system architectures offers the most robust defense against the pervasive risks of convoluted, opaque global supply chains.\n\n\n14.5.8 Case Study\nIn 2018, Bloomberg Businessweek published an alarming story that got much attention in the tech world. The article claimed that Supermicro had secretly planted tiny spy chips on server hardware. Reporters said Chinese state hackers working with Supermicro could sneak these tiny chips onto motherboards during manufacturing. The tiny chips allegedly gave the hackers backdoor access to servers used by over 30 major companies, including Apple and Amazon.\nIf true, this would allow hackers to spy on private data or even tamper with systems. However, after investigating, Apple and Amazon found no proof that such hacked Supermicro hardware existed. Other experts questioned whether the Bloomberg article was accurate reporting.\nWhether the story is completely true or not is not our concern from a pedagogical viewpoint. However, this incident drew attention to the risks of global supply chains for hardware, especially manufactured in China. When companies outsource and buy hardware components from vendors worldwide, there needs to be more visibility into the process. In this complex global pipeline, there are concerns that counterfeits or tampered hardware could be slipped in somewhere along the way without tech companies realizing it. Companies relying too much on single manufacturers or distributors creates risk. For instance, due to the over-reliance on TSMC for semiconductor manufacturing, the U.S. has invested 50 billion dollars into the CHIPS Act.\nAs ML moves into more critical systems, verifying hardware integrity from design through production and delivery is crucial. The reported Supermicro backdoor demonstrated that for ML security, we cannot take global supply chains and manufacturing for granted. We must inspect and validate hardware at every link in the chain.", + "text": "14.5 Security Threats to ML Hardware\nA systematic examination of security threats to embedded machine learning hardware is essential to comprehensively understanding potential vulnerabilities in ML systems. Initially, hardware vulnerabilities arising from intrinsic design flaws that can be exploited will be explored. This foundational knowledge is crucial for recognizing the origins of hardware weaknesses. Following this, physical attacks will be examined, representing the most direct and overt methods of compromising hardware integrity. Building on this, fault injection attacks will be analyzed, demonstrating how deliberate manipulations can induce system failures.\nAdvancing to side-channel attacks next will show the increasing complexity, as these rely on exploiting indirect information leakages, requiring a nuanced understanding of hardware operations and environmental interactions. Leaky interfaces will show how external communication channels can become vulnerable, leading to accidental data exposures. Counterfeit hardware discussions benefit from prior explorations of hardware integrity and exploitation techniques, as they often compound these issues with additional risks due to their questionable provenance. Finally, supply chain risks encompass all concerns above and frame them within the context of the hardware’s journey from production to deployment, highlighting the multifaceted nature of hardware security and the need for vigilance at every stage.\nTable 14.1 overview table summarizing the topics:\n\n\n\nTable 14.1: Threat types on hardware security.\n\n\n\n\n\n\n\n\n\n\nThreat Type\nDescription\nRelevance to ML Hardware Security\n\n\n\n\nHardware Bugs\nIntrinsic flaws in hardware designs that can compromise system integrity.\nFoundation of hardware vulnerability.\n\n\nPhysical Attacks\nDirect exploitation of hardware through physical access or manipulation.\nBasic and overt threat model.\n\n\nFault-injection Attacks\nInduction of faults to cause errors in hardware operation, leading to potential system crashes.\nSystematic manipulation leading to failure.\n\n\nSide-Channel Attacks\nExploitation of leaked information from hardware operation to extract sensitive data.\nIndirect attack via environmental observation.\n\n\nLeaky Interfaces\nVulnerabilities arising from interfaces that expose data unintentionally.\nData exposure through communication channels.\n\n\nCounterfeit Hardware\nUse of unauthorized hardware components that may have security flaws.\nCompounded vulnerability issues.\n\n\nSupply Chain Risks\nRisks introduced through the hardware lifecycle, from production to deployment.\nCumulative & multifaceted security challenges.\n\n\n\n\n\n\n\n14.5.1 Hardware Bugs\nHardware is not immune to the pervasive issue of design flaws or bugs. Attackers can exploit these vulnerabilities to access, manipulate, or extract sensitive data, breaching the confidentiality and integrity that users and services depend on. An example of such vulnerabilities came to light with the discovery of Meltdown and Spectre—two hardware vulnerabilities that exploit critical vulnerabilities in modern processors. These bugs allow attackers to bypass the hardware barrier that separates applications, allowing a malicious program to read the memory of other programs and the operating system.\nMeltdown (Kocher et al. 2019a) and Spectre (Kocher et al. 2019b) work by taking advantage of optimizations in modern CPUs that allow them to speculatively execute instructions out of order before validity checks have been completed. This reveals data that should be inaccessible, which the attack captures through side channels like caches. The technical complexity demonstrates the difficulty of eliminating vulnerabilities even with extensive validation.\n\n———, et al. 2019a. “Spectre Attacks: Exploiting Speculative Execution.” In 2019 IEEE Symposium on Security and Privacy (SP). IEEE. https://doi.org/10.1109/sp.2019.00002.\n\nKocher, Paul, Jann Horn, Anders Fogh, Daniel Genkin, Daniel Gruss, Werner Haas, Mike Hamburg, et al. 2019b. “Spectre Attacks: Exploiting Speculative Execution.” In 2019 IEEE Symposium on Security and Privacy (SP). IEEE. https://doi.org/10.1109/sp.2019.00002.\nIf an ML system is processing sensitive data, such as personal user information or proprietary business analytics, Meltdown and Spectre represent a real and present danger to data security. Consider the case of an ML accelerator card designed to speed up machine learning processes, such as the ones we discussed in the A.I. Hardware chapter. These accelerators work with the CPU to handle complex calculations, often related to data analytics, image recognition, and natural language processing. If such an accelerator card has a vulnerability akin to Meltdown or Spectre, it could leak the data it processes. An attacker could exploit this flaw not just to siphon off data but also to gain insights into the ML model’s workings, including potentially reverse-engineering the model itself (thus, going back to the issue of model theft.\nA real-world scenario where this could be devastating would be in the healthcare industry. ML systems routinely process highly sensitive patient data to help diagnose, plan treatment, and forecast outcomes. A bug in the system’s hardware could lead to the unauthorized disclosure of personal health information, violating patient privacy and contravening strict regulatory standards like the Health Insurance Portability and Accountability Act (HIPAA)\nThe Meltdown and Spectre vulnerabilities are stark reminders that hardware security is not just about preventing unauthorized physical access but also about ensuring that the hardware’s architecture does not become a conduit for data exposure. Similar hardware design flaws regularly emerge in CPUs, accelerators, memory, buses, and other components. This necessitates ongoing retroactive mitigations and performance trade-offs in deployed systems. Proactive solutions like confidential computing architectures could mitigate entire classes of vulnerabilities through fundamentally more secure hardware design. Thwarting hardware bugs requires rigor at every design stage, validation, and deployment.\n\n\n14.5.2 Physical Attacks\nPhysical tampering refers to the direct, unauthorized manipulation of physical computing resources to undermine the integrity of machine learning systems. It’s a particularly insidious attack because it circumvents traditional cybersecurity measures, which often focus more on software vulnerabilities than hardware threats.\nPhysical tampering can take many forms, from the relatively simple, such as someone inserting a USB device loaded with malicious software into a server, to the highly sophisticated, such as embedding a hardware Trojan during the manufacturing process of a microchip (discussed later in greater detail in the Supply Chain section). ML systems are susceptible to this attack because they rely on the accuracy and integrity of their hardware to process and analyze vast amounts of data correctly.\nConsider an ML-powered drone used for geographical mapping. The drone’s operation relies on a series of onboard systems, including a navigation module that processes inputs from various sensors to determine its path. If an attacker gains physical access to this drone, they could replace the genuine navigation module with a compromised one that includes a backdoor. This manipulated module could then alter the drone’s flight path to conduct surveillance over restricted areas or even smuggle contraband by flying undetected routes.\nAnother example is the physical tampering of biometric scanners used for access control in secure facilities. By introducing a modified sensor that transmits biometric data to an unauthorized receiver, an attacker can access personal identification data to authenticate individuals.\nThere are several ways that physical tampering can occur in ML hardware:\n\nManipulating sensors: Consider an autonomous vehicle equipped with cameras and LiDAR for environmental perception. A malicious actor could deliberately manipulate the physical alignment of these sensors to create occlusion zones or distort distance measurements. This could compromise object detection capabilities and potentially endanger vehicle occupants.\nHardware trojans: Malicious circuit modifications can introduce trojans designed to activate upon specific input conditions. For instance, an ML accelerator chip might operate as intended until encountering a predetermined trigger, at which point it behaves erratically.\nTampering with memory: Physically exposing and manipulating memory chips could allow the extraction of encrypted ML model parameters. Fault injection techniques can also corrupt model data to degrade accuracy.\nIntroducing backdoors: Gaining physical access to servers, an adversary could use hardware keyloggers to capture passwords and create backdoor accounts for persistent access. These could then be used to exfiltrate ML training data over time.\nSupply chain attacks: Manipulating third-party hardware components or compromising manufacturing and shipping channels creates systemic vulnerabilities that are difficult to detect and remediate.\n\n\n\n14.5.3 Fault-injection Attacks\nBy intentionally introducing faults into ML hardware, attackers can induce errors in the computational process, leading to incorrect outputs. This manipulation compromises the integrity of ML operations and can serve as a vector for further exploitation, such as system reverse engineering or security protocol bypass. Fault injection involves deliberately disrupting standard computational operations in a system through external interference (Joye and Tunstall 2012). By precisely triggering computational errors, adversaries can alter program execution in ways that degrade reliability or leak sensitive information.\n\nJoye, Marc, and Michael Tunstall. 2012. Fault Analysis in Cryptography. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-29656-7.\n\nBarenghi, Alessandro, Guido M. Bertoni, Luca Breveglieri, Mauro Pellicioli, and Gerardo Pelosi. 2010. “Low Voltage Fault Attacks to AES.” In 2010 IEEE International Symposium on Hardware-Oriented Security and Trust (HOST), 7–12. IEEE; IEEE. https://doi.org/10.1109/hst.2010.5513121.\n\nHutter, Michael, Jorn-Marc Schmidt, and Thomas Plos. 2009. “Contact-Based Fault Injections and Power Analysis on RFID Tags.” In 2009 European Conference on Circuit Theory and Design, 409–12. IEEE; IEEE. https://doi.org/10.1109/ecctd.2009.5275012.\n\nAmiel, Frederic, Christophe Clavier, and Michael Tunstall. 2006. “Fault Analysis of DPA-Resistant Algorithms.” In International Workshop on Fault Diagnosis and Tolerance in Cryptography, 223–36. Springer.\n\nAgrawal, Dakshi, Selcuk Baktir, Deniz Karakoyunlu, Pankaj Rohatgi, and Berk Sunar. 2007. “Trojan Detection Using IC Fingerprinting.” In 2007 IEEE Symposium on Security and Privacy (SP ’07), 29–45. Springer; IEEE. https://doi.org/10.1109/sp.2007.36.\n\nSkorobogatov, Sergei. 2009. “Local Heating Attacks on Flash Memory Devices.” In 2009 IEEE International Workshop on Hardware-Oriented Security and Trust, 1–6. IEEE; IEEE. https://doi.org/10.1109/hst.2009.5225028.\n\nSkorobogatov, Sergei P, and Ross J Anderson. 2003. “Optical Fault Induction Attacks.” In Cryptographic Hardware and Embedded Systems-CHES 2002: 4th International Workshop Redwood Shores, CA, USA, August 1315, 2002 Revised Papers 4, 2–12. Springer.\nVarious physical tampering techniques can be used for fault injection. Low voltage (Barenghi et al. 2010), power spikes (Hutter, Schmidt, and Plos 2009), clock glitches (Amiel, Clavier, and Tunstall 2006), electromagnetic pulses (Agrawal et al. 2007), temperate increase (S. Skorobogatov 2009) and laser strikes (S. P. Skorobogatov and Anderson 2003) are common hardware attack vectors. They are precisely timed to induce faults like flipped bits or skipped instructions during critical operations.\nFor ML systems, consequences include impaired model accuracy, denial of service, extraction of private training data or model parameters, and reverse engineering of model architectures. Attackers could use fault injection to force misclassifications, disrupt autonomous systems, or steal intellectual property.\nFor example, in (Breier et al. 2018), the authors successfully injected a fault attack into a deep neural network deployed on a microcontroller. They used a laser to heat specific transistors, forcing them to switch states. In one instance, they used this method to attack a ReLU activation function, resulting in the function always outputting a value of 0, regardless of the input. In the assembly code in Figure 14.2, the attack caused the executing program always to skip the jmp end instruction on line 6. This means that HiddenLayerOutput[i] is always set to 0, overwriting any values written to it on lines 4 and 5. As a result, the targeted neurons are rendered inactive, resulting in misclassifications.\n\n\n\n\n\n\nFigure 14.2: Fault-injection demonstrated with assembly code. Source: Breier et al. (2018).\n\n\nBreier, Jakub, Xiaolu Hou, Dirmanto Jap, Lei Ma, Shivam Bhasin, and Yang Liu. 2018. “Deeplaser: Practical Fault Attack on Deep Neural Networks.” ArXiv Preprint abs/1806.05859. https://arxiv.org/abs/1806.05859.\n\n\nAn attacker’s strategy could be to infer information about the activation functions using side-channel attacks (discussed next). Then, the attacker could attempt to target multiple activation function computations by randomly injecting faults into the layers as close to the output layer as possible, increasing the likelihood and impact of the attack.\nEmbedded devices are particularly vulnerable due to limited physical hardening and resource constraints that restrict robust runtime defenses. Without tamper-resistant packaging, attacker access to system buses and memory enables precise fault strikes. Lightweight embedded ML models also lack redundancy to overcome errors.\nThese attacks can be particularly insidious because they bypass traditional software-based security measures, often not accounting for physical disruptions. Furthermore, because ML systems rely heavily on the accuracy and reliability of their hardware for tasks like pattern recognition, decision-making, and automated responses, any compromise in their operation due to fault injection can have severe and wide-ranging consequences.\nMitigating fault injection risks necessitates a multilayer approach. Physical hardening through tamper-proof enclosures and design obfuscation helps reduce access. Lightweight anomaly detection can identify unusual sensor inputs or erroneous model outputs (Hsiao et al. 2023). Error-correcting memories minimize disruption, while data encryption safeguards information. Emerging model watermarking techniques trace stolen parameters.\n\nHsiao, Yu-Shun, Zishen Wan, Tianyu Jia, Radhika Ghosal, Abdulrahman Mahmoud, Arijit Raychowdhury, David Brooks, Gu-Yeon Wei, and Vijay Janapa Reddi. 2023. “MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles.” In 2023 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 1–6. IEEE; IEEE. https://doi.org/10.23919/date56975.2023.10137246.\nHowever, balancing robust protections with embedded systems’ tight size and power limits remains challenging. Cryptography limits and lack of secure co-processors on cost-sensitive embedded hardware restrict options. Ultimately, fault injection resilience demands a cross-layer perspective spanning electrical, firmware, software, and physical design layers.\n\n\n14.5.4 Side-Channel Attacks\nSide-channel attacks constitute a class of security breaches that exploit information inadvertently revealed through the physical implementation of computing systems. In contrast to direct attacks targeting software or network vulnerabilities, these attacks leverage the system’s inherent hardware characteristics to extract sensitive information.\nThe fundamental premise of a side-channel attack is that a device’s operation can inadvertently reveal information. Such leaks can come from various sources, including the electrical power a device consumes (Kocher, Jaffe, and Jun 1999), the electromagnetic fields it emits (Gandolfi, Mourtel, and Olivier 2001), the time it takes to process certain operations, or even the sounds it produces. Each channel can indirectly glimpse the system’s internal processes, revealing information that can compromise security.\n\nKocher, Paul, Joshua Jaffe, and Benjamin Jun. 1999. “Differential Power Analysis.” In Advances in CryptologyCRYPTO’99: 19th Annual International Cryptology Conference Santa Barbara, California, USA, August 1519, 1999 Proceedings 19, 388–97. Springer.\n\nGandolfi, Karine, Christophe Mourtel, and Francis Olivier. 2001. “Electromagnetic Analysis: Concrete Results.” In Cryptographic Hardware and Embedded SystemsCHES 2001: Third International Workshop Paris, France, May 1416, 2001 Proceedings 3, 251–61. Springer.\n\nKocher, Paul, Joshua Jaffe, Benjamin Jun, and Pankaj Rohatgi. 2011. “Introduction to Differential Power Analysis.” Journal of Cryptographic Engineering 1 (1): 5–27. https://doi.org/10.1007/s13389-011-0006-y.\nFor instance, consider a machine learning system performing encrypted transactions. Encryption algorithms are supposed to secure data but require computational work to encrypt and decrypt information. An attacker can analyze the power consumption patterns of the device performing encryption to figure out the cryptographic key. With sophisticated statistical methods, small variations in power usage during the encryption process can be correlated with the data being processed, eventually revealing the key. Some differential analysis attack techniques are Differential Power Analysis (DPA) (Kocher et al. 2011), Differential Electromagnetic Analysis (DEMA), and Correlation Power Analysis (CPA).\nFor example, consider an attacker trying to break the AES encryption algorithm using a differential analysis attack. The attacker would first need to collect many power or electromagnetic traces (a trace is a record of consumptions or emissions) of the device while performing AES encryption.\nOnce the attacker has collected sufficient traces, they would use a statistical technique to identify correlations between the traces and the different values of the plaintext (original, unencrypted text) and ciphertext (encrypted text). These correlations would then be used to infer the value of a bit in the AES key and, eventually, the entire key. Differential analysis attacks are dangerous because they are low-cost, effective, and non-intrusive, allowing attackers to bypass algorithmic and hardware-level security measures. Compromises by these attacks are also hard to detect because they do not physically modify the device or break the encryption algorithm.\nBelow, a simplified visualization illustrates how analyzing the encryption device’s power consumption patterns can help extract information about the algorithm’s operations and, in turn, the secret data. Consider a device that takes a 5-byte password as input. The different voltage patterns measured while the encryption device performs operations on the input to authenticate the password will be analyzed and compared.\nFirst, the power analysis of the device’s operations after entering a correct password is shown in the first picture in Figure 14.3. The dense blue graph outputs the encryption device’s voltage measurement. What is significant here is the comparison between the different analysis charts rather than the specific details of what is happening in each scenario.\n\n\n\n\n\n\nFigure 14.3: Power analysis of an encryption device with a correct password. Source: Colin O’Flynn.\n\n\n\nWhen an incorrect password is entered, the power analysis chart is shown in Figure 14.4. The first three bytes of the password are correct. As a result, the voltage patterns are very similar or identical between the two charts, up to and including the fourth byte. After the device processes the fourth byte, a mismatch between the secret key and the attempted input is determined. A change in the pattern at the transition point between the fourth and fifth bytes is noticed: the voltage increases (the current decreases) because the device has stopped processing the rest of the input.\n\n\n\n\n\n\nFigure 14.4: Power analysis of an encryption device with a (partially) wrong password. Source: Colin O’Flynn.\n\n\n\nFigure 14.5 describes another chart of a completely wrong password. After the device finishes processing the first byte, it determines that it is incorrect and stops further processing - the voltage goes up and the current down.\n\n\n\n\n\n\nFigure 14.5: Power analysis of an encryption device with a wrong password. Source: Colin O’Flynn.\n\n\n\nThe example above demonstrates how information about the encryption process and the secret key can be inferred by analyzing different inputs and attempting to ‘eavesdrop’ on the device’s operations on each input byte. For a more detailed explanation, watch Video 14.3 below.\n\n\n\n\n\n\nVideo 14.3: Power Attack\n\n\n\n\n\n\nAnother example is an ML system for speech recognition, which processes voice commands to perform actions. By measuring the latency for the system to respond to commands or the power used during processing, an attacker could infer what commands are being processed and thus learn about the system’s operational patterns. Even more subtly, the sound emitted by a computer’s fan or hard drive could change in response to the workload, which a sensitive microphone could pick up and analyze to determine what kind of operations are being performed.\nIn real-world scenarios, side-channel attacks have effectively extracted encryption keys and compromised secure communications. One of the earliest recorded instances of such an attack occurred in the 1960s when the British intelligence agency MI5 confronted the challenge of deciphering encrypted communications from the Egyptian Embassy in London. Their cipher-breaking efforts were initially thwarted by the computational limitations of the time until an ingenious observation by MI5 agent Peter Wright altered the course of the operation.\nMI5 agent Peter Wright proposed using a microphone to capture the subtle acoustic signatures emitted from the embassy’s rotor cipher machine during encryption (Burnet and Thomas 1989). The distinct mechanical clicks of the rotors as operators configured them daily leaked critical information about the initial settings. This simple side channel of sound enabled MI5 to reduce the complexity of deciphering messages dramatically. This early acoustic leak attack highlights that side-channel attacks are not merely a digital age novelty but a continuation of age-old cryptanalytic principles. The notion that where there is a signal, there is an opportunity for interception remains foundational. From mechanical clicks to electrical fluctuations and beyond, side channels enable adversaries to extract secrets indirectly through careful signal analysis.\n\nBurnet, David, and Richard Thomas. 1989. “Spycatcher: The Commodification of Truth.” J. Law Soc. 16 (2): 210. https://doi.org/10.2307/1410360.\n\nAsonov, D., and R. Agrawal. 2004. “Keyboard Acoustic Emanations.” In IEEE Symposium on Security and Privacy, 2004. Proceedings. 2004, 3–11. IEEE; IEEE. https://doi.org/10.1109/secpri.2004.1301311.\n\nGnad, Dennis R. E., Fabian Oboril, and Mehdi B. Tahoori. 2017. “Voltage Drop-Based Fault Attacks on FPGAs Using Valid Bitstreams.” In 2017 27th International Conference on Field Programmable Logic and Applications (FPL), 1–7. IEEE; IEEE. https://doi.org/10.23919/fpl.2017.8056840.\n\nZhao, Mark, and G. Edward Suh. 2018. “FPGA-Based Remote Power Side-Channel Attacks.” In 2018 IEEE Symposium on Security and Privacy (SP), 229–44. IEEE; IEEE. https://doi.org/10.1109/sp.2018.00049.\nToday, acoustic cryptanalysis has evolved into attacks like keyboard eavesdropping (Asonov and Agrawal 2004). Electrical side channels range from power analysis on cryptographic hardware (Gnad, Oboril, and Tahoori 2017) to voltage fluctuations (Zhao and Suh 2018) on machine learning accelerators. Timing, electromagnetic emission, and even heat footprints can likewise be exploited. New and unexpected side channels often emerge as computing becomes more interconnected and miniaturized.\nJust as MI5’s analog acoustic leak transformed their codebreaking, modern side-channel attacks circumvent traditional boundaries of cyber defense. Understanding the creative spirit and historical persistence of side channel exploits is key knowledge for developers and defenders seeking to secure modern machine learning systems comprehensively against digital and physical threats.\n\n\n14.5.5 Leaky Interfaces\nLeaky interfaces in embedded systems are often overlooked backdoors that can become significant security vulnerabilities. While designed for legitimate purposes such as communication, maintenance, or debugging, these interfaces may inadvertently provide attackers with a window through which they can extract sensitive information or inject malicious data.\nAn interface becomes “leaky” when it exposes more information than it should, often due to a lack of stringent access controls or inadequate shielding of the transmitted data. Here are some real-world examples of leaky interface issues causing security problems in IoT and embedded devices:\n\nBaby Monitors: Many WiFi-enabled baby monitors have been found to have unsecured interfaces for remote access. This allowed attackers to gain live audio and video feeds from people’s homes, representing a major privacy violation.\nPacemakers: Interface vulnerabilities were discovered in some pacemakers that could allow attackers to manipulate cardiac functions if exploited. This presents a potentially life-threatening scenario.\nSmart Lightbulbs: A researcher found he could access unencrypted data from smart lightbulbs via a debug interface, including WiFi credentials, allowing him to gain access to the connected network (Greengard 2015).\nSmart Cars: If left unsecured, The OBD-II diagnostic port has been shown to provide an attack vector into automotive systems. Attackers could use it to control brakes and other components (Miller and Valasek 2015).\n\n\nGreengard, Samuel. 2015. The Internet of Things. The MIT Press. https://doi.org/10.7551/mitpress/10277.001.0001.\n\nMiller, Charlie, and Chris Valasek. 2015. “Remote Exploitation of an Unaltered Passenger Vehicle.” Black Hat USA 2015 (S 91): 1–91.\nWhile the above are not directly connected with ML, consider the example of a smart home system with an embedded ML component that controls home security based on behavior patterns it learns over time. The system includes a maintenance interface accessible via the local network for software updates and system checks. If this interface does not require strong authentication or the data transmitted through it is not encrypted, an attacker on the same network could gain access. They could then eavesdrop on the homeowner’s daily routines or reprogram the security settings by manipulating the firmware.\nSuch leaks are a privacy issue and a potential entry point for more damaging exploits. The exposure of training data, model parameters, or ML outputs from a leak could help adversaries construct adversarial examples or reverse-engineer models. Access through a leaky interface could also be used to alter an embedded device’s firmware, loading it with malicious code that could turn off the device, intercept data, or use it in botnet attacks.\nTo mitigate these risks, a multi-layered approach is necessary, spanning technical controls like authentication, encryption, anomaly detection, policies and processes like interface inventories, access controls, auditing, and secure development practices. Turning off unnecessary interfaces and compartmentalizing risks via a zero-trust model provide additional protection.\nAs designers of embedded ML systems, we should assess interfaces early in development and continually monitor them post-deployment as part of an end-to-end security lifecycle. Understanding and securing interfaces is crucial for ensuring the overall security of embedded ML.\n\n\n14.5.6 Counterfeit Hardware\nML systems are only as reliable as the underlying hardware. In an era where hardware components are global commodities, the rise of counterfeit or cloned hardware presents a significant challenge. Counterfeit hardware encompasses any components that are unauthorized reproductions of original parts. Counterfeit components infiltrate ML systems through complex supply chains that stretch across borders and involve numerous stages from manufacture to delivery.\nA single lapse in the supply chain’s integrity can result in the insertion of counterfeit parts designed to closely imitate the functions and appearance of genuine hardware. For instance, a facial recognition system for high-security access control may be compromised if equipped with counterfeit processors. These processors could fail to accurately process and verify biometric data, potentially allowing unauthorized individuals to access restricted areas.\nThe challenge with counterfeit hardware is multifaceted. It undermines the quality and reliability of ML systems, as these components may degrade faster or perform unpredictably due to substandard manufacturing. The security risks are also profound; counterfeit hardware can contain vulnerabilities ripe for exploitation by malicious actors. For example, a cloned network router in an ML data center might include a hidden backdoor, enabling data interception or network intrusion without detection.\nFurthermore, counterfeit hardware poses legal and compliance risks. Companies inadvertently utilizing counterfeit parts in their ML systems may face serious legal repercussions, including fines and sanctions for failing to comply with industry regulations and standards. This is particularly true for sectors where compliance with specific safety and privacy regulations is mandatory, such as healthcare and finance.\nThe issue of counterfeit hardware is exacerbated by economic pressures to reduce costs, which can compel businesses to source from lower-cost suppliers without stringent verification processes. This economizing can inadvertently introduce counterfeit parts into otherwise secure systems. Additionally, detecting these counterfeits is inherently difficult since they are created to pass as the original components, often requiring sophisticated equipment and expertise to identify.\nIn ML, where decisions are made in real time and based on complex computations, the consequences of hardware failure are inconvenient and potentially dangerous. Stakeholders in the field of ML need to understand these risks thoroughly. The issues presented by counterfeit hardware necessitate a deep dive into the current challenges facing ML system integrity and emphasize the importance of vigilant, informed management of the hardware life cycle within these advanced systems.\n\n\n14.5.7 Supply Chain Risks\nThe threat of counterfeit hardware is closely tied to broader supply chain vulnerabilities. Globalized, interconnected supply chains create multiple opportunities for compromised components to infiltrate a product’s lifecycle. Supply chains involve numerous entities, from design to manufacturing, assembly, distribution, and integration. A lack of transparency and oversight of each partner makes verifying integrity at every step challenging. Lapses anywhere along the chain can allow the insertion of counterfeit parts.\nFor example, a contracted manufacturer may unknowingly receive and incorporate recycled electronic waste containing dangerous counterfeits. An untrustworthy distributor could smuggle in cloned components. Insider threats at any vendor might deliberately mix counterfeits into legitimate shipments.\nOnce counterfeits enter the supply stream, they move quickly through multiple hands before ending up in ML systems where detection is difficult. Advanced counterfeits like refurbished parts or clones with repackaged externals can masquerade as authentic components, passing visual inspection.\nTo identify fakes, thorough technical profiling using micrography, X-ray screening, component forensics, and functional testing is often required. However, such costly analysis is impractical for large-volume procurement.\nStrategies like supply chain audits, screening suppliers, validating component provenance, and adding tamper-evident protections can help mitigate risks. However, given global supply chain security challenges, a zero-trust approach is prudent. Designing ML systems to use redundant checking, fail-safes, and continuous runtime monitoring provides resilience against component compromises.\nRigorous validation of hardware sources coupled with fault-tolerant system architectures offers the most robust defense against the pervasive risks of convoluted, opaque global supply chains.\n\n\n14.5.8 Case Study\nIn 2018, Bloomberg Businessweek published an alarming story that got much attention in the tech world. The article claimed that Supermicro had secretly planted tiny spy chips on server hardware. Reporters said Chinese state hackers working with Supermicro could sneak these tiny chips onto motherboards during manufacturing. The tiny chips allegedly gave the hackers backdoor access to servers used by over 30 major companies, including Apple and Amazon.\nIf true, this would allow hackers to spy on private data or even tamper with systems. However, after investigating, Apple and Amazon found no proof that such hacked Supermicro hardware existed. Other experts questioned whether the Bloomberg article was accurate reporting.\nWhether the story is completely true or not is not our concern from a pedagogical viewpoint. However, this incident drew attention to the risks of global supply chains for hardware, especially manufactured in China. When companies outsource and buy hardware components from vendors worldwide, there needs to be more visibility into the process. In this complex global pipeline, there are concerns that counterfeits or tampered hardware could be slipped in somewhere along the way without tech companies realizing it. Companies relying too much on single manufacturers or distributors creates risk. For instance, due to the over-reliance on TSMC for semiconductor manufacturing, the U.S. has invested 50 billion dollars into the CHIPS Act.\nAs ML moves into more critical systems, verifying hardware integrity from design through production and delivery is crucial. The reported Supermicro backdoor demonstrated that for ML security, we cannot take global supply chains and manufacturing for granted. We must inspect and validate hardware at every link in the chain.", "crumbs": [ "Advanced Topics", "14  Security & Privacy" @@ -1555,7 +1555,7 @@ "href": "contents/privacy_security/privacy_security.html#privacy-concerns-in-data-handling", "title": "14  Security & Privacy", "section": "14.7 Privacy Concerns in Data Handling", - "text": "14.7 Privacy Concerns in Data Handling\nHandling personal and sensitive data securely and ethically is critical as machine learning permeates devices like smartphones, wearables, and smart home appliances. For medical hardware, handling data securely and ethically is further required by law through the Health Insurance Portability and Accountability Act (HIPAA). These embedded ML systems pose unique privacy risks, given their intimate proximity to users’ lives.\n\n14.7.1 Sensitive Data Types\nEmbedded ML devices like wearables, smart home assistants, and autonomous vehicles frequently process highly personal data that requires careful handling to maintain user privacy and prevent misuse. Specific examples include medical reports and treatment plans processed by health wearables, private conversations continuously captured by smart home assistants, and detailed driving habits collected by connected cars. Compromise of such sensitive data can lead to serious consequences like identity theft, emotional manipulation, public shaming, and mass surveillance overreach.\nSensitive data takes many forms - structured records like contact lists and unstructured content like conversational audio and video streams. In medical settings, protected health information (PHI) is collected by doctors throughout every interaction and is heavily regulated by strict HIPAA guidelines. Even outside of medical settings, sensitive data can still be collected in the form of Personally Identifiable Information (PII), which is defined as “any representation of information that permits the identity of an individual to whom the information applies to be reasonably inferred by either direct or indirect means.” Examples of PII include email addresses, social security numbers, and phone numbers, among other fields. PII is collected in medical settings and other settings (financial applications, etc) and is heavily regulated by Department of Labor policies.\nEven derived model outputs could indirectly leak details about individuals. Beyond just personal data, proprietary algorithms and datasets also warrant confidentiality protections. In the Data Engineering section, we covered several topics in detail.\nTechniques like de-identification, aggregation, anonymization, and federation can help transform sensitive data into less risky forms while retaining analytical utility. However, diligent controls around access, encryption, auditing, consent, minimization, and compliance practices are still essential throughout the data lifecycle. Regulations like GDPR categorize different classes of sensitive data and prescribe responsibilities around their ethical handling. Standards like NIST 800-53 provide rigorous security control guidance for confidentiality protection. With growing reliance on embedded ML, understanding sensitive data risks is crucial.\n\n\n14.7.2 Applicable Regulations\nMany embedded ML applications handle sensitive user data under HIPAA, GDPR, and CCPA regulations. Understanding the protections mandated by these laws is crucial for building compliant systems.\n\nHIPAA Privacy Rule establishes care providers that conduct certain governs medical data privacy and security in the US, with severe penalties for violations. Any health-related embedded ML devices like diagnostic wearables or assistive robots would need to implement controls like audit trails, access controls, and encryption prescribed by HIPAA.\nGDPR imposes transparency, retention limits, and user rights on EU citizen data, even when processed by companies outside the EU. Smart home systems capturing family conversations or location patterns would need GDPR compliance. Key requirements include data minimization, encryption, and mechanisms for consent and erasure.\nCCPA, which applies in California, protects consumer data privacy through provisions like required disclosures and opt-out rights—ioT gadgets like smart speakers and fitness trackers Californians use likely to fall under its scope.\nThe CCPA was the first state-specific set of regulations regarding privacy concerns. Following the CCPA, similar regulations were also enacted in 10 other states, with some states proposing bills for consumer data privacy protections.\n\nAdditionally, when relevant to the application, sector-specific rules govern telematics, financial services, utilities, etc. Best practices like Privacy by design, impact assessments, and maintaining audit trails help embed compliance if it is not already required by law. Given potentially costly penalties, consulting legal/compliance teams is advisable when developing regulated embedded ML systems.\n\n\n14.7.3 De-identification\nIf medical data is de-identified thoroughly, HIPAA guidelines do not directly apply, and there are far fewer regulations. However, medical data needs to be de-identified using HIPAA methods (Safe Harbor methods or Expert Determination methods) for HIPAA guidelines to no longer apply.\n\nSafe Harbor Methods\nSafe Harbor methods are most commonly used for de-identifying protected healthcare information due to the limited resources needed compared to Expert Determination methods. Safe Harbor de-identification requires scrubbing datasets of any data that falls into one of 18 categories. The following categories are listed as sensitive information based on the Safe Harbor standard:\n\nName, Geographic locator, Birthdate, Phone Number, Email Address, addresses, Social Security Numbers, Medical Record Numbers, health beneficiary Numbers, Device Identifiers and Serial Numbers, Certificate/License Numbers (Birth Certificate, Drivers License, etc), Account Numbers, Vehicle Identifiers, Website URLs, FullFace Photos and Comparable Images, Biometric Identifiers, Any other unique identifiers\n\nFor most of these categories, all data must be removed regardless of the circumstances. For other categories, including geographical information and birthdate, the data can be partially removed enough to make the information hard to re-identify. For example, if a zip code is large enough, the first 3 digits can remain since there are enough people in the geographic area to make re-identification difficult. Birthdates need to be scrubbed of all elements except birth year, and all ages above 89 need to be aggregated into a 90+ category.\n\n\nExpert Determination Methods\nSafe Harbor methods work for several cases of medical data de-identification, though re-identification is still possible in some cases. For example, let’s say you collect data on a patient in an urban city with a large zip code, but you have documented a rare disease that they have—a disease that only 25 people have in the entire city. Given geographic data coupled with birth year, it is highly possible that someone can re-identify this individual, which is an extremely detrimental privacy breach.\nIn unique cases like these, expert determination data de-identification methods are preferred. Expert determination de-identification requires a “person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable” to evaluate a dataset and determine if the risk of re-identification of individual data in a given dataset in combination with publicly available data (voting records, etc.), is extremely small.\nExpert Determination de-identification is understandably harder to complete than Safe Harbour de-identification due to the cost and feasibility of accessing an expert to verify the likelihood of re-identifying a dataset. However, in many cases, expert determination is required to ensure that re-identification of data is extremely unlikely.\n\n\n\n14.7.4 Data Minimization\nData minimization involves collecting, retaining, and processing only the necessary user data to reduce privacy risks from embedded ML systems. This starts by restricting the data types and instances gathered to the bare minimum required for the system’s core functionality. For example, an object detection model only collects the images needed for that specific computer vision task. Similarly, a voice assistant would limit audio capture to specific spoken commands rather than persistently recording ambient sounds.\nWhere possible, temporary data that briefly resides in memory without persisting storage provides additional minimization. A clear legal basis, like user consent, should be established for collection and retention. Sandboxing and access controls prevent unauthorized use beyond intended tasks. Retention periods should be defined based on purpose, with secure deletion procedures removing expired data.\nData minimization can be broken down into 3 categories:\n\n“Data must be adequate about the purpose that is pursued.” Data omission can limit the accuracy of models trained on the data and any general usefulness of a dataset. Data minimization requires a minimum amount of data to be collected from users while creating a dataset that adds value to others.\nThe data collected from users must be relevant to the purpose of the data collection.\nUsers’ data should be limited to only the necessary data to fulfill the purpose of the initial data collection. If similarly robust and accurate results can be obtained from a smaller dataset, any additional data beyond this smaller dataset should not be collected.\n\nEmerging techniques like differential Privacy, federated learning, and synthetic data generation allow useful insights derived from less raw user data. Performing data flow mapping and impact assessments helps identify opportunities to minimize raw data usage.\nMethodologies like Privacy by Design (Cavoukian 2009) consider such minimization early in system architecture. Regulations like GDPR also mandate data minimization principles. With a multilayered approach across legal, technical, and process realms, data minimization limits risks in embedded ML products.\n\nCavoukian, Ann. 2009. “Privacy by Design.” Office of the Information and Privacy Commissioner.\n\nCase Study - Performance-Based Data Minimization\nPerformance-based data minimization (Biega et al. 2020) focuses on expanding upon the third category of data minimization mentioned above, namely limitation. It specifically defines the robustness of model results on a given dataset by certain performance metrics, such that data should not be additionally collected if it does not significantly improve performance. Performance metrics can be divided into two categories:\n\nBiega, Asia J., Peter Potash, Hal Daumé, Fernando Diaz, and Michèle Finck. 2020. “Operationalizing the Legal Principle of Data Minimization for Personalization.” In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, edited by Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu, 399–408. ACM. https://doi.org/10.1145/3397271.3401034.\n\nGlobal data minimization performance\n\n\nSatisfied if a dataset minimizes the amount of per-user data while its mean performance across all data is comparable to the mean performance of the original, unminimized dataset.\n\n\nPer user data minimization performance\n\n\nSatisfied if a dataset minimizes the amount of per-user data while the minimum performance of individual user data is comparable to that of individual user data in the original, unminimized dataset.\n\nPerformance-based data minimization can be leveraged in machine-learning settings, including movie recommendation algorithms and e-commerce settings.\nGlobal data minimization is much more feasible than per-user data minimization, given the much more significant difference in per-user losses between the minimized and original datasets.\n\n\n\n14.7.5 Consent and Transparency\nMeaningful consent and transparency are crucial when collecting user data for embedded ML products like smart speakers, wearables, and autonomous vehicles. When first set up. Ideally, the device should clearly explain what data types are gathered, for what purposes, how they are processed, and retention policies. For example, a smart speaker might collect voice samples to train speech recognition and personalized voice profiles. During use, reminders and dashboard options provide ongoing transparency into how data is handled, such as weekly digests of captured voice snippets. Control options allow revoking or limiting consent, like turning off the storage of voice profiles.\nConsent flows should provide granular controls beyond just binary yes/no choices. For instance, users could selectively consent to certain data uses, such as training speech recognition, but not personalization. Focus groups and usability testing with target users shape consent interfaces and wording of privacy policies to optimize comprehension and control. Respecting user rights, such as data deletion and rectification, demonstrates trustworthiness. Vague legal jargon hampers transparency. Regulations like GDPR and CCPA reinforce consent requirements. Thoughtful consent and transparency provide users agency over their data while building trust in embedded ML products through open communication and control.\n\n\n14.7.6 Privacy Concerns in Machine Learning\n\nGenerative AI\nPrivacy and security concerns have also risen with the public use of generative AI models, including OpenAI’s GPT4 and other LLMs. ChatGPT, in particular, has been discussed more recently about Privacy, given all the personal information collected from ChatGPT users. In June, a class action lawsuit was filed against ChatGPT due to concerns that it was trained on proprietary medical and personal information without proper permissions or consent. As a result of these privacy concerns, many companies have prohibited their employees from accessing ChatGPT, and uploading private, company related information to the chatbot. Further, ChatGPT is susceptible to prompt injection and other security attacks that could compromise the privacy of the proprietary data upon which it was trained.\n\nCase Study\nWhile ChatGPT has instituted protections to prevent people from accessing private and ethically questionable information, several individuals have successfully bypassed these protections through prompt injection and other security attacks. As demonstrated in Figure 14.9, users can bypass ChatGPT protections to mimic the tone of a “deceased grandmother” to learn how to bypass a web application firewall (Gupta et al. 2023).\n\n\n\n\n\n\nFigure 14.9: Grandma role play to bypass safety restrictions. Source: Gupta et al. (2023).\n\n\n\nFurther, users have also successfully used reverse psychology to manipulate ChatGPT and access information initially prohibited by the model. In Figure 14.10, a user is initially prevented from learning about piracy websites through ChatGPT but can bypass these restrictions using reverse psychology.\n\n\n\n\n\n\nFigure 14.10: Reverse psychology to bypass safety restrictions. Source: Gupta et al. (2023).\n\n\nGupta, Maanak, Charankumar Akiri, Kshitiz Aryal, Eli Parker, and Lopamudra Praharaj. 2023. “From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy.” #IEEE_O_ACC# 11: 80218–45. https://doi.org/10.1109/access.2023.3300381.\n\n\nThe ease at which security attacks can manipulate ChatGPT is concerning, given the private information it was trained upon without consent. Further research on data privacy in LLMs and generative AI should focus on preventing the model from being so naive to prompt injection attacks.\n\n\n\nData Erasure\nMany previous regulations mentioned above, including GDPR, include a “right to be forgotten” clause. This clause essentially states that “the data subject shall have the right to obtain from the controller the erasure of personal data concerning him or her without undue delay.” However, in several cases, even if user data has been erased from a platform, the data is only partially erased if a machine learning model has been trained on this data for separate purposes. Through methods similar to membership inference attacks, other individuals can still predict the training data a model was trained upon, even if the data’s presence was explicitly removed online.\nOne approach to addressing privacy concerns with machine learning training data has been through differential privacy methods. For example, by adding Laplacian noise in the training set, a model can be robust to membership inference attacks, preventing deleted data from being recovered. Another approach to preventing deleted data from being inferred from security attacks is simply retraining the model from scratch on the remaining data. Since this process is time-consuming and computationally expensive, other researchers have attempted to address privacy concerns surrounding inferring model training data through a process called machine unlearning, in which a model actively iterates on itself to remove the influence of “forgotten” data that it might have been trained on, as mentioned below.", + "text": "14.7 Privacy Concerns in Data Handling\nHandling personal and sensitive data securely and ethically is critical as machine learning permeates devices like smartphones, wearables, and smart home appliances. For medical hardware, handling data securely and ethically is further required by law through the Health Insurance Portability and Accountability Act (HIPAA). These embedded ML systems pose unique privacy risks, given their intimate proximity to users’ lives.\n\n14.7.1 Sensitive Data Types\nEmbedded ML devices like wearables, smart home assistants, and autonomous vehicles frequently process highly personal data that requires careful handling to maintain user privacy and prevent misuse. Specific examples include medical reports and treatment plans processed by health wearables, private conversations continuously captured by smart home assistants, and detailed driving habits collected by connected cars. Compromise of such sensitive data can lead to serious consequences like identity theft, emotional manipulation, public shaming, and mass surveillance overreach.\nSensitive data takes many forms - structured records like contact lists and unstructured content like conversational audio and video streams. In medical settings, protected health information (PHI) is collected by doctors throughout every interaction and is heavily regulated by strict HIPAA guidelines. Even outside of medical settings, sensitive data can still be collected in the form of Personally Identifiable Information (PII), which is defined as “any representation of information that permits the identity of an individual to whom the information applies to be reasonably inferred by either direct or indirect means.” Examples of PII include email addresses, social security numbers, and phone numbers, among other fields. PII is collected in medical settings and other settings (financial applications, etc) and is heavily regulated by Department of Labor policies.\nEven derived model outputs could indirectly leak details about individuals. Beyond just personal data, proprietary algorithms and datasets also warrant confidentiality protections. In the Data Engineering section, we covered several topics in detail.\nTechniques like de-identification, aggregation, anonymization, and federation can help transform sensitive data into less risky forms while retaining analytical utility. However, diligent controls around access, encryption, auditing, consent, minimization, and compliance practices are still essential throughout the data lifecycle. Regulations like GDPR categorize different classes of sensitive data and prescribe responsibilities around their ethical handling. Standards like NIST 800-53 provide rigorous security control guidance for confidentiality protection. With growing reliance on embedded ML, understanding sensitive data risks is crucial.\n\n\n14.7.2 Applicable Regulations\nMany embedded ML applications handle sensitive user data under HIPAA, GDPR, and CCPA regulations. Understanding the protections mandated by these laws is crucial for building compliant systems.\n\nHIPAA Privacy Rule establishes care providers that conduct certain governs medical data privacy and security in the US, with severe penalties for violations. Any health-related embedded ML devices like diagnostic wearables or assistive robots would need to implement controls like audit trails, access controls, and encryption prescribed by HIPAA.\nGDPR imposes transparency, retention limits, and user rights on EU citizen data, even when processed by companies outside the EU. Smart home systems capturing family conversations or location patterns would need GDPR compliance. Key requirements include data minimization, encryption, and mechanisms for consent and erasure.\nCCPA, which applies in California, protects consumer data privacy through provisions like required disclosures and opt-out rights—ioT gadgets like smart speakers and fitness trackers Californians use likely to fall under its scope.\nThe CCPA was the first state-specific set of regulations regarding privacy concerns. Following the CCPA, similar regulations were also enacted in 10 other states, with some states proposing bills for consumer data privacy protections.\n\nAdditionally, when relevant to the application, sector-specific rules govern telematics, financial services, utilities, etc. Best practices like Privacy by design, impact assessments, and maintaining audit trails help embed compliance if it is not already required by law. Given potentially costly penalties, consulting legal/compliance teams is advisable when developing regulated embedded ML systems.\n\n\n14.7.3 De-identification\nIf medical data is de-identified thoroughly, HIPAA guidelines do not directly apply, and there are far fewer regulations. However, medical data needs to be de-identified using HIPAA methods (Safe Harbor methods or Expert Determination methods) for HIPAA guidelines to no longer apply.\n\nSafe Harbor Methods\nSafe Harbor methods are most commonly used for de-identifying protected healthcare information due to the limited resources needed compared to Expert Determination methods. Safe Harbor de-identification requires scrubbing datasets of any data that falls into one of 18 categories. The following categories are listed as sensitive information based on the Safe Harbor standard:\n\nName, Geographic locator, Birthdate, Phone Number, Email Address, addresses, Social Security Numbers, Medical Record Numbers, health beneficiary Numbers, Device Identifiers and Serial Numbers, Certificate/License Numbers (Birth Certificate, Drivers License, etc), Account Numbers, Vehicle Identifiers, Website URLs, FullFace Photos and Comparable Images, Biometric Identifiers, Any other unique identifiers\n\nFor most of these categories, all data must be removed regardless of the circumstances. For other categories, including geographical information and birthdate, the data can be partially removed enough to make the information hard to re-identify. For example, if a zip code is large enough, the first 3 digits can remain since there are enough people in the geographic area to make re-identification difficult. Birthdates need to be scrubbed of all elements except birth year, and all ages above 89 need to be aggregated into a 90+ category.\n\n\nExpert Determination Methods\nSafe Harbor methods work for several cases of medical data de-identification, though re-identification is still possible in some cases. For example, let’s say you collect data on a patient in an urban city with a large zip code, but you have documented a rare disease that they have—a disease that only 25 people have in the entire city. Given geographic data coupled with birth year, it is highly possible that someone can re-identify this individual, which is an extremely detrimental privacy breach.\nIn unique cases like these, expert determination data de-identification methods are preferred. Expert determination de-identification requires a “person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable” to evaluate a dataset and determine if the risk of re-identification of individual data in a given dataset in combination with publicly available data (voting records, etc.), is extremely small.\nExpert Determination de-identification is understandably harder to complete than Safe Harbour de-identification due to the cost and feasibility of accessing an expert to verify the likelihood of re-identifying a dataset. However, in many cases, expert determination is required to ensure that re-identification of data is extremely unlikely.\n\n\n\n14.7.4 Data Minimization\nData minimization involves collecting, retaining, and processing only the necessary user data to reduce privacy risks from embedded ML systems. This starts by restricting the data types and instances gathered to the bare minimum required for the system’s core functionality. For example, an object detection model only collects the images needed for that specific computer vision task. Similarly, a voice assistant would limit audio capture to specific spoken commands rather than persistently recording ambient sounds.\nWhere possible, temporary data that briefly resides in memory without persisting storage provides additional minimization. A clear legal basis, like user consent, should be established for collection and retention. Sandboxing and access controls prevent unauthorized use beyond intended tasks. Retention periods should be defined based on purpose, with secure deletion procedures removing expired data.\nData minimization can be broken down into 3 categories:\n\n“Data must be adequate about the purpose that is pursued.” Data omission can limit the accuracy of models trained on the data and any general usefulness of a dataset. Data minimization requires a minimum amount of data to be collected from users while creating a dataset that adds value to others.\nThe data collected from users must be relevant to the purpose of the data collection.\nUsers’ data should be limited to only the necessary data to fulfill the purpose of the initial data collection. If similarly robust and accurate results can be obtained from a smaller dataset, any additional data beyond this smaller dataset should not be collected.\n\nEmerging techniques like differential Privacy, federated learning, and synthetic data generation allow useful insights derived from less raw user data. Performing data flow mapping and impact assessments helps identify opportunities to minimize raw data usage.\nMethodologies like Privacy by Design (Cavoukian 2009) consider such minimization early in system architecture. Regulations like GDPR also mandate data minimization principles. With a multilayered approach across legal, technical, and process realms, data minimization limits risks in embedded ML products.\n\nCavoukian, Ann. 2009. “Privacy by Design.” Office of the Information and Privacy Commissioner.\n\nCase Study - Performance-Based Data Minimization\nPerformance-based data minimization (Biega et al. 2020) focuses on expanding upon the third category of data minimization mentioned above, namely limitation. It specifically defines the robustness of model results on a given dataset by certain performance metrics, such that data should not be additionally collected if it does not significantly improve performance. Performance metrics can be divided into two categories:\n\nBiega, Asia J., Peter Potash, Hal Daumé, Fernando Diaz, and Michèle Finck. 2020. “Operationalizing the Legal Principle of Data Minimization for Personalization.” In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, edited by Jimmy Huang, Yi Chang, Xueqi Cheng, Jaap Kamps, Vanessa Murdock, Ji-Rong Wen, and Yiqun Liu, 399–408. ACM. https://doi.org/10.1145/3397271.3401034.\n\nGlobal data minimization performance\n\n\nSatisfied if a dataset minimizes the amount of per-user data while its mean performance across all data is comparable to the mean performance of the original, unminimized dataset.\n\n\nPer user data minimization performance\n\n\nSatisfied if a dataset minimizes the amount of per-user data while the minimum performance of individual user data is comparable to that of individual user data in the original, unminimized dataset.\n\nPerformance-based data minimization can be leveraged in machine-learning settings, including movie recommendation algorithms and e-commerce settings.\nGlobal data minimization is much more feasible than per-user data minimization, given the much more significant difference in per-user losses between the minimized and original datasets.\n\n\n\n14.7.5 Consent and Transparency\nMeaningful consent and transparency are crucial when collecting user data for embedded ML products like smart speakers, wearables, and autonomous vehicles. When first set up. Ideally, the device should clearly explain what data types are gathered, for what purposes, how they are processed, and retention policies. For example, a smart speaker might collect voice samples to train speech recognition and personalized voice profiles. During use, reminders and dashboard options provide ongoing transparency into how data is handled, such as weekly digests of captured voice snippets. Control options allow revoking or limiting consent, like turning off the storage of voice profiles.\nConsent flows should provide granular controls beyond just binary yes/no choices. For instance, users could selectively consent to certain data uses, such as training speech recognition, but not personalization. Focus groups and usability testing with target users shape consent interfaces and wording of privacy policies to optimize comprehension and control. Respecting user rights, such as data deletion and rectification, demonstrates trustworthiness. Vague legal jargon hampers transparency. Regulations like GDPR and CCPA reinforce consent requirements. Thoughtful consent and transparency provide users agency over their data while building trust in embedded ML products through open communication and control.\n\n\n14.7.6 Privacy Concerns in Machine Learning\n\nGenerative AI\nPrivacy and security concerns have also risen with the public use of generative AI models, including OpenAI’s GPT4 and other LLMs. ChatGPT, in particular, has been discussed more recently about Privacy, given all the personal information collected from ChatGPT users. In June 2023, a class action lawsuit was filed against ChatGPT due to concerns that it was trained on proprietary medical and personal information without proper permissions or consent. As a result of these privacy concerns, many companies have prohibited their employees from accessing ChatGPT, and uploading private, company related information to the chatbot. Further, ChatGPT is susceptible to prompt injection and other security attacks that could compromise the privacy of the proprietary data upon which it was trained.\n\nCase Study\nWhile ChatGPT has instituted protections to prevent people from accessing private and ethically questionable information, several individuals have successfully bypassed these protections through prompt injection and other security attacks. As demonstrated in Figure 14.9, users can bypass ChatGPT protections to mimic the tone of a “deceased grandmother” to learn how to bypass a web application firewall (Gupta et al. 2023).\n\n\n\n\n\n\nFigure 14.9: Grandma role play to bypass safety restrictions. Source: Gupta et al. (2023).\n\n\n\nFurther, users have also successfully used reverse psychology to manipulate ChatGPT and access information initially prohibited by the model. In Figure 14.10, a user is initially prevented from learning about piracy websites through ChatGPT but can bypass these restrictions using reverse psychology.\n\n\n\n\n\n\nFigure 14.10: Reverse psychology to bypass safety restrictions. Source: Gupta et al. (2023).\n\n\nGupta, Maanak, Charankumar Akiri, Kshitiz Aryal, Eli Parker, and Lopamudra Praharaj. 2023. “From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy.” #IEEE_O_ACC# 11: 80218–45. https://doi.org/10.1109/access.2023.3300381.\n\n\nThe ease at which security attacks can manipulate ChatGPT is concerning, given the private information it was trained upon without consent. Further research on data privacy in LLMs and generative AI should focus on preventing the model from being so naive to prompt injection attacks.\n\n\n\nData Erasure\nMany previous regulations mentioned above, including GDPR, include a “right to be forgotten” clause. This clause essentially states that “the data subject shall have the right to obtain from the controller the erasure of personal data concerning him or her without undue delay.” However, in several cases, even if user data has been erased from a platform, the data is only partially erased if a machine learning model has been trained on this data for separate purposes. Through methods similar to membership inference attacks, other individuals can still predict the training data a model was trained upon, even if the data’s presence was explicitly removed online.\nOne approach to addressing privacy concerns with machine learning training data has been through differential privacy methods. For example, by adding Laplacian noise in the training set, a model can be robust to membership inference attacks, preventing deleted data from being recovered. Another approach to preventing deleted data from being inferred from security attacks is simply retraining the model from scratch on the remaining data. Since this process is time-consuming and computationally expensive, other researchers have attempted to address privacy concerns surrounding inferring model training data through a process called machine unlearning, in which a model actively iterates on itself to remove the influence of “forgotten” data that it might have been trained on, as mentioned below.", "crumbs": [ "Advanced Topics", "14  Security & Privacy" @@ -1566,7 +1566,7 @@ "href": "contents/privacy_security/privacy_security.html#privacy-preserving-ml-techniques", "title": "14  Security & Privacy", "section": "14.8 Privacy-Preserving ML Techniques", - "text": "14.8 Privacy-Preserving ML Techniques\nMany techniques have been developed to preserve privacy, each addressing different aspects and data security challenges. These methods can be broadly categorized into several key areas: Differential Privacy, which focuses on statistical privacy in data outputs; Federated Learning, emphasizing decentralized data processing; Homomorphic Encryption and Secure Multi-party Computation (SMC), both enabling secure computations on encrypted or private data; Data Anonymization and Data Masking and Obfuscation, which alter data to protect individual identities; Private Set Intersection and Zero-Knowledge Proofs, facilitating secure data comparisons and validations; Decentralized Identifiers (DIDs) for self-sovereign digital identities; Privacy-Preserving Record Linkage (PPRL), linking data across sources without exposure; Synthetic Data Generation, creating artificial datasets for safe analysis; and Adversarial Learning Techniques, enhancing data or model resistance to privacy attacks.\nGiven the extensive range of these techniques, it is not feasible to dive into each in depth within a single course or discussion, let alone for anyone to know it all in its glorious detail. Therefore, we will explore a few specific techniques in relative detail, providing a deeper understanding of their principles, applications, and the unique privacy challenges they address in machine learning. This focused approach will give us a more comprehensive and practical understanding of key privacy-preserving methods in modern ML systems.\n\n14.8.1 Differential Privacy\n\nCore Idea\nDifferential Privacy is a framework for quantifying and managing the privacy of individuals in a dataset (Dwork et al. 2006). It provides a mathematical guarantee that the privacy of individuals in the dataset will not be compromised, regardless of any additional knowledge an attacker may possess. The core idea of differential Privacy is that the outcome of any analysis (like a statistical query) should be essentially the same, whether any individual’s data is included in the dataset or not. This means that by observing the analysis result, one cannot determine whether any individual’s data was used in the computation.\n\nDwork, Cynthia, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. “Calibrating Noise to Sensitivity in Private Data Analysis.” In Theory of Cryptography, edited by Shai Halevi and Tal Rabin, 265–84. Berlin, Heidelberg: Springer Berlin Heidelberg.\nFor example, let’s say a database contains medical records for 10 patients. We want to release statistics about the prevalence of diabetes in this sample without revealing one patient’s condition. To do this, we could add a small amount of random noise to the true count before releasing it. If the true number of diabetes patients is 6, we might add noise from a Laplace distribution to randomly output 5, 6, or 7 each with some probability. An observer now can’t tell if any single patient has diabetes based only on the noisy output. The query result looks similar to whether each patient’s data is included or excluded. This is differential Privacy. More formally, a randomized algorithm satisfies ε-differential Privacy if, for any neighbor databases D and Dʹ differing by only one entry, the probability of any outcome changes by at most a factor of ε. A lower ε provides stronger privacy guarantees.\nThe Laplace Mechanism is one of the most straightforward and commonly used methods to achieve differential Privacy. It involves adding noise that follows a Laplace distribution to the data or query results. Apart from the Laplace Mechanism, the general principle of adding noise is central to differential Privacy. The idea is to add random noise to the data or the results of a query. The noise is calibrated to ensure the necessary privacy guarantee while keeping the data useful.\nWhile the Laplace distribution is common, other distributions like Gaussian can also be used. Laplace noise is used for strict ε-differential Privacy for low-sensitivity queries. In contrast, Gaussian distributions can be used when Privacy is not guaranteed, known as (ϵ, 𝛿)-Differential Privacy. In this relaxed version of differential Privacy, epsilon and delta define the amount of Privacy guaranteed when releasing information or a model related to a dataset. Epsilon sets a bound on how much information can be learned about the data based on the output. At the same time, delta allows for a small probability of the privacy guarantee to be violated. The choice between Laplace, Gaussian, and other distributions will depend on the specific requirements of the query and the dataset and the tradeoff between Privacy and accuracy.\nTo illustrate the tradeoff of Privacy and accuracy in (\\(\\epsilon\\), \\(\\delta\\))-differential Privacy, the following graphs in Figure 14.11 show the results on accuracy for different noise levels on the MNIST dataset, a large dataset of handwritten digits (Abadi et al. 2016). The delta value (black line; right y-axis) denotes the level of privacy relaxation (a high value means Privacy is less stringent). As Privacy becomes more relaxed, the accuracy of the model increases.\n\n\n\n\n\n\nFigure 14.11: Privacy-accuracy tradeoff. Source: Abadi et al. (2016).\n\n\nAbadi, Martin, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. “Deep Learning with Differential Privacy.” In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–18. CCS ’16. New York, NY, USA: ACM. https://doi.org/10.1145/2976749.2978318.\n\n\nThe key points to remember about differential Privacy are the following:\n\nAdding Noise: The fundamental technique in differential Privacy is adding controlled random noise to the data or query results. This noise masks the contribution of individual data points.\nBalancing Act: There’s a balance between Privacy and accuracy. More noise (lower ϵ) in the data means higher Privacy but less accuracy in the model’s results.\nUniversality: Differential Privacy doesn’t rely on assumptions about what an attacker knows. This makes it robust against re-identification attacks, where an attacker tries to uncover individual data.\nApplicability: It can be applied to various types of data and queries, making it a versatile tool for privacy-preserving data analysis.\n\n\n\nTradeoffs\nThere are several tradeoffs to make with differential Privacy, as is the case with any algorithm. But let’s focus on the computational-specific tradeoffs since we care about ML systems. There are some key computational considerations and tradeoffs when implementing differential Privacy in a machine-learning system:\nNoise generation: Implementing differential Privacy introduces several important computational tradeoffs compared to standard machine learning techniques. One major consideration is the need to securely generate random noise from distributions like Laplace or Gaussian that get added to query results and model outputs. High-quality cryptographic random number generation can be computationally expensive.\nSensitivity analysis: Another key requirement is rigorously tracking the sensitivity of the underlying algorithms to single data points getting added or removed. This global sensitivity analysis is required to calibrate the noise levels properly. However, analyzing worst-case sensitivity can substantially increase computational complexity for complex model training procedures and data pipelines.\nPrivacy budget management: Managing the privacy loss budget across multiple queries and learning iterations is another bookkeeping overhead. The system must keep track of cumulative privacy costs and compose them to explain overall privacy guarantees. This adds a computational burden beyond just running queries or training models.\nBatch vs. online tradeoffs: For online learning systems with continuous high-volume queries, differentially private algorithms require new mechanisms to maintain utility and prevent too much accumulated privacy loss since each query can potentially alter the privacy budget. Batch offline processing is simpler from a computational perspective as it processes data in large batches, where each batch is treated as a single query. High-dimensional sparse data also increases sensitivity analysis challenges.\nDistributed training: When training models using distributed or federated approaches, new cryptographic protocols are needed to track and bound privacy leakage across nodes. Secure multiparty computation with encrypted data for differential Privacy adds substantial computational load.\nWhile differential Privacy provides strong formal privacy guarantees, implementing it rigorously requires additions and modifications to the machine learning pipeline at a computational cost. Managing these overheads while preserving model accuracy remains an active research area.\n\n\nCase Study\nApple’s implementation of differential Privacy in iOS and MacOS provides a prominent real-world example of how differential Privacy can be deployed at large scale. Apple wanted to collect aggregated usage statistics across their ecosystem to improve products and services, but aimed to do so without compromising individual user privacy.\nTo achieve this, they implemented differential privacy techniques directly on user devices to anonymize data points before sending them to Apple servers. Specifically, Apple uses the Laplace mechanism to inject carefully calibrated random noise. For example, suppose a user’s location history contains [Work, Home, Work, Gym, Work, Home]. In that case, the differentially private version might replace the exact locations with a noisy sample like [Gym, Home, Work, Work, Home, Work].\nApple tunes the Laplace noise distribution to provide a high level of Privacy while preserving the utility of aggregated statistics. Increasing noise levels provides stronger privacy guarantees (lower ε values in DP terminology) but can reduce data utility. Apple’s privacy engineers empirically optimized this tradeoff based on their product goals.\nApple obtains high-fidelity aggregated statistics by aggregating hundreds of millions of noisy data points from devices. For instance, they can analyze new iOS apps’ features while masking any user’s app behaviors. On-device computation avoids sending raw data to Apple servers.\nThe system uses hardware-based secure random number generation to sample from the Laplace distribution on devices efficiently. Apple also had to optimize its differentially private algorithms and pipeline to operate under the computational constraints of consumer hardware.\nMultiple third-party audits have verified that Apple’s system provides rigorous differential privacy protections in line with their stated policies. Of course, assumptions around composition over time and potential re-identification risks still apply. Apple’s deployment shows how differential Privacy can be realized in large real-world products when backed by sufficient engineering resources.\n\n\n\n\n\n\nExercise 14.1: Differential Privacy - TensorFlow Privacy\n\n\n\n\n\nWant to train an ML model without compromising anyone’s secrets? Differential Privacy is like a superpower for your data! In this Colab, we’ll use TensorFlow Privacy to add special noise during training. This makes it way harder for anyone to determine if a single person’s data was used, even if they have sneaky ways of peeking at the model.\n\n\n\n\n\n\n\n14.8.2 Federated Learning\n\nCore Idea\nFederated Learning (FL) is a type of machine learning in which a model is built and distributed across multiple devices or servers while keeping the training data localized. It was previously discussed in the Model Optimizations chapter. Still, we will recap it here briefly to complete it and focus on things that pertain to this chapter.\nFL aims to train machine learning models across decentralized networks of devices or systems while keeping all training data localized. Figure 14.12 illustrates this process: each participating device leverages its local data to calculate model updates, which are then aggregated to build an improved global model. However, the raw training data is never directly shared, transferred, or compiled. This privacy-preserving approach allows for the joint development of ML models without centralizing the potentially sensitive training data in one place.\n\n\n\n\n\n\nFigure 14.12: Federated Learning lifecycle. Source: Jin et al. (2020).\n\n\nJin, Yilun, Xiguang Wei, Yang Liu, and Qiang Yang. 2020. “Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective.” arXiv Preprint arXiv:2002.11545.\n\n\nOne of the most common model aggregation algorithms is Federated Averaging (FedAvg), where the global model is created by averaging all of the parameters from local parameters. While FedAvg works well with independent and identically distributed data (IID), alternate algorithms like Federated Proximal (FedProx) are crucial in real-world applications where data is often non-IID. FedProx is designed for the FL process when there is significant heterogeneity in the client updates due to diverse data distributions across devices, computational capabilities, or varied amounts of data.\nBy leaving the raw data distributed and exchanging only temporary model updates, federated learning provides a more secure and privacy-enhancing alternative to traditional centralized machine learning pipelines. This allows organizations and users to benefit collaboratively from shared models while maintaining control and ownership over sensitive data. The decentralized nature of FL also makes it robust to single points of failure.\nImagine a group of hospitals that want to collaborate on a study to predict patient outcomes based on their symptoms. However, they cannot share their patient data due to privacy concerns and regulations like HIPAA. Here’s how Federated Learning can help.\n\nLocal Training: Each hospital trains a machine learning model on patient data. This training happens locally, meaning the data never leaves the hospital’s servers.\nModel Sharing: After training, each hospital only sends the model (specifically, its parameters or weights ) to a central server. It does not send any patient data.\nAggregating Models: The central server aggregates these models from all hospitals into a single, more robust model. This process typically involves averaging the model parameters.\nBenefit: The result is a machine learning model that has learned from a wide range of patient data without sharing sensitive data or removing it from its original location.\n\n\n\nTradeoffs\nThere are several system performance-related aspects of FL in machine learning systems. It would be wise to understand these tradeoffs because there is no “free lunch” for preserving Privacy through FL (Li et al. 2020).\n\nLi, Tian, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. “Federated Learning: Challenges, Methods, and Future Directions.” IEEE Signal Process Mag. 37 (3): 50–60. https://doi.org/10.1109/msp.2020.2975749.\nCommunication Overhead and Network Constraints: In FL, one of the most significant challenges is managing the communication overhead. This involves the frequent transmission of model updates between a central server and numerous client devices, which can be bandwidth-intensive. The total number of communication rounds and the size of transmitted messages per round need to be reduced to minimize communication further. This can lead to substantial network traffic, especially in scenarios with many participants. Additionally, latency becomes a critical factor — the time taken for these updates to be sent, aggregated, and redistributed can introduce delays. This affects the overall training time and impacts the system’s responsiveness and real-time capabilities. Managing this communication while minimizing bandwidth usage and latency is crucial for implementing FL.\nComputational Load on Local Devices: FL relies on client devices (like smartphones or IoT devices, which especially matter in TinyML) for model training, which often have limited computational power and battery life. Running complex machine learning algorithms locally can strain these resources, leading to potential performance issues. Moreover, the capabilities of these devices can vary significantly, resulting in uneven contributions to the model training process. Some devices process updates faster and more efficiently than others, leading to disparities in the learning process. Balancing the computational load to ensure consistent participation and efficiency across all devices is a key challenge in FL.\nModel Training Efficiency: FL’s decentralized nature can impact model training’s efficiency. Achieving convergence, where the model no longer significantly improves, can be slower in FL than in centralized training methods. This is particularly true in cases where the data is non-IID (non-independent and identically distributed) across devices. Additionally, the algorithms used for aggregating model updates play a critical role in the training process. Their efficiency directly affects the speed and effectiveness of learning. Developing and implementing algorithms that can handle the complexities of FL while ensuring timely convergence is essential for the system’s performance.\nScalability Challenges: Scalability is a significant concern in FL, especially as the number of participating devices increases. Managing and coordinating model updates from many devices adds complexity and can strain the system. Ensuring that the system architecture can efficiently handle this increased load without degrading performance is crucial. This involves not just handling the computational and communication aspects but also maintaining the quality and consistency of the model as the scale of the operation grows. A key challenge is designing FL systems that scale effectively while maintaining performance.\nData Synchronization and Consistency: Ensuring data synchronization and maintaining model consistency across all participating devices in FL is challenging. Keeping all devices synchronized with the latest model version can be difficult in environments with intermittent connectivity or devices that go offline periodically. Furthermore, maintaining consistency in the learned model, especially when dealing with a wide range of devices with different data distributions and update frequencies, is crucial. This requires sophisticated synchronization and aggregation strategies to ensure that the final model accurately reflects the learnings from all devices.\nEnergy Consumption: The energy consumption of client devices in FL is a critical factor, particularly for battery-powered devices like smartphones and other TinyML/IoT devices. The computational demands of training models locally can lead to significant battery drain, which might discourage continuous participation in the FL process. Balancing the computational requirements of model training with energy efficiency is essential. This involves optimizing algorithms and training processes to reduce energy consumption while achieving effective learning outcomes. Ensuring energy-efficient operation is key to user acceptance and the sustainability of FL systems.\n\n\nCase Studies\nHere are a couple of real-world case studies that can illustrate the use of federated learning:\n\nGoogle Gboard\nGoogle uses federated learning to improve predictions on its Gboard mobile keyboard app. The app runs a federated learning algorithm on users’ devices to learn from their local usage patterns and text predictions while keeping user data private. The model updates are aggregated in the cloud to produce an enhanced global model. This allows for providing next-word predictions personalized to each user’s typing style while avoiding directly collecting sensitive typing data. Google reported that the federated learning approach reduced prediction errors by 25% compared to the baseline while preserving Privacy.\n\n\nHealthcare Research\nThe UK Biobank and American College of Cardiology combined datasets to train a model for heart arrhythmia detection using federated learning. The datasets could not be combined directly due to legal and Privacy restrictions. Federated learning allowed collaborative model development without sharing protected health data, with only model updates exchanged between the parties. This improved model accuracy as it could leverage a wider diversity of training data while meeting regulatory requirements.\n\n\nFinancial Services\nBanks are exploring using federated learning for anti-money laundering (AML) detection models. Multiple banks could jointly improve AML Models without sharing confidential customer transaction data with competitors or third parties. Only the model updates need to be aggregated rather than raw transaction data. This allows access to richer training data from diverse sources while avoiding regulatory and confidentiality issues around sharing sensitive financial customer data.\nThese examples demonstrate how federated learning provides tangible privacy benefits and enables collaborative ML in settings where direct data sharing is impossible.\n\n\n\n\n14.8.3 Machine Unlearning\n\nCore Idea\nMachine unlearning is a fairly new process that describes how the influence of a subset of training data can be removed from the model. Several methods have been used to perform machine unlearning and remove the influence of a subset of training data from the final model. A baseline approach might consist of simply fine-tuning the model for more epochs on just the data that should be remembered to decrease the influence of the data “forgotten” by the model. Since this approach doesn’t explicitly remove the influence of data that should be erased, membership inference attacks are still possible, so researchers have adopted other approaches to unlearn data from a model explicitly. One type of approach that researchers have adopted includes adjusting the model loss function to treat the losses of the “forget set explicitly” (data to be unlearned) and the “retain set” (remaining data that should still be remembered) differently (Tarun et al. 2022; Khan and Swaroop 2021).\n\nTarun, Ayush K, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli. 2022. “Deep Regression Unlearning.” ArXiv Preprint abs/2210.08196. https://arxiv.org/abs/2210.08196.\n\nKhan, Mohammad Emtiyaz, and Siddharth Swaroop. 2021. “Knowledge-Adaptation Priors.” In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, Virtual, edited by Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, 19757–70. https://proceedings.neurips.cc/paper/2021/hash/a4380923dd651c195b1631af7c829187-Abstract.html.\n\n\nCase Study\nSome researchers demonstrate a real-life example of machine unlearning approaches applied to SOTA machine learning models through training an LLM, LLaMA2-7b, to unlearn any references to Harry Potter (Eldan and Russinovich 2023). Though this model took 184K GPU hours to pre-train, it only took 1 GPU hour of fine-tuning to erase the model’s ability to generate or recall Harry Potter-related content without noticeably compromising the accuracy of generating content unrelated to Harry Potter. Figure 14.13 demonstrates how the model output changes before (Llama-7b-chat-hf column) and after (Finetuned Llama-b column) unlearning has occurred.\n\n\n\n\n\n\nFigure 14.13: Llama unlearning Harry Potter. Source: Eldan and Russinovich (2023).\n\n\nEldan, Ronen, and Mark Russinovich. 2023. “Who’s Harry Potter? Approximate Unlearning in LLMs.” ArXiv Preprint abs/2310.02238. https://arxiv.org/abs/2310.02238.\n\n\n\n\nOther Uses\n\nRemoving adversarial data\nDeep learning models have previously been shown to be vulnerable to adversarial attacks, in which the attacker generates adversarial data similar to the original training data, where a human cannot tell the difference between the real and fabricated data. The adversarial data results in the model outputting incorrect predictions, which could have detrimental consequences in various applications, including healthcare diagnosis predictions. Machine unlearning has been used to unlearn the influence of adversarial data to prevent these incorrect predictions from occurring and causing any harm\n\n\n\n\n14.8.4 Homomorphic Encryption\n\nCore Idea\nHomomorphic encryption is a form of encryption that allows computations to be carried out on ciphertext, generating an encrypted result that, when decrypted, matches the result of operations performed on the plaintext. For example, multiplying two numbers encrypted with homomorphic encryption produces an encrypted product that decrypts the actual product of the two numbers. This means that data can be processed in an encrypted form, and only the resulting output needs to be decrypted, significantly enhancing data security, especially for sensitive information.\nHomomorphic encryption enables outsourced computation on encrypted data without exposing the data itself to the external party performing the operations. However, only certain computations like addition and multiplication are supported in partially homomorphic schemes. Fully homomorphic encryption (FHE) that can handle any computation is even more complex. The number of possible operations is limited before noise accumulation corrupts the ciphertext.\nTo use homomorphic encryption across different entities, carefully generated public keys must be exchanged for operations across separately encrypted data. This advanced encryption technique enables previously impossible secure computation paradigms but requires expertise to implement correctly for real-world systems.\n\n\nBenefits\nHomomorphic encryption enables machine learning model training and inference on encrypted data, ensuring that sensitive inputs and intermediate values remain confidential. This is critical in healthcare, finance, genetics, and other domains, which are increasingly relying on ML to analyze sensitive and regulated data sets containing billions of personal records.\nHomomorphic encryption thwarts attacks like model extraction and membership inference that could expose private data used in ML workflows. It provides an alternative to TEEs using hardware enclaves for confidential computing. However, current schemes have high computational overheads and algorithmic limitations that constrain real-world applications.\nHomomorphic encryption realizes the decades-old vision of secure multiparty computation by allowing computation on ciphertexts. Conceptualized in the 1970s, the first fully homomorphic cryptosystems emerged in 2009, enabling arbitrary computations. Ongoing research is making these techniques more efficient and practical.\nHomomorphic encryption shows great promise in enabling privacy-preserving machine learning under emerging data regulations. However, given constraints, one should carefully evaluate its applicability against other confidential computing approaches. Extensive resources exist to explore homomorphic encryption and track progress in easing adoption barriers.\n\n\nMechanics\n\nData Encryption: Before data is processed or sent to an ML model, it is encrypted using a homomorphic encryption scheme and public key. For example, encrypting numbers \\(x\\) and \\(y\\) generates ciphertexts \\(E(x)\\) and \\(E(y)\\).\nComputation on Ciphertext: The ML algorithm processes the encrypted data directly. For instance, multiplying the ciphertexts \\(E(x)\\) and \\(E(y)\\) generates \\(E(xy)\\). More complex model training can also be done on ciphertexts.\nResult Encryption: The result \\(E(xy)\\) remains encrypted and can only be decrypted by someone with the corresponding private key to reveal the actual product \\(xy\\).\n\nOnly authorized parties with the private key can decrypt the final outputs, protecting the intermediate state. However, noise accumulates with each operation, preventing further computation without decryption.\nBeyond healthcare, homomorphic encryption enables confidential computing for applications like financial fraud detection, insurance analytics, genetics research, and more. It offers an alternative to techniques like multipartymultiparty computation and TEEs. Ongoing research aims to improve the efficiency and capabilities.\nTools like HElib, SEAL, and TensorFlow HE provide libraries for exploring implementing homomorphic encryption in real-world machine learning pipelines.\n\n\nTradeoffs\nFor many real-time and embedded applications, fully homomorphic encryption remains impractical for the following reasons.\nComputational Overhead: Homomorphic encryption imposes very high computational overheads, often resulting in slowdowns of over 100x for real-world ML applications. This makes it impractical for many time-sensitive or resource-constrained uses. Optimized hardware and parallelization can help but not eliminate this issue.\nComplexity of Implementation The sophisticated algorithms require deep expertise in cryptography to be implemented correctly. Nuances like format compatibility with floating point ML models and scalable key management pose hurdles. This complexity hinders widespread practical adoption.\nAlgorithmic Limitations: Current schemes restrict the functions and depth of computations supported, limiting the models and data volumes that can be processed. Ongoing research is pushing these boundaries, but restrictions remain.\nHardware Acceleration: Homomorphic encryption requires specialized hardware, such as secure processors or coprocessors with TEEs, which adds design and infrastructure costs.\nHybrid Designs: Rather than encrypting entire workflows, selective application of homomorphic encryption to critical subcomponents can achieve protection while minimizing overheads.\n\n\n\n\n\n\nExercise 14.2: Homomorphic Encryption\n\n\n\n\n\nReady to unlock the power of encrypted computation? Homomorphic encryption is like a magic trick for your data! In this Colab, we’ll learn how to do calculations on secret numbers without ever revealing them. Imagine training a model on data you can’t even see – that’s the power of this mind-bending technology.\n\n\n\n\n\n\n\n14.8.5 Secure Multiparty Communication\n\nCore Idea\nThe overarching goal of MPC is to enable different parties to jointly compute a function over their inputs while keeping those inputs private. For example, two organizations may want to collaborate on training a machine learning model by combining their respective data sets. Still, they cannot directly reveal that data due to Privacy or confidentiality constraints. MPC aims to provide protocols and techniques that allow them to achieve the benefits of pooled data for model accuracy without compromising the privacy of each organization’s sensitive data.\nAt a high level, MPC works by carefully splitting the computation into parts that each party can execute independently using their private input. The results are then combined to reveal only the final output of the function and nothing about the intermediate values. Cryptographic techniques are used to guarantee that the partial results remain private provably.\nLet’s take a simple example of an MPC protocol. One of the most basic MPC protocols is the secure addition of two numbers. Each party splits its input into random shares that are secretly distributed. They exchange the shares and locally compute the sum of the shares, which reconstructs the final sum without revealing the individual inputs. For example, if Alice has input x and Bob has input y:\n\nAlice generates random \\(x_1\\) and sets \\(x_2 = x - x_1\\)\nBob generates random \\(y_1\\) and sets \\(y_2 = y - y_1\\)\nAlice sends \\(x_1\\) to Bob, Bob sends \\(y_1\\) to Alice (keeping \\(x_2\\) and \\(y_2\\) secret)\nAlice computes \\(x_2 + y_1 = s_1\\), Bob computes \\(x_1 + y_2 = s_2\\)\n\\(s_1 + s_2 = x + y\\) is the final sum, without revealing \\(x\\) or \\(y\\).\n\nAlice’s and Bob’s individual inputs (\\(x\\) and \\(y\\)) remain private, and each party only reveals one number associated with their original inputs. The random spits ensure no information about the original numbers disclosed\nSecure Comparison: Another basic operation is a secure comparison of two numbers, determining which is greater than the other. This can be done using techniques like Yao’s Garbled Circuits, where the comparison circuit is encrypted to allow joint evaluation of the inputs without leaking them.\nSecure Matrix Multiplication: Matrix operations like multiplication are essential for machine learning. MPC techniques like additive secret sharing can be used to split matrices into random shares, compute products on the shares, and then reconstruct the result.\nSecure Model Training: Distributed machine learning training algorithms like federated averaging can be made secure using MPC. Model updates computed on partitioned data at each node are secretly shared between nodes and aggregated to train the global model without exposing individual updates.\nThe core idea behind MPC protocols is to divide the computation into steps that can be executed jointly without revealing intermediate sensitive data. This is accomplished by combining cryptographic techniques like secret sharing, homomorphic encryption, oblivious transfer, and garbled circuits. MPC protocols enable the collaborative computation of sensitive data while providing provable privacy guarantees. This privacy-preserving capability is essential for many machine learning applications today involving multiple parties that cannot directly share their raw data.\nThe main approaches used in MPC include:\n\nHomomorphic encryption: Special encryption allows computations to be carried out on encrypted data without decrypting it.\nSecret sharing: The private data is divided into random shares distributed to each party. Computations are done locally on the shares and finally reconstructed.\nOblivious transfer: A protocol where a receiver obtains a subset of data from a sender, but the sender does not know which specific data was transferred.\nGarbled circuits: The function to be computed is represented as a Boolean circuit that is encrypted (“garbled”) to allow joint evaluation without revealing inputs.\n\n\n\nTradeoffs\nWhile MPC protocols provide strong privacy guarantees, they come at a high computational cost compared to plain computations. Every secure operation, like addition, multiplication, comparison, etc., requires more processing orders than the equivalent unencrypted operation. This overhead stems from the underlying cryptographic techniques:\n\nIn partially homomorphic encryption, each computation on ciphertexts requires costly public-key operations. Fully homomorphic encryption has even higher overheads.\nSecret sharing divides data into multiple shares, so even basic operations require manipulating many shares.\nOblivious transfer and garbled circuits add masking and encryption to hide data access patterns and execution flows.\nMPC systems require extensive communication and interaction between parties to compute on shares/ciphertexts jointly.\n\nAs a result, MPC protocols can slow down computations by 3-4 orders of magnitude compared to plain implementations. This becomes prohibitively expensive for large datasets and models. Therefore, training machine learning models on encrypted data using MPC remains infeasible today for realistic dataset sizes due to the overhead. Clever optimizations and approximations are needed to make MPC practical.\nOngoing MPC research aims to close this efficiency gap through cryptographic advances, new algorithms, trusted hardware like SGX enclaves, and leveraging accelerators like GPUs/TPUs. However, in the foreseeable future, some degree of approximation and performance tradeoff is needed to scale MPC to meet the demands of real-world machine learning systems.\n\n\n\n14.8.6 Synthetic Data Generation\n\nCore Idea\nSynthetic data generation has emerged as an important privacy-preserving machine learning approach that allows models to be developed and tested without exposing real user data. The key idea is to train generative models on real-world datasets and then sample from these models to synthesize artificial data that statistically match the original data distribution but does not contain actual user information. For example, a GAN could be trained on a dataset of sensitive medical records to learn the underlying patterns and then used to sample synthetic patient data.\nThe primary challenge of synthesizing data is to ensure adversaries are unable to re-identify the original dataset. A simple approach to achieving synthetic data is adding noise to the original dataset, which still risks privacy leakage. When noise is added to data in the context of differential privacy, sophisticated mechanisms based on the data’s sensitivity are used to calibrate the amount and distribution of noise. Through these mathematically rigorous frameworks, differential Privacy generally guarantees Privacy at some level, which is the primary goal of this privacy-preserving technique. Beyond preserving privacy, synthetic data combats multiple data availability issues such as imbalanced datasets, scarce datasets, and anomaly detection.\nResearchers can freely share this synthetic data and collaborate on modeling without revealing private medical information. Well-constructed synthetic data protects Privacy while providing utility for developing accurate models. Key techniques to prevent reconstructing the original data include adding differential privacy noise during training, enforcing plausibility constraints, and using multiple diverse generative models. Here are some common approaches for generating synthetic data:\n\nGenerative Adversarial Networks (GANs): GANs are an AI algorithm used in unsupervised learning where two neural networks compete against each other in a game. Figure 14.14 is an overview of the GAN system. The generator network (big red box) is responsible for producing the synthetic data, and the discriminator network (yellow box) evaluates the authenticity of the data by distinguishing between fake data created by the generator network and the real data. The generator and discriminator networks learn and update their parameters based on the results. The discriminator acts as a metric on how similar the fake and real data are to one another. It is highly effective at generating realistic data and is a popular approach for generating synthetic data.\n\n\n\n\n\n\n\nFigure 14.14: Flowchart of GANs. Source: Rosa and Papa (2021).\n\n\nRosa, Gustavo H. de, and João P. Papa. 2021. “A Survey on Text Generation Using Generative Adversarial Networks.” Pattern Recogn. 119 (November): 108098. https://doi.org/10.1016/j.patcog.2021.108098.\n\n\n\nVariational Autoencoders (VAEs): VAEs are neural networks capable of learning complex probability distributions and balancing data generation quality and computational efficiency. They encode data into a latent space where they learn the distribution to decode the data back.\nData Augmentation: This involves transforming existing data to create new, altered data. For example, flipping, rotating, and scaling (uniformly or non-uniformly) original images can help create a more diverse, robust image dataset before training an ML model.\nSimulations: Mathematical models can simulate real-world systems or processes to mimic real-world phenomena. This is highly useful in scientific research, urban planning, and economics.\n\n\n\nBenefits\nWhile synthetic data may be necessary due to Privacy or compliance risks, it is widely used in machine learning models when available data is of poor quality, scarce, or inaccessible. Synthetic data offers more efficient and effective development by streamlining robust model training, testing, and deployment processes. It allows researchers to share models more widely without breaching privacy laws and regulations. Collaboration between users of the same dataset will be facilitated, which will help broaden the capabilities and advancements in ML research.\nThere are several motivations for using synthetic data in machine learning:\n\nPrivacy and compliance: Synthetic data avoids exposing personal information, allowing more open sharing and collaboration. This is important when working with sensitive datasets like healthcare records or financial information.\nData scarcity: When insufficient real-world data is available, synthetic data can augment training datasets. This improves model accuracy when limited data is a bottleneck.\nModel testing: Synthetic data provides privacy-safe sandboxes for testing model performance, debugging issues, and monitoring for bias.\nData labeling: High-quality labeled training data is often scarce and expensive. Synthetic data can help auto-generate labeled examples.\n\n\n\nTradeoffs\nWhile synthetic data aims to remove any evidence of the original dataset, privacy leakage is still a risk since the synthetic data mimics the original data. The statistical information and distribution are similar, if not the same, between the original and synthetic data. By resampling from the distribution, adversaries may still be able to recover the original training samples. Due to their inherent learning processes and complexities, neural networks might accidentally reveal sensitive information about the original training data.\nA core challenge with synthetic data is the potential gap between synthetic and real-world data distributions. Despite advancements in generative modeling techniques, synthetic data may only partially capture real data’s complexity, diversity, and nuanced patterns. This can limit the utility of synthetic data for robustly training machine learning models. Rigorously evaluating synthetic data quality through adversary methods and comparing model performance to real data benchmarks helps assess and improve fidelity. However, inherently, synthetic data remains an approximation.\nAnother critical concern is the privacy risks of synthetic data. Generative models may leak identifiable information about individuals in the training data, which could enable reconstruction of private information. Emerging adversarial attacks demonstrate the challenges in preventing identity leakage from synthetic data generation pipelines. Techniques like differential Privacy can help safeguard Privacy but come with tradeoffs in data utility. There is an inherent tension between producing useful synthetic data and fully protecting sensitive training data, which must be balanced.\nAdditional pitfalls of synthetic data include amplified biases, labeling difficulties, the computational overhead of training generative models, storage costs, and failure to account for out-of-distribution novel data. While these are secondary to the core synthetic-real gap and privacy risks, they remain important considerations when evaluating the suitability of synthetic data for particular machine-learning tasks. As with any technique, the advantages of synthetic data come with inherent tradeoffs and limitations that require thoughtful mitigation strategies.\n\n\n\n14.8.7 Summary\nWhile all the techniques we have discussed thus far aim to enable privacy-preserving machine learning, they involve distinct mechanisms and tradeoffs. Factors like computational constraints, required trust assumptions, threat models, and data characteristics help guide the selection process for a particular use case. However, finding the right balance between Privacy, accuracy, and efficiency necessitates experimentation and empirical evaluation for many applications. Table 14.2 is a comparison table of the key privacy-preserving machine learning techniques and their pros and cons:\n\n\n\nTable 14.2: Comparing techniques for privacy-preserving machine learning.\n\n\n\n\n\n\n\n\n\n\nTechnique\nPros\nCons\n\n\n\n\nDifferential Privacy\n\nStrong formal privacy guarantees\nRobust to auxiliary data attacks\nVersatile for many data types and analyses\n\n\nAccuracy loss from noise addition\nComputational overhead for sensitivity analysis and noise generation\n\n\n\nFederated Learning\n\nAllows collaborative learning without sharing raw data\nData remains decentralized improving security\nNo need for encrypted computation\n\n\nIncreased communication overhead\nPotentially slower model convergence\nUneven client device capabilities\n\n\n\nSecure Multi-Party Computation\n\nEnables joint computation on sensitive data\nProvides cryptographic privacy guarantees\nFlexible protocols for various functions\n\n\nVery high computational overhead\nComplexity of implementation\nAlgorithmic constraints on function depth\n\n\n\nHomomorphic Encryption\n\nAllows computation on encrypted data\nPrevents intermediate state exposure\n\n\nExtremely high computational cost\nComplex cryptographic implementations\nRestrictions on function types\n\n\n\nSynthetic Data Generation\n\nEnables data sharing without leakage\nMitigates data scarcity problems\n\n\nSynthetic-real gap in distributions\nPotential for reconstructing private data\nBiases and labeling challenges", + "text": "14.8 Privacy-Preserving ML Techniques\nMany techniques have been developed to preserve privacy, each addressing different aspects and data security challenges. These methods can be broadly categorized into several key areas: Differential Privacy, which focuses on statistical privacy in data outputs; Federated Learning, emphasizing decentralized data processing; Homomorphic Encryption and Secure Multi-party Computation (SMC), both enabling secure computations on encrypted or private data; Data Anonymization and Data Masking and Obfuscation, which alter data to protect individual identities; Private Set Intersection and Zero-Knowledge Proofs, facilitating secure data comparisons and validations; Decentralized Identifiers (DIDs) for self-sovereign digital identities; Privacy-Preserving Record Linkage (PPRL), linking data across sources without exposure; Synthetic Data Generation, creating artificial datasets for safe analysis; and Adversarial Learning Techniques, enhancing data or model resistance to privacy attacks.\nGiven the extensive range of these techniques, it is not feasible to dive into each in depth within a single course or discussion, let alone for anyone to know it all in its glorious detail. Therefore, we will explore a few specific techniques in relative detail, providing a deeper understanding of their principles, applications, and the unique privacy challenges they address in machine learning. This focused approach will give us a more comprehensive and practical understanding of key privacy-preserving methods in modern ML systems.\n\n14.8.1 Differential Privacy\n\nCore Idea\nDifferential Privacy is a framework for quantifying and managing the privacy of individuals in a dataset (Dwork et al. 2006). It provides a mathematical guarantee that the privacy of individuals in the dataset will not be compromised, regardless of any additional knowledge an attacker may possess. The core idea of differential Privacy is that the outcome of any analysis (like a statistical query) should be essentially the same, whether any individual’s data is included in the dataset or not. This means that by observing the analysis result, one cannot determine whether any individual’s data was used in the computation.\n\nDwork, Cynthia, Frank McSherry, Kobbi Nissim, and Adam Smith. 2006. “Calibrating Noise to Sensitivity in Private Data Analysis.” In Theory of Cryptography, edited by Shai Halevi and Tal Rabin, 265–84. Berlin, Heidelberg: Springer Berlin Heidelberg.\nFor example, let’s say a database contains medical records for 10 patients. We want to release statistics about the prevalence of diabetes in this sample without revealing one patient’s condition. To do this, we could add a small amount of random noise to the true count before releasing it. If the true number of diabetes patients is 6, we might add noise from a Laplace distribution to randomly output 5, 6, or 7 each with some probability. An observer now can’t tell if any single patient has diabetes based only on the noisy output. The query result looks similar to whether each patient’s data is included or excluded. This is differential Privacy. More formally, a randomized algorithm satisfies ε-differential Privacy if, for any neighbor databases D and Dʹ differing by only one entry, the probability of any outcome changes by at most a factor of ε. A lower ε provides stronger privacy guarantees.\nThe Laplace Mechanism is one of the most straightforward and commonly used methods to achieve differential Privacy. It involves adding noise that follows a Laplace distribution to the data or query results. Apart from the Laplace Mechanism, the general principle of adding noise is central to differential Privacy. The idea is to add random noise to the data or the results of a query. The noise is calibrated to ensure the necessary privacy guarantee while keeping the data useful.\nWhile the Laplace distribution is common, other distributions like Gaussian can also be used. Laplace noise is used for strict ε-differential Privacy for low-sensitivity queries. In contrast, Gaussian distributions can be used when Privacy is not guaranteed, known as (ϵ, 𝛿)-Differential Privacy. In this relaxed version of differential Privacy, epsilon and delta define the amount of Privacy guaranteed when releasing information or a model related to a dataset. Epsilon sets a bound on how much information can be learned about the data based on the output. At the same time, delta allows for a small probability of the privacy guarantee to be violated. The choice between Laplace, Gaussian, and other distributions will depend on the specific requirements of the query and the dataset and the tradeoff between Privacy and accuracy.\nTo illustrate the tradeoff of Privacy and accuracy in (\\(\\epsilon\\), \\(\\delta\\))-differential Privacy, the following graphs in Figure 14.11 show the results on accuracy for different noise levels on the MNIST dataset, a large dataset of handwritten digits (Abadi et al. 2016). The delta value (black line; right y-axis) denotes the level of privacy relaxation (a high value means Privacy is less stringent). As Privacy becomes more relaxed, the accuracy of the model increases.\n\n\n\n\n\n\nFigure 14.11: Privacy-accuracy tradeoff. Source: Abadi et al. (2016).\n\n\nAbadi, Martin, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. “Deep Learning with Differential Privacy.” In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, 308–18. CCS ’16. New York, NY, USA: ACM. https://doi.org/10.1145/2976749.2978318.\n\n\nThe key points to remember about differential Privacy are the following:\n\nAdding Noise: The fundamental technique in differential Privacy is adding controlled random noise to the data or query results. This noise masks the contribution of individual data points.\nBalancing Act: There’s a balance between Privacy and accuracy. More noise (lower ϵ) in the data means higher Privacy but less accuracy in the model’s results.\nUniversality: Differential Privacy doesn’t rely on assumptions about what an attacker knows. This makes it robust against re-identification attacks, where an attacker tries to uncover individual data.\nApplicability: It can be applied to various types of data and queries, making it a versatile tool for privacy-preserving data analysis.\n\n\n\nTradeoffs\nThere are several tradeoffs to make with differential Privacy, as is the case with any algorithm. But let’s focus on the computational-specific tradeoffs since we care about ML systems. There are some key computational considerations and tradeoffs when implementing differential Privacy in a machine-learning system:\nNoise generation: Implementing differential Privacy introduces several important computational tradeoffs compared to standard machine learning techniques. One major consideration is the need to securely generate random noise from distributions like Laplace or Gaussian that get added to query results and model outputs. High-quality cryptographic random number generation can be computationally expensive.\nSensitivity analysis: Another key requirement is rigorously tracking the sensitivity of the underlying algorithms to single data points getting added or removed. This global sensitivity analysis is required to calibrate the noise levels properly. However, analyzing worst-case sensitivity can substantially increase computational complexity for complex model training procedures and data pipelines.\nPrivacy budget management: Managing the privacy loss budget across multiple queries and learning iterations is another bookkeeping overhead. The system must keep track of cumulative privacy costs and compose them to explain overall privacy guarantees. This adds a computational burden beyond just running queries or training models.\nBatch vs. online tradeoffs: For online learning systems with continuous high-volume queries, differentially private algorithms require new mechanisms to maintain utility and prevent too much accumulated privacy loss since each query can potentially alter the privacy budget. Batch offline processing is simpler from a computational perspective as it processes data in large batches, where each batch is treated as a single query. High-dimensional sparse data also increases sensitivity analysis challenges.\nDistributed training: When training models using distributed or federated approaches, new cryptographic protocols are needed to track and bound privacy leakage across nodes. Secure multiparty computation with encrypted data for differential Privacy adds substantial computational load.\nWhile differential Privacy provides strong formal privacy guarantees, implementing it rigorously requires additions and modifications to the machine learning pipeline at a computational cost. Managing these overheads while preserving model accuracy remains an active research area.\n\n\nCase Study\nApple’s implementation of differential Privacy in iOS and MacOS provides a prominent real-world example of how differential Privacy can be deployed at large scale. Apple wanted to collect aggregated usage statistics across their ecosystem to improve products and services, but aimed to do so without compromising individual user privacy.\nTo achieve this, they implemented differential privacy techniques directly on user devices to anonymize data points before sending them to Apple servers. Specifically, Apple uses the Laplace mechanism to inject carefully calibrated random noise. For example, suppose a user’s location history contains [Work, Home, Work, Gym, Work, Home]. In that case, the differentially private version might replace the exact locations with a noisy sample like [Gym, Home, Work, Work, Home, Work].\nApple tunes the Laplace noise distribution to provide a high level of Privacy while preserving the utility of aggregated statistics. Increasing noise levels provides stronger privacy guarantees (lower ε values in DP terminology) but can reduce data utility. Apple’s privacy engineers empirically optimized this tradeoff based on their product goals.\nApple obtains high-fidelity aggregated statistics by aggregating hundreds of millions of noisy data points from devices. For instance, they can analyze new iOS apps’ features while masking any user’s app behaviors. On-device computation avoids sending raw data to Apple servers.\nThe system uses hardware-based secure random number generation to sample from the Laplace distribution on devices efficiently. Apple also had to optimize its differentially private algorithms and pipeline to operate under the computational constraints of consumer hardware.\nMultiple third-party audits have verified that Apple’s system provides rigorous differential privacy protections in line with their stated policies. Of course, assumptions around composition over time and potential re-identification risks still apply. Apple’s deployment shows how differential Privacy can be realized in large real-world products when backed by sufficient engineering resources.\n\n\n\n\n\n\nExercise 14.1: Differential Privacy - TensorFlow Privacy\n\n\n\n\n\nWant to train an ML model without compromising anyone’s secrets? Differential Privacy is like a superpower for your data! In this Colab, we’ll use TensorFlow Privacy to add special noise during training. This makes it way harder for anyone to determine if a single person’s data was used, even if they have sneaky ways of peeking at the model.\n\n\n\n\n\n\n\n14.8.2 Federated Learning\n\nCore Idea\nFederated Learning (FL) is a type of machine learning in which a model is built and distributed across multiple devices or servers while keeping the training data localized. It was previously discussed in the Model Optimizations chapter. Still, we will recap it here briefly to complete it and focus on things that pertain to this chapter.\nFL trains machine learning models across decentralized networks of devices or systems while keeping all training data localized. Figure 14.12 illustrates this process: each participating device leverages its local data to calculate model updates, which are then aggregated to build an improved global model. However, the raw training data is never directly shared, transferred, or compiled. This privacy-preserving approach allows for the joint development of ML models without centralizing the potentially sensitive training data in one place.\n\n\n\n\n\n\nFigure 14.12: Federated Learning lifecycle. Source: Jin et al. (2020).\n\n\nJin, Yilun, Xiguang Wei, Yang Liu, and Qiang Yang. 2020. “Towards Utilizing Unlabeled Data in Federated Learning: A Survey and Prospective.” arXiv Preprint arXiv:2002.11545.\n\n\nOne of the most common model aggregation algorithms is Federated Averaging (FedAvg), where the global model is created by averaging all of the parameters from local parameters. While FedAvg works well with independent and identically distributed data (IID), alternate algorithms like Federated Proximal (FedProx) are crucial in real-world applications where data is often non-IID. FedProx is designed for the FL process when there is significant heterogeneity in the client updates due to diverse data distributions across devices, computational capabilities, or varied amounts of data.\nBy leaving the raw data distributed and exchanging only temporary model updates, federated learning provides a more secure and privacy-enhancing alternative to traditional centralized machine learning pipelines. This allows organizations and users to benefit collaboratively from shared models while maintaining control and ownership over sensitive data. The decentralized nature of FL also makes it robust to single points of failure.\nImagine a group of hospitals that want to collaborate on a study to predict patient outcomes based on their symptoms. However, they cannot share their patient data due to privacy concerns and regulations like HIPAA. Here’s how Federated Learning can help.\n\nLocal Training: Each hospital trains a machine learning model on patient data. This training happens locally, meaning the data never leaves the hospital’s servers.\nModel Sharing: After training, each hospital only sends the model (specifically, its parameters or weights ) to a central server. It does not send any patient data.\nAggregating Models: The central server aggregates these models from all hospitals into a single, more robust model. This process typically involves averaging the model parameters.\nBenefit: The result is a machine learning model that has learned from a wide range of patient data without sharing sensitive data or removing it from its original location.\n\n\n\nTradeoffs\nThere are several system performance-related aspects of FL in machine learning systems. It would be wise to understand these tradeoffs because there is no “free lunch” for preserving Privacy through FL (Li et al. 2020).\n\nLi, Tian, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. 2020. “Federated Learning: Challenges, Methods, and Future Directions.” IEEE Signal Process Mag. 37 (3): 50–60. https://doi.org/10.1109/msp.2020.2975749.\nCommunication Overhead and Network Constraints: In FL, one of the most significant challenges is managing the communication overhead. This involves the frequent transmission of model updates between a central server and numerous client devices, which can be bandwidth-intensive. The total number of communication rounds and the size of transmitted messages per round need to be reduced to minimize communication further. This can lead to substantial network traffic, especially in scenarios with many participants. Additionally, latency becomes a critical factor — the time taken for these updates to be sent, aggregated, and redistributed can introduce delays. This affects the overall training time and impacts the system’s responsiveness and real-time capabilities. Managing this communication while minimizing bandwidth usage and latency is crucial for implementing FL.\nComputational Load on Local Devices: FL relies on client devices (like smartphones or IoT devices, which especially matter in TinyML) for model training, which often have limited computational power and battery life. Running complex machine learning algorithms locally can strain these resources, leading to potential performance issues. Moreover, the capabilities of these devices can vary significantly, resulting in uneven contributions to the model training process. Some devices process updates faster and more efficiently than others, leading to disparities in the learning process. Balancing the computational load to ensure consistent participation and efficiency across all devices is a key challenge in FL.\nModel Training Efficiency: FL’s decentralized nature can impact model training’s efficiency. Achieving convergence, where the model no longer significantly improves, can be slower in FL than in centralized training methods. This is particularly true in cases where the data is non-IID (non-independent and identically distributed) across devices. Additionally, the algorithms used for aggregating model updates play a critical role in the training process. Their efficiency directly affects the speed and effectiveness of learning. Developing and implementing algorithms that can handle the complexities of FL while ensuring timely convergence is essential for the system’s performance.\nScalability Challenges: Scalability is a significant concern in FL, especially as the number of participating devices increases. Managing and coordinating model updates from many devices adds complexity and can strain the system. Ensuring that the system architecture can efficiently handle this increased load without degrading performance is crucial. This involves not just handling the computational and communication aspects but also maintaining the quality and consistency of the model as the scale of the operation grows. A key challenge is designing FL systems that scale effectively while maintaining performance.\nData Synchronization and Consistency: Ensuring data synchronization and maintaining model consistency across all participating devices in FL is challenging. Keeping all devices synchronized with the latest model version can be difficult in environments with intermittent connectivity or devices that go offline periodically. Furthermore, maintaining consistency in the learned model, especially when dealing with a wide range of devices with different data distributions and update frequencies, is crucial. This requires sophisticated synchronization and aggregation strategies to ensure that the final model accurately reflects the learnings from all devices.\nEnergy Consumption: The energy consumption of client devices in FL is a critical factor, particularly for battery-powered devices like smartphones and other TinyML/IoT devices. The computational demands of training models locally can lead to significant battery drain, which might discourage continuous participation in the FL process. Balancing the computational requirements of model training with energy efficiency is essential. This involves optimizing algorithms and training processes to reduce energy consumption while achieving effective learning outcomes. Ensuring energy-efficient operation is key to user acceptance and the sustainability of FL systems.\n\n\nCase Studies\nHere are a couple of real-world case studies that can illustrate the use of federated learning:\n\nGoogle Gboard\nGoogle uses federated learning to improve predictions on its Gboard mobile keyboard app. The app runs a federated learning algorithm on users’ devices to learn from their local usage patterns and text predictions while keeping user data private. The model updates are aggregated in the cloud to produce an enhanced global model. This allows for providing next-word predictions personalized to each user’s typing style while avoiding directly collecting sensitive typing data. Google reported that the federated learning approach reduced prediction errors by 25% compared to the baseline while preserving Privacy.\n\n\nHealthcare Research\nThe UK Biobank and American College of Cardiology combined datasets to train a model for heart arrhythmia detection using federated learning. The datasets could not be combined directly due to legal and Privacy restrictions. Federated learning allowed collaborative model development without sharing protected health data, with only model updates exchanged between the parties. This improved model accuracy as it could leverage a wider diversity of training data while meeting regulatory requirements.\n\n\nFinancial Services\nBanks are exploring using federated learning for anti-money laundering (AML) detection models. Multiple banks could jointly improve AML Models without sharing confidential customer transaction data with competitors or third parties. Only the model updates need to be aggregated rather than raw transaction data. This allows access to richer training data from diverse sources while avoiding regulatory and confidentiality issues around sharing sensitive financial customer data.\nThese examples demonstrate how federated learning provides tangible privacy benefits and enables collaborative ML in settings where direct data sharing is impossible.\n\n\n\n\n14.8.3 Machine Unlearning\n\nCore Idea\nMachine unlearning is a fairly new process that describes how the influence of a subset of training data can be removed from the model. Several methods have been used to perform machine unlearning and remove the influence of a subset of training data from the final model. A baseline approach might consist of simply fine-tuning the model for more epochs on just the data that should be remembered to decrease the influence of the data “forgotten” by the model. Since this approach doesn’t explicitly remove the influence of data that should be erased, membership inference attacks are still possible, so researchers have adopted other approaches to unlearn data from a model explicitly. One type of approach that researchers have adopted includes adjusting the model loss function to treat the losses of the “forget set explicitly” (data to be unlearned) and the “retain set” (remaining data that should still be remembered) differently (Tarun et al. 2022; Khan and Swaroop 2021).\n\nTarun, Ayush K, Vikram S Chundawat, Murari Mandal, and Mohan Kankanhalli. 2022. “Deep Regression Unlearning.” ArXiv Preprint abs/2210.08196. https://arxiv.org/abs/2210.08196.\n\nKhan, Mohammad Emtiyaz, and Siddharth Swaroop. 2021. “Knowledge-Adaptation Priors.” In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, Virtual, edited by Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, 19757–70. https://proceedings.neurips.cc/paper/2021/hash/a4380923dd651c195b1631af7c829187-Abstract.html.\n\n\nCase Study\nSome researchers have demonstrated a real-life example of machine unlearning approaches applied to SOTA machine learning models through training an LLM, LLaMA2-7b, to unlearn any references to Harry Potter (Eldan and Russinovich 2023). Though this model took 184K GPU hours to pre-train, it only took 1 GPU hour of fine-tuning to erase the model’s ability to generate or recall Harry Potter-related content without noticeably compromising the accuracy of generating content unrelated to Harry Potter. Figure 14.13 demonstrates how the model output changes before (Llama-7b-chat-hf column) and after (Finetuned Llama-b column) unlearning has occurred.\n\n\n\n\n\n\nFigure 14.13: Llama unlearning Harry Potter. Source: Eldan and Russinovich (2023).\n\n\nEldan, Ronen, and Mark Russinovich. 2023. “Who’s Harry Potter? Approximate Unlearning in LLMs.” ArXiv Preprint abs/2310.02238. https://arxiv.org/abs/2310.02238.\n\n\n\n\nOther Uses\n\nRemoving adversarial data\nDeep learning models have previously been shown to be vulnerable to adversarial attacks, in which the attacker generates adversarial data similar to the original training data, where a human cannot tell the difference between the real and fabricated data. The adversarial data results in the model outputting incorrect predictions, which could have detrimental consequences in various applications, including healthcare diagnosis predictions. Machine unlearning has been used to unlearn the influence of adversarial data to prevent these incorrect predictions from occurring and causing any harm.\n\n\n\n\n14.8.4 Homomorphic Encryption\n\nCore Idea\nHomomorphic encryption is a form of encryption that allows computations to be carried out on ciphertext, generating an encrypted result that, when decrypted, matches the result of operations performed on the plaintext. For example, multiplying two numbers encrypted with homomorphic encryption produces an encrypted product that decrypts the actual product of the two numbers. This means that data can be processed in an encrypted form, and only the resulting output needs to be decrypted, significantly enhancing data security, especially for sensitive information.\nHomomorphic encryption enables outsourced computation on encrypted data without exposing the data itself to the external party performing the operations. However, only certain computations like addition and multiplication are supported in partially homomorphic schemes. Fully homomorphic encryption (FHE) that can handle any computation is even more complex. The number of possible operations is limited before noise accumulation corrupts the ciphertext.\nTo use homomorphic encryption across different entities, carefully generated public keys must be exchanged for operations across separately encrypted data. This advanced encryption technique enables previously impossible secure computation paradigms but requires expertise to implement correctly for real-world systems.\n\n\nBenefits\nHomomorphic encryption enables machine learning model training and inference on encrypted data, ensuring that sensitive inputs and intermediate values remain confidential. This is critical in healthcare, finance, genetics, and other domains, which are increasingly relying on ML to analyze sensitive and regulated data sets containing billions of personal records.\nHomomorphic encryption thwarts attacks like model extraction and membership inference that could expose private data used in ML workflows. It provides an alternative to TEEs using hardware enclaves for confidential computing. However, current schemes have high computational overheads and algorithmic limitations that constrain real-world applications.\nHomomorphic encryption realizes the decades-old vision of secure multiparty computation by allowing computation on ciphertexts. Conceptualized in the 1970s, the first fully homomorphic cryptosystems emerged in 2009, enabling arbitrary computations. Ongoing research is making these techniques more efficient and practical.\nHomomorphic encryption shows great promise in enabling privacy-preserving machine learning under emerging data regulations. However, given constraints, one should carefully evaluate its applicability against other confidential computing approaches. Extensive resources exist to explore homomorphic encryption and track progress in easing adoption barriers.\n\n\nMechanics\n\nData Encryption: Before data is processed or sent to an ML model, it is encrypted using a homomorphic encryption scheme and public key. For example, encrypting numbers \\(x\\) and \\(y\\) generates ciphertexts \\(E(x)\\) and \\(E(y)\\).\nComputation on Ciphertext: The ML algorithm processes the encrypted data directly. For instance, multiplying the ciphertexts \\(E(x)\\) and \\(E(y)\\) generates \\(E(xy)\\). More complex model training can also be done on ciphertexts.\nResult Encryption: The result \\(E(xy)\\) remains encrypted and can only be decrypted by someone with the corresponding private key to reveal the actual product \\(xy\\).\n\nOnly authorized parties with the private key can decrypt the final outputs, protecting the intermediate state. However, noise accumulates with each operation, preventing further computation without decryption.\nBeyond healthcare, homomorphic encryption enables confidential computing for applications like financial fraud detection, insurance analytics, genetics research, and more. It offers an alternative to techniques like multiparty computation and TEEs. Ongoing research improves the efficiency and capabilities.\nTools like HElib, SEAL, and TensorFlow HE provide libraries for exploring implementing homomorphic encryption in real-world machine learning pipelines.\n\n\nTradeoffs\nFor many real-time and embedded applications, fully homomorphic encryption remains impractical for the following reasons.\nComputational Overhead: Homomorphic encryption imposes very high computational overheads, often resulting in slowdowns of over 100x for real-world ML applications. This makes it impractical for many time-sensitive or resource-constrained uses. Optimized hardware and parallelization can alleviate but not eliminate this issue.\nComplexity of Implementation The sophisticated algorithms require deep expertise in cryptography to be implemented correctly. Nuances like format compatibility with floating point ML models and scalable key management pose hurdles. This complexity hinders widespread practical adoption.\nAlgorithmic Limitations: Current schemes restrict the functions and depth of computations supported, limiting the models and data volumes that can be processed. Ongoing research is pushing these boundaries, but restrictions remain.\nHardware Acceleration: Homomorphic encryption requires specialized hardware, such as secure processors or coprocessors with TEEs, which adds design and infrastructure costs.\nHybrid Designs: Rather than encrypting entire workflows, selective application of homomorphic encryption to critical subcomponents can achieve protection while minimizing overheads.\n\n\n\n\n\n\nExercise 14.2: Homomorphic Encryption\n\n\n\n\n\nReady to unlock the power of encrypted computation? Homomorphic encryption is like a magic trick for your data! In this Colab, we’ll learn how to do calculations on secret numbers without ever revealing them. Imagine training a model on data you can’t even see – that’s the power of this mind-bending technology.\n\n\n\n\n\n\n\n14.8.5 Secure Multiparty Communication\n\nCore Idea\nThe overarching goal of Multi-Party Communication (MPC) is to enable different parties to jointly compute a function over their inputs while keeping those inputs private. For example, two organizations may want to collaborate on training a machine learning model by combining their respective data sets. Still, they cannot directly reveal that data due to Privacy or confidentiality constraints. MPC provides protocols and techniques that allow them to achieve the benefits of pooled data for model accuracy without compromising the privacy of each organization’s sensitive data.\nAt a high level, MPC works by carefully splitting the computation into parts that each party can execute independently using their private input. The results are then combined to reveal only the final output of the function and nothing about the intermediate values. Cryptographic techniques are used to guarantee that the partial results remain private provably.\nLet’s take a simple example of an MPC protocol. One of the most basic MPC protocols is the secure addition of two numbers. Each party splits its input into random shares that are secretly distributed. They exchange the shares and locally compute the sum of the shares, which reconstructs the final sum without revealing the individual inputs. For example, if Alice has input x and Bob has input y:\n\nAlice generates random \\(x_1\\) and sets \\(x_2 = x - x_1\\)\nBob generates random \\(y_1\\) and sets \\(y_2 = y - y_1\\)\nAlice sends \\(x_1\\) to Bob, Bob sends \\(y_1\\) to Alice (keeping \\(x_2\\) and \\(y_2\\) secret)\nAlice computes \\(x_2 + y_1 = s_1\\), Bob computes \\(x_1 + y_2 = s_2\\)\n\\(s_1 + s_2 = x + y\\) is the final sum, without revealing \\(x\\) or \\(y\\).\n\nAlice’s and Bob’s individual inputs (\\(x\\) and \\(y\\)) remain private, and each party only reveals one number associated with their original inputs. The random outputs ensure that no information about the original numbers disclosed.\nSecure Comparison: Another basic operation is a secure comparison of two numbers, determining which is greater than the other. This can be done using techniques like Yao’s Garbled Circuits, where the comparison circuit is encrypted to allow joint evaluation of the inputs without leaking them.\nSecure Matrix Multiplication: Matrix operations like multiplication are essential for machine learning. MPC techniques like additive secret sharing can be used to split matrices into random shares, compute products on the shares, and then reconstruct the result.\nSecure Model Training: Distributed machine learning training algorithms like federated averaging can be made secure using MPC. Model updates computed on partitioned data at each node are secretly shared between nodes and aggregated to train the global model without exposing individual updates.\nThe core idea behind MPC protocols is to divide the computation into steps that can be executed jointly without revealing intermediate sensitive data. This is accomplished by combining cryptographic techniques like secret sharing, homomorphic encryption, oblivious transfer, and garbled circuits. MPC protocols enable the collaborative computation of sensitive data while providing provable privacy guarantees. This privacy-preserving capability is essential for many machine learning applications today involving multiple parties that cannot directly share their raw data.\nThe main approaches used in MPC include:\n\nHomomorphic encryption: Special encryption allows computations to be carried out on encrypted data without decrypting it.\nSecret sharing: The private data is divided into random shares distributed to each party. Computations are done locally on the shares and finally reconstructed.\nOblivious transfer: A protocol where a receiver obtains a subset of data from a sender, but the sender does not know which specific data was transferred.\nGarbled circuits: The function to be computed is represented as a Boolean circuit that is encrypted (“garbled”) to allow joint evaluation without revealing inputs.\n\n\n\nTradeoffs\nWhile MPC protocols provide strong privacy guarantees, they come at a high computational cost compared to plain computations. Every secure operation, like addition, multiplication, comparison, etc., requires more processing orders than the equivalent unencrypted operation. This overhead stems from the underlying cryptographic techniques:\n\nIn partially homomorphic encryption, each computation on ciphertexts requires costly public-key operations. Fully homomorphic encryption has even higher overheads.\nSecret sharing divides data into multiple shares, so even basic operations require manipulating many shares.\nOblivious transfer and garbled circuits add masking and encryption to hide data access patterns and execution flows.\nMPC systems require extensive communication and interaction between parties to compute on shares/ciphertexts jointly.\n\nAs a result, MPC protocols can slow down computations by 3-4 orders of magnitude compared to plain implementations. This becomes prohibitively expensive for large datasets and models. Therefore, training machine learning models on encrypted data using MPC remains infeasible today for realistic dataset sizes due to the overhead. Clever optimizations and approximations are needed to make MPC practical.\nOngoing MPC research closes this efficiency gap through cryptographic advances, new algorithms, trusted hardware like SGX enclaves, and leveraging accelerators like GPUs/TPUs. However, in the foreseeable future, some degree of approximation and performance tradeoff is needed to scale MPC to meet the demands of real-world machine learning systems.\n\n\n\n14.8.6 Synthetic Data Generation\n\nCore Idea\nSynthetic data generation has emerged as an important privacy-preserving machine learning approach that allows models to be developed and tested without exposing real user data. The key idea is to train generative models on real-world datasets and then sample from these models to synthesize artificial data that statistically match the original data distribution but does not contain actual user information. For example, a GAN could be trained on a dataset of sensitive medical records to learn the underlying patterns and then used to sample synthetic patient data.\nThe primary challenge of synthesizing data is to ensure adversaries are unable to re-identify the original dataset. A simple approach to achieving synthetic data is adding noise to the original dataset, which still risks privacy leakage. When noise is added to data in the context of differential privacy, sophisticated mechanisms based on the data’s sensitivity are used to calibrate the amount and distribution of noise. Through these mathematically rigorous frameworks, differential Privacy generally guarantees Privacy at some level, which is the primary goal of this privacy-preserving technique. Beyond preserving privacy, synthetic data combats multiple data availability issues such as imbalanced datasets, scarce datasets, and anomaly detection.\nResearchers can freely share this synthetic data and collaborate on modeling without revealing private medical information. Well-constructed synthetic data protects Privacy while providing utility for developing accurate models. Key techniques to prevent reconstructing the original data include adding differential privacy noise during training, enforcing plausibility constraints, and using multiple diverse generative models. Here are some common approaches for generating synthetic data:\n\nGenerative Adversarial Networks (GANs): GANs are an AI algorithm used in unsupervised learning where two neural networks compete against each other in a game. Figure 14.14 is an overview of the GAN system. The generator network (big red box) is responsible for producing the synthetic data, and the discriminator network (yellow box) evaluates the authenticity of the data by distinguishing between fake data created by the generator network and the real data. The generator and discriminator networks learn and update their parameters based on the results. The discriminator acts as a metric on how similar the fake and real data are to one another. It is highly effective at generating realistic data and is a popular approach for generating synthetic data.\n\n\n\n\n\n\n\nFigure 14.14: Flowchart of GANs. Source: Rosa and Papa (2021).\n\n\nRosa, Gustavo H. de, and João P. Papa. 2021. “A Survey on Text Generation Using Generative Adversarial Networks.” Pattern Recogn. 119 (November): 108098. https://doi.org/10.1016/j.patcog.2021.108098.\n\n\n\nVariational Autoencoders (VAEs): VAEs are neural networks capable of learning complex probability distributions and balancing data generation quality and computational efficiency. They encode data into a latent space where they learn the distribution to decode the data back.\nData Augmentation: This involves transforming existing data to create new, altered data. For example, flipping, rotating, and scaling (uniformly or non-uniformly) original images can help create a more diverse, robust image dataset before training an ML model.\nSimulations: Mathematical models can simulate real-world systems or processes to mimic real-world phenomena. This is highly useful in scientific research, urban planning, and economics.\n\n\n\nBenefits\nWhile synthetic data may be necessary due to Privacy or compliance risks, it is widely used in machine learning models when available data is of poor quality, scarce, or inaccessible. Synthetic data offers more efficient and effective development by streamlining robust model training, testing, and deployment processes. It allows researchers to share models more widely without breaching privacy laws and regulations. Collaboration between users of the same dataset will be facilitated, which will help broaden the capabilities and advancements in ML research.\nThere are several motivations for using synthetic data in machine learning:\n\nPrivacy and compliance: Synthetic data avoids exposing personal information, allowing more open sharing and collaboration. This is important when working with sensitive datasets like healthcare records or financial information.\nData scarcity: When insufficient real-world data is available, synthetic data can augment training datasets. This improves model accuracy when limited data is a bottleneck.\nModel testing: Synthetic data provides privacy-safe sandboxes for testing model performance, debugging issues, and monitoring for bias.\nData labeling: High-quality labeled training data is often scarce and expensive. Synthetic data can help auto-generate labeled examples.\n\n\n\nTradeoffs\nWhile synthetic data tries to remove any evidence of the original dataset, privacy leakage is still a risk since the synthetic data mimics the original data. The statistical information and distribution are similar, if not the same, between the original and synthetic data. By resampling from the distribution, adversaries may still be able to recover the original training samples. Due to their inherent learning processes and complexities, neural networks might accidentally reveal sensitive information about the original training data.\nA core challenge with synthetic data is the potential gap between synthetic and real-world data distributions. Despite advancements in generative modeling techniques, synthetic data may only partially capture real data’s complexity, diversity, and nuanced patterns. This can limit the utility of synthetic data for robustly training machine learning models. Rigorously evaluating synthetic data quality through adversary methods and comparing model performance to real data benchmarks helps assess and improve fidelity. However, inherently, synthetic data remains an approximation.\nAnother critical concern is the privacy risks of synthetic data. Generative models may leak identifiable information about individuals in the training data, which could enable reconstruction of private information. Emerging adversarial attacks demonstrate the challenges in preventing identity leakage from synthetic data generation pipelines. Techniques like differential Privacy can help safeguard Privacy but come with tradeoffs in data utility. There is an inherent tension between producing useful synthetic data and fully protecting sensitive training data, which must be balanced.\nAdditional pitfalls of synthetic data include amplified biases, mislabeling, the computational overhead of training generative models, storage costs, and failure to account for out-of-distribution novel data. While these are secondary to the core synthetic-real gap and privacy risks, they remain important considerations when evaluating the suitability of synthetic data for particular machine-learning tasks. As with any technique, the advantages of synthetic data come with inherent tradeoffs and limitations that require thoughtful mitigation strategies.\n\n\n\n14.8.7 Summary\nWhile all the techniques we have discussed thus far aim to enable privacy-preserving machine learning, they involve distinct mechanisms and tradeoffs. Factors like computational constraints, required trust assumptions, threat models, and data characteristics help guide the selection process for a particular use case. However, finding the right balance between Privacy, accuracy, and efficiency necessitates experimentation and empirical evaluation for many applications. Table 14.2 is a comparison table of the key privacy-preserving machine learning techniques and their pros and cons:\n\n\n\nTable 14.2: Comparing techniques for privacy-preserving machine learning.\n\n\n\n\n\n\n\n\n\n\nTechnique\nPros\nCons\n\n\n\n\nDifferential Privacy\n\nStrong formal privacy guarantees\nRobust to auxiliary data attacks\nVersatile for many data types and analyses\n\n\nAccuracy loss from noise addition\nComputational overhead for sensitivity analysis and noise generation\n\n\n\nFederated Learning\n\nAllows collaborative learning without sharing raw data\nData remains decentralized improving security\nNo need for encrypted computation\n\n\nIncreased communication overhead\nPotentially slower model convergence\nUneven client device capabilities\n\n\n\nSecure Multi-Party Computation\n\nEnables joint computation on sensitive data\nProvides cryptographic privacy guarantees\nFlexible protocols for various functions\n\n\nVery high computational overhead\nComplexity of implementation\nAlgorithmic constraints on function depth\n\n\n\nHomomorphic Encryption\n\nAllows computation on encrypted data\nPrevents intermediate state exposure\n\n\nExtremely high computational cost\nComplex cryptographic implementations\nRestrictions on function types\n\n\n\nSynthetic Data Generation\n\nEnables data sharing without leakage\nMitigates data scarcity problems\n\n\nSynthetic-real gap in distributions\nPotential for reconstructing private data\nBiases and labeling challenges", "crumbs": [ "Advanced Topics", "14  Security & Privacy" @@ -1632,7 +1632,7 @@ "href": "contents/responsible_ai/responsible_ai.html#cloud-edge-tiny-ml", "title": "15  Responsible AI", "section": "15.4 Cloud, Edge & Tiny ML", - "text": "15.4 Cloud, Edge & Tiny ML\nWhile these principles broadly apply across AI systems, certain responsible AI considerations are unique or pronounced when dealing with machine learning on embedded devices versus traditional server-based modeling. Therefore, we present a high-level taxonomy comparing responsible AI considerations across cloud, edge, and TinyML systems.\n\n15.4.1 Summary\nTable 15.1 summarizes how responsible AI principles manifest differently across cloud, edge, and TinyML architectures and how core considerations tie into their unique capabilities and limitations. Each environment’s constraints and tradeoffs shape how we approach transparency, accountability, governance, and other pillars of responsible AI.\n\n\n\nTable 15.1: Comparison of key principles in Cloud ML, Edge ML, and TinyML.\n\n\n\n\n\n\n\n\n\n\n\nPrinciple\nCloud ML\nEdge ML\nTinyML\n\n\n\n\nExplainability\nComplex models supported\nLightweight required\nSevere limits\n\n\nFairness\nBroad data available\nOn-device biases\nLimited data labels\n\n\nPrivacy\nCloud data vulnerabilities\nMore sensitive data\nData dispersed\n\n\nSafety\nHacking threats\nReal-world interaction\nAutonomous devices\n\n\nAccountability\nCorporate policies\nSupply chain issues\nComponent tracing\n\n\nGovernance\nExternal oversight feasible\nSelf-governance needed\nProtocol constraints\n\n\n\n\n\n\n\n\n15.4.2 Explainability\nFor cloud-based machine learning, explainability techniques can leverage significant compute resources, enabling complex methods like SHAP values or sampling-based approaches to interpret model behaviors. For example, Microsoft’s InterpretML toolkit provides explainability techniques tailored for cloud environments.\nHowever, edge ML operates on resource-constrained devices, requiring more lightweight explainability methods that can run locally without excessive latency. Techniques like LIME (Ribeiro, Singh, and Guestrin 2016) approximate model explanations using linear models or decision trees to avoid expensive computations, which makes them ideal for resource-constrained devices. However, LIME requires training hundreds to even thousands of models to generate good explanations, which is often infeasible given edge computing constraints. In contrast, saliency-based methods are often much faster in practice, only requiring a single forward pass through the network to estimate feature importance. This greater efficiency makes such methods better suited to edge devices with limited compute resources where low-latency explanations are critical.\nGiven tiny hardware capabilities, embedded systems pose the most significant challenges for explainability. More compact models and limited data make inherent model transparency easier. Explaining decisions may not be feasible on high-size and power-optimized microcontrollers. DARPA’s Transparent Computing program aims to develop extremely low overhead explainability, especially for TinyML devices like sensors and wearables.\n\n\n15.4.3 Fairness\nFor cloud machine learning, vast datasets and computing power enable detecting biases across large heterogeneous populations and mitigating them through techniques like re-weighting data samples. However, biases may emerge from the broad behavioral data used to train cloud models. Amazon’s Fairness Flow framework helps assess cloud ML fairness.\nEdge ML relies on limited on-device data, making analyzing biases across diverse groups harder. However, edge devices interact closely with individuals, providing an opportunity to adapt locally for fairness. Google’s Federated Learning distributes model training across devices to incorporate individual differences.\nTinyML poses unique challenges for fairness with highly dispersed specialized hardware and minimal training data. Bias testing is difficult across diverse devices. Collecting representative data from many devices to mitigate bias has scale and privacy hurdles. DARPA’s Assured Neuro Symbolic Learning and Reasoning (ANSR) efforts are geared toward developing fairness techniques given extreme hardware constraints.\n\n\n15.4.4 Safety\nKey safety risks for cloud ML include model hacking, data poisoning, and malware disrupting cloud services. Robustness techniques like adversarial training, anomaly detection, and diversified models aim to harden cloud ML against attacks. Redundancy can help prevent single points of failure.\nEdge ML and TinyML interact with the physical world, so reliability and safety validation are critical. Rigorous testing platforms like Foretellix synthetically generate edge scenarios to validate safety. TinyML safety is magnified by autonomous devices with limited supervision. TinyML safety often relies on collective coordination - swarms of drones maintain safety through redundancy. Physical control barriers also constrain unsafe TinyML device behaviors.\nIn summary, safety is crucial but manifests differently in each domain. Cloud ML guards against hacking, edge ML interacts physically, so reliability is key, and TinyML leverages distributed coordination for safety. Understanding the nuances guides appropriate safety techniques.\n\n\n15.4.5 Accountability\nCloud ML’s accountability centers on corporate practices like responsible AI committees, ethical charters, and processes to address harmful incidents. Third-party audits and external government oversight promote cloud ML accountability.\nEdge ML accountability is more complex with distributed devices and supply chain fragmentation. Companies are accountable for devices, but components come from various vendors. Industry standards help coordinate edge ML accountability across stakeholders.\nWith TinyML, accountability mechanisms must be traced across long, complex supply chains of integrated circuits, sensors, and other hardware. TinyML certification schemes help track component provenance. Trade associations should ideally promote shared accountability for ethical TinyML.\n\n\n15.4.6 Governance\nOrganizations institute internal governance for cloud ML, such as ethics boards, audits, and model risk management. But external governance also oversees cloud ML, like regulations on bias and transparency such as the AI Bill of Rights, General Data Protection Regulation (GDPR), and California Consumer Protection Act (CCPA). Third-party auditing supports cloud ML governance.\nEdge ML is more decentralized, requiring responsible self-governance by developers and companies deploying models locally. Industry associations coordinate governance across edge ML vendors, and open software helps align incentives for ethical edge ML.\nExtreme decentralization and complexity make external governance infeasible with TinyML. TinyML relies on protocols and standards for self-governance baked into model design and hardware. Cryptography enables the provable trustworthiness of TinyML devices.\n\n\n15.4.7 Privacy\nFor cloud ML, vast amounts of user data are concentrated in the cloud, creating risks of exposure through breaches. Differential privacy techniques add noise to cloud data to preserve privacy. Strict access controls and encryption protect cloud data at rest and in transit.\nEdge ML moves data processing onto user devices, reducing aggregated data collection but increasing potential sensitivity as personal data resides on the device. Apple uses on-device ML and differential privacy to train models while minimizing data sharing. Data anonymization and secure enclaves protect on-device data.\nTinyML distributes data across many resource-constrained devices, making centralized breaches unlikely and making scale anonymization challenging. Data minimization and using edge devices as intermediaries help TinyML privacy.\nSo, while cloud ML must protect expansive centralized data, edge ML secures sensitive on-device data, and TinyML aims for minimal distributed data sharing due to constraints. While privacy is vital throughout, techniques must match the environment. Understanding nuances allows for selecting appropriate privacy preservation approaches.", + "text": "15.4 Cloud, Edge & Tiny ML\nWhile these principles broadly apply across AI systems, certain responsible AI considerations are unique or pronounced when dealing with machine learning on embedded devices versus traditional server-based modeling. Therefore, we present a high-level taxonomy comparing responsible AI considerations across cloud, edge, and TinyML systems.\n\n15.4.1 Summary\nTable 15.1 summarizes how responsible AI principles manifest differently across cloud, edge, and TinyML architectures and how core considerations tie into their unique capabilities and limitations. Each environment’s constraints and tradeoffs shape how we approach transparency, accountability, governance, and other pillars of responsible AI.\n\n\n\nTable 15.1: Comparison of key principles in Cloud ML, Edge ML, and TinyML.\n\n\n\n\n\n\n\n\n\n\n\nPrinciple\nCloud ML\nEdge ML\nTinyML\n\n\n\n\nExplainability\nComplex models supported\nLightweight required\nSevere limits\n\n\nFairness\nBroad data available\nOn-device biases\nLimited data labels\n\n\nPrivacy\nCloud data vulnerabilities\nMore sensitive data\nData dispersed\n\n\nSafety\nHacking threats\nReal-world interaction\nAutonomous devices\n\n\nAccountability\nCorporate policies\nSupply chain issues\nComponent tracing\n\n\nGovernance\nExternal oversight feasible\nSelf-governance needed\nProtocol constraints\n\n\n\n\n\n\n\n\n15.4.2 Explainability\nFor cloud-based machine learning, explainability techniques can leverage significant compute resources, enabling complex methods like SHAP values or sampling-based approaches to interpret model behaviors. For example, Microsoft’s InterpretML toolkit provides explainability techniques tailored for cloud environments.\nHowever, edge ML operates on resource-constrained devices, requiring more lightweight explainability methods that can run locally without excessive latency. Techniques like LIME (Ribeiro, Singh, and Guestrin 2016) approximate model explanations using linear models or decision trees to avoid expensive computations, which makes them ideal for resource-constrained devices. However, LIME requires training hundreds to even thousands of models to generate good explanations, which is often infeasible given edge computing constraints. In contrast, saliency-based methods are often much faster in practice, only requiring a single forward pass through the network to estimate feature importance. This greater efficiency makes such methods better suited to edge devices with limited compute resources where low-latency explanations are critical.\nGiven tiny hardware capabilities, embedded systems pose the most significant challenges for explainability. More compact models and limited data make inherent model transparency easier. Explaining decisions may not be feasible on high-size and power-optimized microcontrollers. DARPA’s Transparent Computing program tries to develop extremely low overhead explainability, especially for TinyML devices like sensors and wearables.\n\n\n15.4.3 Fairness\nFor cloud machine learning, vast datasets and computing power enable detecting biases across large heterogeneous populations and mitigating them through techniques like re-weighting data samples. However, biases may emerge from the broad behavioral data used to train cloud models. Amazon’s Fairness Flow framework helps assess cloud ML fairness.\nEdge ML relies on limited on-device data, making analyzing biases across diverse groups harder. However, edge devices interact closely with individuals, providing an opportunity to adapt locally for fairness. Google’s Federated Learning distributes model training across devices to incorporate individual differences.\nTinyML poses unique challenges for fairness with highly dispersed specialized hardware and minimal training data. Bias testing is difficult across diverse devices. Collecting representative data from many devices to mitigate bias has scale and privacy hurdles. DARPA’s Assured Neuro Symbolic Learning and Reasoning (ANSR) efforts are geared toward developing fairness techniques given extreme hardware constraints.\n\n\n15.4.4 Safety\nKey safety risks for cloud ML include model hacking, data poisoning, and malware disrupting cloud services. Robustness techniques like adversarial training, anomaly detection, and diversified models aim to harden cloud ML against attacks. Redundancy can help prevent single points of failure.\nEdge ML and TinyML interact with the physical world, so reliability and safety validation are critical. Rigorous testing platforms like Foretellix synthetically generate edge scenarios to validate safety. TinyML safety is magnified by autonomous devices with limited supervision. TinyML safety often relies on collective coordination - swarms of drones maintain safety through redundancy. Physical control barriers also constrain unsafe TinyML device behaviors.\nIn summary, safety is crucial but manifests differently in each domain. Cloud ML guards against hacking, edge ML interacts physically, so reliability is key, and TinyML leverages distributed coordination for safety. Understanding the nuances guides appropriate safety techniques.\n\n\n15.4.5 Accountability\nCloud ML’s accountability centers on corporate practices like responsible AI committees, ethical charters, and processes to address harmful incidents. Third-party audits and external government oversight promote cloud ML accountability.\nEdge ML accountability is more complex with distributed devices and supply chain fragmentation. Companies are accountable for devices, but components come from various vendors. Industry standards help coordinate edge ML accountability across stakeholders.\nWith TinyML, accountability mechanisms must be traced across long, complex supply chains of integrated circuits, sensors, and other hardware. TinyML certification schemes help track component provenance. Trade associations should ideally promote shared accountability for ethical TinyML.\n\n\n15.4.6 Governance\nOrganizations institute internal governance for cloud ML, such as ethics boards, audits, and model risk management. But external governance also oversees cloud ML, like regulations on bias and transparency such as the AI Bill of Rights, General Data Protection Regulation (GDPR), and California Consumer Protection Act (CCPA). Third-party auditing supports cloud ML governance.\nEdge ML is more decentralized, requiring responsible self-governance by developers and companies deploying models locally. Industry associations coordinate governance across edge ML vendors, and open software helps align incentives for ethical edge ML.\nExtreme decentralization and complexity make external governance infeasible with TinyML. TinyML relies on protocols and standards for self-governance baked into model design and hardware. Cryptography enables the provable trustworthiness of TinyML devices.\n\n\n15.4.7 Privacy\nFor cloud ML, vast amounts of user data are concentrated in the cloud, creating risks of exposure through breaches. Differential privacy techniques add noise to cloud data to preserve privacy. Strict access controls and encryption protect cloud data at rest and in transit.\nEdge ML moves data processing onto user devices, reducing aggregated data collection but increasing potential sensitivity as personal data resides on the device. Apple uses on-device ML and differential privacy to train models while minimizing data sharing. Data anonymization and secure enclaves protect on-device data.\nTinyML distributes data across many resource-constrained devices, making centralized breaches unlikely and making scale anonymization challenging. Data minimization and using edge devices as intermediaries help TinyML privacy.\nSo, while cloud ML must protect expansive centralized data, edge ML secures sensitive on-device data, and TinyML aims for minimal distributed data sharing due to constraints. While privacy is vital throughout, techniques must match the environment. Understanding nuances allows for selecting appropriate privacy preservation approaches.", "crumbs": [ "Advanced Topics", "15  Responsible AI" @@ -1808,7 +1808,7 @@ "href": "contents/sustainable_ai/sustainable_ai.html#case-study-google-4ms", "title": "16  Sustainable AI", "section": "16.10 Case Study: Google’s 4Ms", - "text": "16.10 Case Study: Google’s 4Ms\nOver the past decade, AI has rapidly moved from academic research to large-scale production systems powering numerous Google products and services. As AI models and workloads have grown exponentially in size and computational demands, concerns have emerged about their energy consumption and carbon footprint. Some researchers predicted runaway growth in ML’s energy appetite that could outweigh efficiencies gained from improved algorithms and hardware (Thompson et al. 2021).\n\nThompson, Neil C., Kristjan Greenewald, Keeheon Lee, and Gabriel F. Manso. 2021. “Deep Learning’s Diminishing Returns: The Cost of Improvement Is Becoming Unsustainable.” IEEE Spectr. 58 (10): 50–55. https://doi.org/10.1109/mspec.2021.9563954.\nHowever, Google’s production data reveals a different story—AI represents a steady 10-15% of total company energy usage from 2019 to 2021. This case study analyzes how Google applied a systematic approach leveraging four best practices—what they term the “4 Ms” of model efficiency, machine optimization, mechanization through cloud computing, and mapping to green locations—to bend the curve on emissions from AI workloads.\nThe scale of Google’s AI usage makes it an ideal case study. In 2021 alone, the company trained models like the 1.2 trillion-parameter GLam model. Analyzing how the application of AI has been paired with rapid efficiency gains in this environment helps us by providing a logical blueprint for the broader AI field to follow.\nBy transparently publishing detailed energy usage statistics, adopting rates of carbon-free clouds and renewables purchases, and more, alongside its technical innovations, Google has enabled outside researchers to measure progress accurately. Their study in the ACM CACM (Patterson et al. 2022) highlights how the company’s multipronged approach shows that runaway AI energy consumption predictions can be overcome by focusing engineering efforts on sustainable development patterns. The pace of improvements also suggests ML’s efficiency gains are just starting.\n\nPatterson, David, Joseph Gonzalez, Urs Holzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, and Jeff Dean. 2022. “The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink.” Computer 55 (7): 18–28. https://doi.org/10.1109/mc.2022.3148714.\n\n16.10.1 Google’s 4M Best Practices\nTo curb emissions from their rapidly expanding AI workloads, Google engineers systematically identified four best practice areas–termed the “4 Ms”–where optimizations could compound to reduce the carbon footprint of ML:\n\nModel - Selecting efficient AI model architectures can reduce computation by 5-10X with no loss in model quality. Google has extensively researched developing sparse models and neural architecture search to create more efficient models like the Evolved Transformer and Primer.\nMachine—Using hardware optimized for AI over general-purpose systems improves performance per watt by 2-5X. Google’s Tensor Processing Units (TPUs) led to 5-13X better carbon efficiency versus GPUs not optimized for ML.\nMechanization—By leveraging cloud computing systems tailored for high utilization over conventional on-premise data centers, energy costs are reduced by 1.4-2X. Google cites its data center’s power usage effectiveness as outpacing industry averages.\nMap - Choosing data center locations with low-carbon electricity reduces gross emissions by another 5-10X. Google provides real-time maps highlighting the percentage of renewable energy used by its facilities.\n\nTogether, these practices created drastic compound efficiency gains. For example, optimizing the Transformer AI model on TPUs in a sustainable data center location cut energy use by 83. It lowered \\(\\textrm{CO}_2\\) emissions by a factor of 747.\n\n\n16.10.2 Significant Results\nDespite exponential growth in AI adoption across products and services, Google’s efforts to improve the carbon efficiency of ML have produced measurable gains, helping to restrain overall energy appetite. One key data point highlighting this progress is that AI workloads have remained a steady 10% to 15% of total company energy use from 2019 to 2021. As AI became integral to more Google offerings, overall compute cycles dedicated to AI grew substantially. However, efficiencies in algorithms, specialized hardware, data center design, and flexible geography allowed sustainability to keep pace—with AI representing just a fraction of total data center electricity over years of expansion.\nOther case studies underscore how an engineering focus on sustainable AI development patterns enabled rapid quality improvements in lockstep with environmental gains. For example, the natural language processing model GPT-3 was viewed as state-of-the-art in mid-2020. Yet its successor GLaM improved accuracy while cutting training compute needs and using cleaner data center energy–cutting CO2 emissions by a factor of 14 in just 18 months of model evolution.\nSimilarly, Google found past published speculation missing the mark on ML’s energy appetite by factors of 100 to 100,000X due to a lack of real-world metrics. By transparently tracking optimization impact, Google hoped to motivate efficiency while preventing overestimated extrapolations about ML’s environmental toll.\nThese data-driven case studies show how companies like Google are steering AI advancements toward sustainable trajectories and improving efficiency to outpace adoption growth. With further efforts around lifecycle analysis, inference optimization, and renewable expansion, companies can aim to accelerate progress, giving evidence that ML’s clean potential is only just being unlocked by current gains.\n\n\n16.10.3 Further Improvements\nWhile Google has made measurable progress in restraining the carbon footprint of its AI operations, the company recognizes further efficiency gains will be vital for responsible innovation given the technology’s ongoing expansion.\nOne area of focus is showing how advances are often incorrectly viewed as increasing unsustainable computing—like neural architecture search (NAS) to find optimized models— spur downstream savings, outweighing their upfront costs. Despite expending more energy on model discovery rather than hand-engineering, NAS cuts lifetime emissions by producing efficient designs callable across countless applications.\nAdditionally, the analysis reveals that focusing sustainability efforts on data center and server-side optimization makes sense, given the dominant energy draw versus consumer devices. Though Google aims to shrink inference impacts across processors like mobile phones, priority rests on improving training cycles and data center renewables procurement for maximal effect.\nTo that end, Google’s progress in pooling computing inefficiently designed cloud facilities highlights the value of scale and centralization. As more workloads shift away from inefficient on-premise servers, internet giants’ prioritization of renewable energy—with Google and Facebook matched 100% by renewables since 2017 and 2020, respectively—unlocks compounding emissions cuts.\nTogether, these efforts emphasize that while no resting on laurels is possible, Google’s multipronged approach shows that AI efficiency improvements are only accelerating. Cross-domain initiatives around lifecycle assessment, carbon-conscious development patterns, transparency, and matching rising AI demand with clean electricity supply pave a path toward bending the curve further as adoption grows. The company’s results compel the broader field towards replicating these integrated sustainability pursuits.", + "text": "16.10 Case Study: Google’s 4Ms\nOver the past decade, AI has rapidly moved from academic research to large-scale production systems powering numerous Google products and services. As AI models and workloads have grown exponentially in size and computational demands, concerns have emerged about their energy consumption and carbon footprint. Some researchers predicted runaway growth in ML’s energy appetite that could outweigh efficiencies gained from improved algorithms and hardware (Thompson et al. 2021).\n\nThompson, Neil C., Kristjan Greenewald, Keeheon Lee, and Gabriel F. Manso. 2021. “Deep Learning’s Diminishing Returns: The Cost of Improvement Is Becoming Unsustainable.” IEEE Spectr. 58 (10): 50–55. https://doi.org/10.1109/mspec.2021.9563954.\nHowever, Google’s production data reveals a different story—AI represents a steady 10-15% of total company energy usage from 2019 to 2021. This case study analyzes how Google applied a systematic approach leveraging four best practices—what they term the “4 Ms” of model efficiency, machine optimization, mechanization through cloud computing, and mapping to green locations—to bend the curve on emissions from AI workloads.\nThe scale of Google’s AI usage makes it an ideal case study. In 2021 alone, the company trained models like the 1.2 trillion-parameter GLam model. Analyzing how the application of AI has been paired with rapid efficiency gains in this environment helps us by providing a logical blueprint for the broader AI field to follow.\nBy transparently publishing detailed energy usage statistics, adopting rates of carbon-free clouds and renewables purchases, and more, alongside its technical innovations, Google has enabled outside researchers to measure progress accurately. Their study in the ACM CACM (Patterson et al. 2022) highlights how the company’s multipronged approach shows that runaway AI energy consumption predictions can be overcome by focusing engineering efforts on sustainable development patterns. The pace of improvements also suggests ML’s efficiency gains are just starting.\n\nPatterson, David, Joseph Gonzalez, Urs Holzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, and Jeff Dean. 2022. “The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink.” Computer 55 (7): 18–28. https://doi.org/10.1109/mc.2022.3148714.\n\n16.10.1 Google’s 4M Best Practices\nTo curb emissions from their rapidly expanding AI workloads, Google engineers systematically identified four best practice areas–termed the “4 Ms”–where optimizations could compound to reduce the carbon footprint of ML:\n\nModel - Selecting efficient AI model architectures can reduce computation by 5-10X with no loss in model quality. Google has extensively researched developing sparse models and neural architecture search to create more efficient models like the Evolved Transformer and Primer.\nMachine—Using hardware optimized for AI over general-purpose systems improves performance per watt by 2-5X. Google’s Tensor Processing Units (TPUs) led to 5-13X better carbon efficiency versus GPUs not optimized for ML.\nMechanization—By leveraging cloud computing systems tailored for high utilization over conventional on-premise data centers, energy costs are reduced by 1.4-2X. Google cites its data center’s power usage effectiveness as outpacing industry averages.\nMap - Choosing data center locations with low-carbon electricity reduces gross emissions by another 5-10X. Google provides real-time maps highlighting the percentage of renewable energy used by its facilities.\n\nTogether, these practices created drastic compound efficiency gains. For example, optimizing the Transformer AI model on TPUs in a sustainable data center location cut energy use by 83. It lowered \\(\\textrm{CO}_2\\) emissions by a factor of 747.\n\n\n16.10.2 Significant Results\nDespite exponential growth in AI adoption across products and services, Google’s efforts to improve the carbon efficiency of ML have produced measurable gains, helping to restrain overall energy appetite. One key data point highlighting this progress is that AI workloads have remained a steady 10% to 15% of total company energy use from 2019 to 2021. As AI became integral to more Google offerings, overall compute cycles dedicated to AI grew substantially. However, efficiencies in algorithms, specialized hardware, data center design, and flexible geography allowed sustainability to keep pace—with AI representing just a fraction of total data center electricity over years of expansion.\nOther case studies underscore how an engineering focus on sustainable AI development patterns enabled rapid quality improvements in lockstep with environmental gains. For example, the natural language processing model GPT-3 was viewed as state-of-the-art in mid-2020. Yet its successor GLaM improved accuracy while cutting training compute needs and using cleaner data center energy–cutting CO2 emissions by a factor of 14 in just 18 months of model evolution.\nSimilarly, Google found past published speculation missing the mark on ML’s energy appetite by factors of 100 to 100,000X due to a lack of real-world metrics. By transparently tracking optimization impact, Google hoped to motivate efficiency while preventing overestimated extrapolations about ML’s environmental toll.\nThese data-driven case studies show how companies like Google are steering AI advancements toward sustainable trajectories and improving efficiency to outpace adoption growth. With further efforts around lifecycle analysis, inference optimization, and renewable expansion, companies can aim to accelerate progress, giving evidence that ML’s clean potential is only just being unlocked by current gains.\n\n\n16.10.3 Further Improvements\nWhile Google has made measurable progress in restraining the carbon footprint of its AI operations, the company recognizes further efficiency gains will be vital for responsible innovation given the technology’s ongoing expansion.\nOne area of focus is showing how advances are often incorrectly viewed as increasing unsustainable computing—like neural architecture search (NAS) to find optimized models— spur downstream savings, outweighing their upfront costs. Despite expending more energy on model discovery rather than hand-engineering, NAS cuts lifetime emissions by producing efficient designs callable across countless applications.\nAdditionally, the analysis reveals that focusing sustainability efforts on data center and server-side optimization makes sense, given the dominant energy draw versus consumer devices. Though Google shrinks inference impacts across processors like mobile phones, priority rests on improving training cycles and data center renewables procurement for maximal effect.\nTo that end, Google’s progress in pooling computing inefficiently designed cloud facilities highlights the value of scale and centralization. As more workloads shift away from inefficient on-premise servers, internet giants’ prioritization of renewable energy—with Google and Facebook matched 100% by renewables since 2017 and 2020, respectively—unlocks compounding emissions cuts.\nTogether, these efforts emphasize that while no resting on laurels is possible, Google’s multipronged approach shows that AI efficiency improvements are only accelerating. Cross-domain initiatives around lifecycle assessment, carbon-conscious development patterns, transparency, and matching rising AI demand with clean electricity supply pave a path toward bending the curve further as adoption grows. The company’s results compel the broader field towards replicating these integrated sustainability pursuits.", "crumbs": [ "Advanced Topics", "16  Sustainable AI" @@ -1830,7 +1830,7 @@ "href": "contents/sustainable_ai/sustainable_ai.html#policy-and-regulatory-considerations", "title": "16  Sustainable AI", "section": "16.12 Policy and Regulatory Considerations", - "text": "16.12 Policy and Regulatory Considerations\n\n16.12.1 Measurement and Reporting Mandates\nOne policy mechanism that is increasingly relevant for AI systems is measurement and reporting requirements regarding energy consumption and carbon emissions. Mandated metering, auditing, disclosures, and more rigorous methodologies aligned to sustainability metrics can help address information gaps hindering efficiency optimizations.\nSimultaneously, national or regional policies require companies above a certain size to use AI in their products or backend systems to report energy consumption or emissions associated with major AI workloads. Organizations like the Partnership on AI, IEEE, and NIST could help shape standardized methodologies. More complex proposals involve defining consistent ways to measure computational complexity, data center PUE, carbon intensity of energy supply, and efficiencies gained through AI-specific hardware.\nReporting obligations for public sector users procuring AI services—such as through proposed legislation in Europe—could also increase transparency. However, regulators must balance the additional measurement burden such mandates place on organizations against ongoing carbon reductions from ingraining sustainability-conscious development patterns.\nTo be most constructive, any measurement and reporting policies should focus on enabling continuous refinement rather than simplistic restrictions or caps. As AI advancements unfold rapidly, nimble governance guardrails that embed sustainability considerations into normal evaluation metrics can motivate positive change. However, overprescription risks constraining innovation if requirements grow outdated. AI efficiency policy aims to accelerate progress industry-wide by combining flexibility with appropriate transparency guardrails.\n\n\n16.12.2 Restriction Mechanisms\nIn addition to reporting mandates, policymakers have several restriction mechanisms that could directly shape how AI systems are developed and deployed to curb emissions:\nCaps on Computing Emissions: The European Commission’s proposed AI Act takes a horizontal approach that could allow setting economy-wide caps on the volume of computing power available for training AI models. Like emissions trading systems, caps aim to disincentivize extensive computing over sustainability indirectly. However, model quality could be improved to provide more pathways for procuring additional capacity.\nConditioning Access to Public Resources: Some experts have proposed incentives like only allowing access to public datasets or computing power for developing fundamentally efficient models rather than extravagant architectures. For example, the MLCommons benchmarking consortium founded by major tech firms could formally integrate efficiency into its standardized leaderboard metrics—however, conditioned access risks limiting innovation.\nFinancial Mechanisms: Analogous to carbon taxes on polluting industries, fees applied per unit of AI-related compute consumption could discourage unnecessary model scaling while funding efficiency innovations. Tax credits could alternatively reward organizations pioneering more accurate but compact AI techniques. However, financial tools require careful calibration between revenue generation and fairness and not over-penalizing productive uses of AI.\nTechnology Bans: If measurement consistently pinned extreme emissions on specific applications of AI without paths for remediation, outright bans present a tool of last resort for policymakers. However, given AI’s dual use, defining harmful versus beneficial deployments proves complex, necessitating holistic impact assessment before concluding no redeeming value exists. Banning promising technologies risks unintended consequences and requires caution.\n\n\n16.12.3 Government Incentives\nIt is a common practice for governments to provide tax or other incentives to consumers or businesses when contributing to more sustainable technological practices. Such incentives already exist in the US for adopting solar panels or energy-efficient buildings. To the best of our knowledge, no such tax incentives exist for AI-specific development practices yet.\nAnother potential incentive program that is beginning to be explored is using government grants to fund Green AI projects. For example, in Spain, 300 million euros have been allocated to specifically fund projects in AI and sustainability. Government incentives are a promising avenue to encourage sustainable business and consumer behavior practices, but careful thought is required to determine how those incentives will fit into market demands (Cohen, Lobel, and Perakis 2016).\n\nCohen, Maxime C., Ruben Lobel, and Georgia Perakis. 2016. “The Impact of Demand Uncertainty on Consumer Subsidies for Green Technology Adoption.” Manage. Sci. 62 (5): 1235–58. https://doi.org/10.1287/mnsc.2015.2173.\n\n\n16.12.4 Self-Regulation\nComplimentary to potential government action, voluntary self-governance mechanisms allow the AI community to pursue sustainability ends without top-down intervention:\nRenewables Commitments: Large AI practitioners like Google, Microsoft, Amazon, and Facebook have pledged to procure enough renewable electricity to match 100% of their energy demands. These commitments unlock compounding emissions cuts as compute scales up. Formalizing such programs incentivizes green data center regions. However, there are critiques on whether these pledges are enough (Monyei and Jenkins 2018).\n\nMonyei, Chukwuka G., and Kirsten E. H. Jenkins. 2018. “Electrons Have No Identity: Setting Right Misrepresentations in Google and Apple’s Clean Energy Purchasing.” Energy Research &Amp; Social Science 46 (December): 48–51. https://doi.org/10.1016/j.erss.2018.06.015.\nInternal Carbon Prices: Some organizations use shadow prices on carbon emissions to represent environmental costs in capital allocation decisions between AI projects. If modeled effectively, theoretical charges on development carbon footprints steer funding toward efficient innovations rather than solely accuracy gains.\nEfficiency Development Checklists: Groups like the AI Sustainability Coalition suggest voluntary checklist templates highlighting model design choices, hardware configurations, and other factors architects can tune per application to restrain emissions. Organizations can drive change by ingraining sustainability as a primary success metric alongside accuracy and cost.\nIndependent Auditing: Even absent public disclosure mandates, firms specializing in technology sustainability audits help AI developers identify waste, create efficiency roadmaps, and benchmark progress via impartial reviews. Structuring such audits into internal governance procedures or the procurement process expands accountability.\n\n\n16.12.5 Global Considerations\nWhile measurement, restrictions, incentives, and self-regulation represent potential policy mechanisms for furthering AI sustainability, fragmentation across national regimes risks unintended consequences. As with other technology policy domains, divergence between regions must be carefully managed.\nFor example, due to regional data privacy concerns, OpenAI barred European users from accessing its viral ChatGPT chatbot. This came after the EU’s proposed AI Act signaled a precautionary approach, allowing the EC to ban certain high-risk AI uses and enforcing transparency rules that create uncertainty for releasing brand new models. However, it would be wise to caution against regulator action as it could inadvertently limit European innovation if regimes with lighter-touch regulation attract more private-sector AI research spending and talent. Finding common ground is key.\nThe OECD principles on AI and the United Nations frameworks underscore universally agreed-upon tenets all national policies should uphold: transparency, accountability, bias mitigation, and more. Constructively embedding sustainability as a core principle for responsible AI within international guidance can motivate unified action without sacrificing flexibility across divergent legal systems. Avoiding race-to-the-bottom dynamics hinges on enlightened multilateral cooperation.", + "text": "16.12 Policy and Regulatory Considerations\n\n16.12.1 Measurement and Reporting Mandates\nOne policy mechanism that is increasingly relevant for AI systems is measurement and reporting requirements regarding energy consumption and carbon emissions. Mandated metering, auditing, disclosures, and more rigorous methodologies aligned to sustainability metrics can help address information gaps hindering efficiency optimizations.\nSimultaneously, national or regional policies require companies above a certain size to use AI in their products or backend systems to report energy consumption or emissions associated with major AI workloads. Organizations like the Partnership on AI, IEEE, and NIST could help shape standardized methodologies. More complex proposals involve defining consistent ways to measure computational complexity, data center PUE, carbon intensity of energy supply, and efficiencies gained through AI-specific hardware.\nReporting obligations for public sector users procuring AI services—such as through proposed legislation in Europe—could also increase transparency. However, regulators must balance the additional measurement burden such mandates place on organizations against ongoing carbon reductions from ingraining sustainability-conscious development patterns.\nTo be most constructive, any measurement and reporting policies should focus on enabling continuous refinement rather than simplistic restrictions or caps. As AI advancements unfold rapidly, nimble governance guardrails that embed sustainability considerations into normal evaluation metrics can motivate positive change. However, overprescription risks constraining innovation if requirements grow outdated. AI efficiency policy accelerates progress industry-wide by combining flexibility with appropriate transparency guardrails.\n\n\n16.12.2 Restriction Mechanisms\nIn addition to reporting mandates, policymakers have several restriction mechanisms that could directly shape how AI systems are developed and deployed to curb emissions:\nCaps on Computing Emissions: The European Commission’s proposed AI Act takes a horizontal approach that could allow setting economy-wide caps on the volume of computing power available for training AI models. Like emissions trading systems, caps aim to disincentivize extensive computing over sustainability indirectly. However, model quality could be improved to provide more pathways for procuring additional capacity.\nConditioning Access to Public Resources: Some experts have proposed incentives like only allowing access to public datasets or computing power for developing fundamentally efficient models rather than extravagant architectures. For example, the MLCommons benchmarking consortium founded by major tech firms could formally integrate efficiency into its standardized leaderboard metrics—however, conditioned access risks limiting innovation.\nFinancial Mechanisms: Analogous to carbon taxes on polluting industries, fees applied per unit of AI-related compute consumption could discourage unnecessary model scaling while funding efficiency innovations. Tax credits could alternatively reward organizations pioneering more accurate but compact AI techniques. However, financial tools require careful calibration between revenue generation and fairness and not over-penalizing productive uses of AI.\nTechnology Bans: If measurement consistently pinned extreme emissions on specific applications of AI without paths for remediation, outright bans present a tool of last resort for policymakers. However, given AI’s dual use, defining harmful versus beneficial deployments proves complex, necessitating holistic impact assessment before concluding no redeeming value exists. Banning promising technologies risks unintended consequences and requires caution.\n\n\n16.12.3 Government Incentives\nIt is a common practice for governments to provide tax or other incentives to consumers or businesses when contributing to more sustainable technological practices. Such incentives already exist in the US for adopting solar panels or energy-efficient buildings. To the best of our knowledge, no such tax incentives exist for AI-specific development practices yet.\nAnother potential incentive program that is beginning to be explored is using government grants to fund Green AI projects. For example, in Spain, 300 million euros have been allocated to specifically fund projects in AI and sustainability. Government incentives are a promising avenue to encourage sustainable business and consumer behavior practices, but careful thought is required to determine how those incentives will fit into market demands (Cohen, Lobel, and Perakis 2016).\n\nCohen, Maxime C., Ruben Lobel, and Georgia Perakis. 2016. “The Impact of Demand Uncertainty on Consumer Subsidies for Green Technology Adoption.” Manage. Sci. 62 (5): 1235–58. https://doi.org/10.1287/mnsc.2015.2173.\n\n\n16.12.4 Self-Regulation\nComplimentary to potential government action, voluntary self-governance mechanisms allow the AI community to pursue sustainability ends without top-down intervention:\nRenewables Commitments: Large AI practitioners like Google, Microsoft, Amazon, and Facebook have pledged to procure enough renewable electricity to match 100% of their energy demands. These commitments unlock compounding emissions cuts as compute scales up. Formalizing such programs incentivizes green data center regions. However, there are critiques on whether these pledges are enough (Monyei and Jenkins 2018).\n\nMonyei, Chukwuka G., and Kirsten E. H. Jenkins. 2018. “Electrons Have No Identity: Setting Right Misrepresentations in Google and Apple’s Clean Energy Purchasing.” Energy Research &Amp; Social Science 46 (December): 48–51. https://doi.org/10.1016/j.erss.2018.06.015.\nInternal Carbon Prices: Some organizations use shadow prices on carbon emissions to represent environmental costs in capital allocation decisions between AI projects. If modeled effectively, theoretical charges on development carbon footprints steer funding toward efficient innovations rather than solely accuracy gains.\nEfficiency Development Checklists: Groups like the AI Sustainability Coalition suggest voluntary checklist templates highlighting model design choices, hardware configurations, and other factors architects can tune per application to restrain emissions. Organizations can drive change by ingraining sustainability as a primary success metric alongside accuracy and cost.\nIndependent Auditing: Even absent public disclosure mandates, firms specializing in technology sustainability audits help AI developers identify waste, create efficiency roadmaps, and benchmark progress via impartial reviews. Structuring such audits into internal governance procedures or the procurement process expands accountability.\n\n\n16.12.5 Global Considerations\nWhile measurement, restrictions, incentives, and self-regulation represent potential policy mechanisms for furthering AI sustainability, fragmentation across national regimes risks unintended consequences. As with other technology policy domains, divergence between regions must be carefully managed.\nFor example, due to regional data privacy concerns, OpenAI barred European users from accessing its viral ChatGPT chatbot. This came after the EU’s proposed AI Act signaled a precautionary approach, allowing the EC to ban certain high-risk AI uses and enforcing transparency rules that create uncertainty for releasing brand new models. However, it would be wise to caution against regulator action as it could inadvertently limit European innovation if regimes with lighter-touch regulation attract more private-sector AI research spending and talent. Finding common ground is key.\nThe OECD principles on AI and the United Nations frameworks underscore universally agreed-upon tenets all national policies should uphold: transparency, accountability, bias mitigation, and more. Constructively embedding sustainability as a core principle for responsible AI within international guidance can motivate unified action without sacrificing flexibility across divergent legal systems. Avoiding race-to-the-bottom dynamics hinges on enlightened multilateral cooperation.", "crumbs": [ "Advanced Topics", "16  Sustainable AI" @@ -1852,7 +1852,7 @@ "href": "contents/sustainable_ai/sustainable_ai.html#future-directions-and-challenges", "title": "16  Sustainable AI", "section": "16.14 Future Directions and Challenges", - "text": "16.14 Future Directions and Challenges\nAs we look towards the future, the role of AI in environmental sustainability is poised to grow even more significant. AI’s potential to drive advancements in renewable energy, climate modeling, conservation efforts, and more is immense. However, it is a two-sided coin, as we need to overcome several challenges and direct our efforts towards sustainable and responsible AI development.\n\n16.14.1 Future Directions\nOne key future direction is the development of more energy-efficient AI models and algorithms. This involves ongoing research and innovation in areas like model pruning, quantization, and the use of low-precision numerics, as well as developing the hardware to enable full profitability of these innovations. Even further, we look at alternative computing paradigms that do not rely on von-Neumann architectures. More on this topic can be found in the hardware acceleration chapter. The goal is to create AI systems that deliver high performance while minimizing energy consumption and carbon emissions.\nAnother important direction is the integration of renewable energy sources into AI infrastructure. As data centers continue to be major contributors to AI’s carbon footprint, transitioning to renewable energy sources like solar and wind is crucial. Developments in long-term, sustainable energy storage, such as Ambri, an MIT spinoff, could enable this transition. This requires significant investment and collaboration between tech companies, energy providers, and policymakers.\n\n\n16.14.2 Challenges\nDespite these promising directions, several challenges need to be addressed. One of the major challenges is the need for consistent standards and methodologies for measuring and reporting the environmental impact of AI. These methods must capture the complexity of the life cycles of AI models and system hardware. Next, efficient and environmentally sustainable AI infrastructure and system hardware are needed. This consists of three components. It aims to maximize the utilization of accelerator and system resources, prolong the lifetime of AI infrastructure, and design systems hardware with environmental impact in mind.\nOn the software side, we should trade off experimentation and the subsequent training cost. Techniques such as neural architecture search and hyperparameter optimization can be used for design space exploration. However, these are often very resource-intensive. Efficient experimentation can significantly reduce the environmental footprint overhead. Next, methods to reduce wasted training efforts should be explored.\nTo improve model quality, we often scale the dataset. However, the increased system resources required for data storage and ingestion caused by this scaling have a significant environmental impact (Wu et al. 2022). A thorough understanding of the rate at which data loses its predictive value and devising data sampling strategies is important.\n\nWu, Carole-Jean, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, et al. 2022. “Sustainable Ai: Environmental Implications, Challenges and Opportunities.” Proceedings of Machine Learning and Systems 4: 795–813.\nData gaps also pose a significant challenge. Without companies and governments openly sharing detailed and accurate data on energy consumption, carbon emissions, and other environmental impacts, it isn’t easy to develop effective strategies for sustainable AI.\nFinally, the fast pace of AI development requires an agile approach to the policy imposed on these systems. The policy should ensure sustainable development without constraining innovation. This requires experts in all domains of AI, environmental sciences, energy, and policy to work together to achieve a sustainable future.", + "text": "16.14 Future Directions and Challenges\nAs we look towards the future, the role of AI in environmental sustainability is poised to grow even more significant. AI’s potential to drive advancements in renewable energy, climate modeling, conservation efforts, and more is immense. However, it is a two-sided coin, as we need to overcome several challenges and direct our efforts towards sustainable and responsible AI development.\n\n16.14.1 Future Directions\nOne key future direction is the development of more energy-efficient AI models and algorithms. This involves ongoing research and innovation in areas like model pruning, quantization, and the use of low-precision numerics, as well as developing the hardware to enable full profitability of these innovations. Even further, we look at alternative computing paradigms that do not rely on von-Neumann architectures. More on this topic can be found in the hardware acceleration chapter. The goal is to create AI systems that deliver high performance while minimizing energy consumption and carbon emissions.\nAnother important direction is the integration of renewable energy sources into AI infrastructure. As data centers continue to be major contributors to AI’s carbon footprint, transitioning to renewable energy sources like solar and wind is crucial. Developments in long-term, sustainable energy storage, such as Ambri, an MIT spinoff, could enable this transition. This requires significant investment and collaboration between tech companies, energy providers, and policymakers.\n\n\n16.14.2 Challenges\nDespite these promising directions, several challenges need to be addressed. One of the major challenges is the need for consistent standards and methodologies for measuring and reporting the environmental impact of AI. These methods must capture the complexity of the life cycles of AI models and system hardware. Next, efficient and environmentally sustainable AI infrastructure and system hardware are needed. This consists of three components. It maximizes the utilization of accelerator and system resources, prolong the lifetime of AI infrastructure, and design systems hardware with environmental impact in mind.\nOn the software side, we should trade off experimentation and the subsequent training cost. Techniques such as neural architecture search and hyperparameter optimization can be used for design space exploration. However, these are often very resource-intensive. Efficient experimentation can significantly reduce the environmental footprint overhead. Next, methods to reduce wasted training efforts should be explored.\nTo improve model quality, we often scale the dataset. However, the increased system resources required for data storage and ingestion caused by this scaling have a significant environmental impact (Wu et al. 2022). A thorough understanding of the rate at which data loses its predictive value and devising data sampling strategies is important.\n\nWu, Carole-Jean, Ramya Raghavendra, Udit Gupta, Bilge Acun, Newsha Ardalani, Kiwan Maeng, Gloria Chang, et al. 2022. “Sustainable Ai: Environmental Implications, Challenges and Opportunities.” Proceedings of Machine Learning and Systems 4: 795–813.\nData gaps also pose a significant challenge. Without companies and governments openly sharing detailed and accurate data on energy consumption, carbon emissions, and other environmental impacts, it isn’t easy to develop effective strategies for sustainable AI.\nFinally, the fast pace of AI development requires an agile approach to the policy imposed on these systems. The policy should ensure sustainable development without constraining innovation. This requires experts in all domains of AI, environmental sciences, energy, and policy to work together to achieve a sustainable future.", "crumbs": [ "Advanced Topics", "16  Sustainable AI" @@ -1929,7 +1929,7 @@ "href": "contents/robust_ai/robust_ai.html#ml-model-robustness", "title": "17  Robust AI", "section": "17.4 ML Model Robustness", - "text": "17.4 ML Model Robustness\n\n17.4.1 Adversarial Attacks\n\nDefinition and Characteristics\nAdversarial attacks aim to trick models into making incorrect predictions by providing them with specially crafted, deceptive inputs (called adversarial examples) (Parrish et al. 2023). By adding slight perturbations to input data, adversaries can “hack” a model’s pattern recognition and deceive it. These are sophisticated techniques where slight, often imperceptible alterations to input data can trick an ML model into making a wrong prediction, as shown in Figure 17.18.\n\nParrish, Alicia, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Max Bartolo, Oana Inel, Juan Ciro, et al. 2023. “Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models.” ArXiv Preprint abs/2305.14384. https://arxiv.org/abs/2305.14384.\n\n\n\n\n\n\nFigure 17.18: A small adversarial noise added to the original image can make the neural network classify the image as a Guacamole instead of an Egyptian cat. Source: Sutanto\n\n\n\nOne can generate prompts that lead to unsafe images in text-to-image models like DALLE (Ramesh et al. 2021) or Stable Diffusion (Rombach et al. 2022). For example, by altering the pixel values of an image, attackers can deceive a facial recognition system into identifying a face as a different person.\n\nRamesh, Aditya, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. “Zero-Shot Text-to-Image Generation.” In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, edited by Marina Meila and Tong Zhang, 139:8821–31. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v139/ramesh21a.html.\n\nRombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer. 2022. “High-Resolution Image Synthesis with Latent Diffusion Models.” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. https://doi.org/10.1109/cvpr52688.2022.01042.\nAdversarial attacks exploit the way ML models learn and make decisions during inference. These models work on the principle of recognizing patterns in data. An adversary crafts special inputs with perturbations to mislead the model’s pattern recognition---essentially ‘hacking’ the model’s perceptions.\nAdversarial attacks fall under different scenarios:\n\nWhitebox Attacks: The attacker fully knows the target model’s internal workings, including the training data, parameters, and architecture (Ye and Hamidi 2021). This comprehensive access creates favorable conditions for attackers to exploit the model’s vulnerabilities. The attacker can use specific and subtle weaknesses to craft effective adversarial examples.\nBlackbox Attacks: In contrast to white-box attacks, black-box attacks involve the attacker having little to no knowledge of the target model (Guo et al. 2019). To carry out the attack, the adversarial actor must carefully observe the model’s output behavior.\nGreybox Attacks: These fall between blackbox and whitebox attacks. The attacker has only partial knowledge about the target model’s internal design (Xu et al. 2021). For example, the attacker could have knowledge about training data but not the architecture or parameters. In the real world, practical attacks fall under black black-box box grey-boxes.\n\n\nYe, Linfeng, and Shayan Mohajer Hamidi. 2021. “Thundernna: A White Box Adversarial Attack.” arXiv Preprint arXiv:2111.12305.\n\nGuo, Chuan, Jacob Gardner, Yurong You, Andrew Gordon Wilson, and Kilian Weinberger. 2019. “Simple Black-Box Adversarial Attacks.” In International Conference on Machine Learning, 2484–93. PMLR.\n\nXu, Ying, Xu Zhong, Antonio Jimeno Yepes, and Jey Han Lau. 2021. “Grey-Box Adversarial Attack and Defence for Sentiment Classification.” arXiv Preprint arXiv:2103.11576.\nThe landscape of machine learning models is complex and broad, especially given their relatively recent integration into commercial applications. This rapid adoption, while transformative, has brought to light numerous vulnerabilities within these models. Consequently, various adversarial attack methods have emerged, each strategically exploiting different aspects of different models. Below, we highlight a subset of these methods, showcasing the multifaceted nature of adversarial attacks on machine learning models:\n\nGenerative Adversarial Networks (GANs) are deep learning models that consist of two networks competing against each other: a generator and a discriminator (Goodfellow et al. 2020). The generator tries to synthesize realistic data while the discriminator evaluates whether they are real or fake. GANs can be used to craft adversarial examples. The generator network is trained to produce inputs that the target model misclassifies. These GAN-generated images can then attack a target classifier or detection model. The generator and the target model are engaged in a competitive process, with the generator continually improving its ability to create deceptive examples and the target model enhancing its resistance to such examples. GANs provide a powerful framework for crafting complex and diverse adversarial inputs, illustrating the adaptability of generative models in the adversarial landscape.\nTransfer Learning Adversarial Attacks exploit the knowledge transferred from a pre-trained model to a target model, creating adversarial examples that can deceive both models. These attacks pose a growing concern, particularly when adversaries have knowledge of the feature extractor but lack access to the classification head (the part or layer responsible for making the final classifications). Referred to as “headless attacks,” these transferable adversarial strategies leverage the expressive capabilities of feature extractors to craft perturbations while being oblivious to the label space or training data. The existence of such attacks underscores the importance of developing robust defenses for transfer learning applications, especially since pre-trained models are commonly used (Abdelkader et al. 2020).\n\n\nGoodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. “Generative Adversarial Networks.” Commun. ACM 63 (11): 139–44. https://doi.org/10.1145/3422622.\n\nAbdelkader, Ahmed, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, and Chen Zhu. 2020. “Headless Horseman: Adversarial Attacks on Transfer Learning Models.” In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3087–91. IEEE. https://doi.org/10.1109/icassp40776.2020.9053181.\n\n\nMechanisms of Adversarial Attacks\n\n\n\n\n\n\nFigure 17.19: Gradient-Based Attacks. Source: Ivezic\n\n\n\nGradient-based Attacks\nOne prominent category of adversarial attacks is gradient-based attacks. These attacks leverage the gradients of the ML model’s loss function to craft adversarial examples. The Fast Gradient Sign Method (FGSM) is a well-known technique in this category. FGSM perturbs the input data by adding small noise in the gradient direction, aiming to maximize the model’s prediction error. FGSM can quickly generate adversarial examples, as shown in Figure 17.19, by taking a single step in the gradient direction.\nAnother variant, the Projected Gradient Descent (PGD) attack, extends FGSM by iteratively applying the gradient update step, allowing for more refined and powerful adversarial examples. The Jacobian-based Saliency Map Attack (JSMA) is another gradient-based approach that identifies the most influential input features and perturbs them to create adversarial examples.\nOptimization-based Attacks\nThese attacks formulate the generation of adversarial examples as an optimization problem. The Carlini and Wagner (C&W) attack is a prominent example in this category. It aims to find the smallest perturbation that can cause misclassification while maintaining the perceptual similarity to the original input. The C&W attack employs an iterative optimization process to minimize the perturbation while maximizing the model’s prediction error.\nAnother optimization-based approach is the Elastic Net Attack to DNNs (EAD), which incorporates elastic net regularization to generate adversarial examples with sparse perturbations.\nTransfer-based Attacks\nTransfer-based attacks exploit the transferability property of adversarial examples. Transferability refers to the phenomenon where adversarial examples crafted for one ML model can often fool other models, even if they have different architectures or were trained on different datasets. This enables attackers to generate adversarial examples using a surrogate model and then transfer them to the target model without requiring direct access to its parameters or gradients. Transfer-based attacks highlight the generalization of adversarial vulnerabilities across different models and the potential for black-box attacks.\nPhysical-world Attacks\nPhysical-world attacks bring adversarial examples into the realm of real-world scenarios. These attacks involve creating physical objects or manipulations that can deceive ML models when captured by sensors or cameras. Adversarial patches, for example, are small, carefully designed patches that can be placed on objects to fool object detection or classification models. When attached to real-world objects, these patches can cause models to misclassify or fail to detect the objects accurately. Adversarial objects, such as 3D-printed sculptures or modified road signs, can also be crafted to deceive ML systems in physical environments.\nSummary\nTable 17.2 a concise overview of the different categories of adversarial attacks, including gradient-based attacks (FGSM, PGD, JSMA), optimization-based attacks (C&W, EAD), transfer-based attacks, and physical-world attacks (adversarial patches and objects). Each attack is briefly described, highlighting its key characteristics and mechanisms.\n\n\n\nTable 17.2: Different attack types on ML models.\n\n\n\n\n\n\n\n\n\n\nAttack Category\nAttack Name\nDescription\n\n\n\n\nGradient-based\nFast Gradient Sign Method (FGSM) Projected Gradient Descent (PGD) Jacobian-based Saliency Map Attack (JSMA)\nPerturbs input data by adding small noise in the gradient direction to maximize prediction error. Extends FGSM by iteratively applying the gradient update step for more refined adversarial examples. Identifies influential input features and perturbs them to create adversarial examples.\n\n\nOptimization-based\nCarlini and Wagner (C&W) Attack Elastic Net Attack to DNNs (EAD)\nFinds the smallest perturbation that causes misclassification while maintaining perceptual similarity. Incorporates elastic net regularization to generate adversarial examples with sparse perturbations.\n\n\nTransfer-based\nTransferability-based Attacks\nExploits the transferability of adversarial examples across different models, enabling black-box attacks.\n\n\nPhysical-world\nAdversarial Patches Adversarial Objects\nSmall, carefully designed patches placed on objects to fool object detection or classification models. Physical objects (e.g., 3D-printed sculptures, modified road signs) crafted to deceive ML systems in real-world scenarios.\n\n\n\n\n\n\nThe mechanisms of adversarial attacks reveal the intricate interplay between the ML model’s decision boundaries, the input data, and the attacker’s objectives. By carefully manipulating the input data, attackers can exploit the model’s sensitivities and blind spots, leading to incorrect predictions. The success of adversarial attacks highlights the need for a deeper understanding of ML models’ robustness and generalization properties.\nDefending against adversarial attacks requires a multifaceted approach. Adversarial training is one common defense strategy in which models are trained on adversarial examples to improve robustness. Exposing the model to adversarial examples during training teaches it to classify them correctly and become more resilient to attacks. Defensive distillation, input preprocessing, and ensemble methods are other techniques that can help mitigate the impact of adversarial attacks.\nAs adversarial machine learning evolves, researchers explore new attack mechanisms and develop more sophisticated defenses. The arms race between attackers and defenders drives the need for constant innovation and vigilance in securing ML systems against adversarial threats. Understanding the mechanisms of adversarial attacks is crucial for developing robust and reliable ML models that can withstand the ever-evolving landscape of adversarial examples.\n\n\nImpact on ML Systems\nAdversarial attacks on machine learning systems have emerged as a significant concern in recent years, highlighting the potential vulnerabilities and risks associated with the widespread adoption of ML technologies. These attacks involve carefully crafted perturbations to input data that can deceive or mislead ML models, leading to incorrect predictions or misclassifications, as shown in Figure 17.20. The impact of adversarial attacks on ML systems is far-reaching and can have serious consequences in various domains.\nOne striking example of the impact of adversarial attacks was demonstrated by researchers in 2017. They experimented with small black and white stickers on stop signs (Eykholt et al. 2017). To the human eye, these stickers did not obscure the sign or prevent its interpretability. However, when images of the sticker-modified stop signs were fed into standard traffic sign classification ML models, a shocking result emerged. The models misclassified the stop signs as speed limit signs over 85% of the time.\n\nEykholt, Kevin, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. 2017. “Robust Physical-World Attacks on Deep Learning Models.” ArXiv Preprint abs/1707.08945. https://arxiv.org/abs/1707.08945.\nThis demonstration shed light on the alarming potential of simple adversarial stickers to trick ML systems into misreading critical road signs. The implications of such attacks in the real world are significant, particularly in the context of autonomous vehicles. If deployed on actual roads, these adversarial stickers could cause self-driving cars to misinterpret stop signs as speed limits, leading to dangerous situations, as shown in Figure 17.21. Researchers warned that this could result in rolling stops or unintended acceleration into intersections, endangering public safety.\n\n\n\n\n\n\nFigure 17.20: Adversarial example generation applied to GoogLeNet (Szegedy et al., 2014a) on ImageNet. Source: Goodfellow\n\n\n\n\n\n\n\n\n\nFigure 17.21: Graffiti on a stop sign tricked a self-driving car into thinking it was a 45 mph speed limit sign. Source: Eykholt\n\n\n\nThe case study of the adversarial stickers on stop signs provides a concrete illustration of how adversarial examples exploit how ML models recognize patterns. By subtly manipulating the input data in ways that are invisible to humans, attackers can induce incorrect predictions and create serious risks, especially in safety-critical applications like autonomous vehicles. The attack’s simplicity highlights the vulnerability of ML models to even minor changes in the input, emphasizing the need for robust defenses against such threats.\nThe impact of adversarial attacks extends beyond the degradation of model performance. These attacks raise significant security and safety concerns, particularly in domains where ML models are relied upon for critical decision-making. In healthcare applications, adversarial attacks on medical imaging models could lead to misdiagnosis or incorrect treatment recommendations, jeopardizing patient well-being (M.-J. Tsai, Lin, and Lee 2023). In financial systems, adversarial attacks could enable fraud or manipulation of trading algorithms, resulting in substantial economic losses.\n\nTsai, Min-Jen, Ping-Yi Lin, and Ming-En Lee. 2023. “Adversarial Attacks on Medical Image Classification.” Cancers 15 (17): 4228. https://doi.org/10.3390/cancers15174228.\n\nFursov, Ivan, Matvey Morozov, Nina Kaploukhaya, Elizaveta Kovtun, Rodrigo Rivera-Castro, Gleb Gusev, Dmitry Babaev, Ivan Kireev, Alexey Zaytsev, and Evgeny Burnaev. 2021. “Adversarial Attacks on Deep Models for Financial Transaction Records.” In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining, 2868–78. ACM. https://doi.org/10.1145/3447548.3467145.\nMoreover, adversarial vulnerabilities undermine the trustworthiness and interpretability of ML models. If carefully crafted perturbations can easily fool models, confidence in their predictions and decisions erodes. Adversarial examples expose the models’ reliance on superficial patterns and the inability to capture the true underlying concepts, challenging the reliability of ML systems (Fursov et al. 2021).\nDefending against adversarial attacks often requires additional computational resources and can impact the overall system performance. Techniques like adversarial training, where models are trained on adversarial examples to improve robustness, can significantly increase training time and computational requirements (Bai et al. 2021). Runtime detection and mitigation mechanisms, such as input preprocessing (Addepalli et al. 2020) or prediction consistency checks, introduce latency and affect the real-time performance of ML systems.\n\nBai, Tao, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang. 2021. “Recent Advances in Adversarial Training for Adversarial Robustness.” arXiv Preprint arXiv:2102.01356.\n\nAddepalli, Sravanti, B. S. Vivek, Arya Baburaj, Gaurang Sriramanan, and R. Venkatesh Babu. 2020. “Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes.” In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1020–29. IEEE. https://doi.org/10.1109/cvpr42600.2020.00110.\nThe presence of adversarial vulnerabilities also complicates the deployment and maintenance of ML systems. System designers and operators must consider the potential for adversarial attacks and incorporate appropriate defenses and monitoring mechanisms. Regular updates and retraining of models become necessary to adapt to new adversarial techniques and maintain system security and performance over time.\nThe impact of adversarial attacks on ML systems is significant and multifaceted. These attacks expose ML models’ vulnerabilities, from degrading model performance and raising security and safety concerns to challenging model trustworthiness and interpretability. Developers and researchers must prioritize the development of robust defenses and countermeasures to mitigate the risks posed by adversarial attacks. By addressing these challenges, we can build more secure, reliable, and trustworthy ML systems that can withstand the ever-evolving landscape of adversarial threats.\n\n\n\n\n\n\nExercise 17.2: Adversarial Attacks\n\n\n\n\n\nGet ready to become an AI adversary! In this Colab, you’ll become a white-box hacker, learning to craft attacks that deceive image classification models. We’ll focus on the Fast Gradient Sign Method (FGSM), where you’ll weaponize a model’s gradients against it! You’ll deliberately distort images with tiny perturbations, observing how they increasingly fool the AI more intensely. This hands-on exercise highlights the importance of building secure AI – a critical skill as AI integrates into cars and healthcare. The Colab directly ties into the Robust AI chapter of your book, moving adversarial attacks from theory into your own hands-on experience.\n\nThink you can outsmart an AI? In this Colab, learn how to trick image classification models with adversarial attacks. We’ll use methods like FGSM to change images and subtly fool the AI. Discover how to design deceptive image patches and witness the surprising vulnerability of these powerful models. This is crucial knowledge for building truly robust AI systems!\n\n\n\n\n\n\n\n17.4.2 Data Poisoning\n\nDefinition and Characteristics\nData poisoning is an attack where the training data is tampered with, leading to a compromised model (Biggio, Nelson, and Laskov 2012), as shown in Figure 17.22. Attackers can modify existing training examples, insert new malicious data points, or influence the data collection process. The poisoned data is labeled in such a way as to skew the model’s learned behavior. This can be particularly damaging in applications where ML models make automated decisions based on learned patterns. Beyond training sets, poisoning tests, and validation data can allow adversaries to boost reported model performance artificially.\n\nBiggio, Battista, Blaine Nelson, and Pavel Laskov. 2012. “Poisoning Attacks Against Support Vector Machines.” In Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012. icml.cc / Omnipress. http://icml.cc/2012/papers/880.pdf.\n\n\n\n\n\n\nFigure 17.22: NightShade’s poisoning effects on Stable Diffusion. Source: TOMÉ\n\n\n\nThe process usually involves the following steps:\n\nInjection: The attacker adds incorrect or misleading examples into the training set. These examples are often designed to look normal to cursory inspection but have been carefully crafted to disrupt the learning process.\nTraining: The ML model trains on this manipulated dataset and develops skewed understandings of the data patterns.\nDeployment: Once the model is deployed, the corrupted training leads to flawed decision-making or predictable vulnerabilities the attacker can exploit.\n\nThe impact of data poisoning extends beyond classification errors or accuracy drops. In critical applications like healthcare, such alterations can lead to significant trust and safety issues (Marulli, Marrone, and Verde 2022). Later, we will discuss a few case studies of these issues.\n\nMarulli, Fiammetta, Stefano Marrone, and Laura Verde. 2022. “Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain.” Journal of Sensor and Actuator Networks 11 (2): 21. https://doi.org/10.3390/jsan11020021.\n\nOprea, Alina, Anoop Singhal, and Apostol Vassilev. 2022. “Poisoning Attacks Against Machine Learning: Can Machine Learning Be Trustworthy?” Computer 55 (11): 94–99. https://doi.org/10.1109/mc.2022.3190787.\nThere are six main categories of data poisoning (Oprea, Singhal, and Vassilev 2022):\n\nAvailability Attacks: These attacks aim to compromise the overall functionality of a model. They cause it to misclassify most testing samples, rendering the model unusable for practical applications. An example is label flipping, where labels of a specific, targeted class are replaced with labels from a different one.\nTargeted Attacks: In contrast to availability attacks, targeted attacks aim to compromise a small number of the testing samples. So, the effect is localized to a limited number of classes, while the model maintains the same original level of accuracy for the majority of the classes. The targeted nature of the attack requires the attacker to possess knowledge of the model’s classes, making detecting these attacks more challenging.\nBackdoor Attacks: In these attacks, an adversary targets specific patterns in the data. The attacker introduces a backdoor (a malicious, hidden trigger or pattern) into the training data, such as manipulating certain features in structured data or manipulating a pattern of pixels at a fixed position. This causes the model to associate the malicious pattern with specific labels. As a result, when the model encounters test samples that contain a malicious pattern, it makes false predictions.\nSubpopulation Attacks: Attackers selectively choose to compromise a subset of the testing samples while maintaining accuracy on the rest of the samples. You can think of these attacks as a combination of availability and targeted attacks: performing availability attacks (performance degradation) within the scope of a targeted subset. Although subpopulation attacks may seem very similar to targeted attacks, the two have clear differences:\nScope: While targeted attacks target a selected set of samples, subpopulation attacks target a general subpopulation with similar feature representations. For example, in a targeted attack, an actor inserts manipulated images of a ‘speed bump’ warning sign (with carefully crafted perturbations or patterns), which causes an autonomous car to fail to recognize such a sign and slow down. On the other hand, manipulating all samples of people with a British accent so that a speech recognition model would misclassify a British person’s speech is an example of a subpopulation attack.\nKnowledge: While targeted attacks require a high degree of familiarity with the data, subpopulation attacks require less intimate knowledge to be effective.\n\nThe characteristics of data poisoning include:\nSubtle and hard-to-detect manipulations of training data: Data poisoning often involves subtle manipulations of the training data that are carefully crafted to be difficult to detect through casual inspection. Attackers employ sophisticated techniques to ensure that the poisoned samples blend seamlessly with the legitimate data, making them easier to identify with thorough analysis. These manipulations can target specific features or attributes of the data, such as altering numerical values, modifying categorical labels, or introducing carefully designed patterns. The goal is to influence the model’s learning process while evading detection, allowing the poisoned data to subtly corrupt the model’s behavior.\nCan be performed by insiders or external attackers: Data poisoning attacks can be carried out by various actors, including malicious insiders with access to the training data and external attackers who find ways to influence the data collection or preprocessing pipeline. Insiders pose a significant threat because they often have privileged access and knowledge of the system, enabling them to introduce poisoned data without raising suspicions. On the other hand, external attackers may exploit vulnerabilities in data sourcing, crowdsourcing platforms, or data aggregation processes to inject poisoned samples into the training dataset. This highlights the importance of implementing strong access controls, data governance policies, and monitoring mechanisms to mitigate the risk of insider threats and external attacks.\nExploits vulnerabilities in data collection and preprocessing: Data poisoning attacks often exploit vulnerabilities in the machine learning pipeline’s data collection and preprocessing stages. Attackers carefully design poisoned samples to evade common data validation techniques, ensuring that the manipulated data still falls within acceptable ranges, follows expected distributions, or maintains consistency with other features. This allows the poisoned data to pass through data preprocessing steps without detection. Furthermore, poisoning attacks can take advantage of weaknesses in data preprocessing, such as inadequate data cleaning, insufficient outlier detection, or lack of integrity checks. Attackers may also exploit the lack of robust data provenance and lineage tracking mechanisms to introduce poisoned data without leaving a traceable trail. Addressing these vulnerabilities requires rigorous data validation, anomaly detection, and data provenance tracking techniques to ensure the integrity and trustworthiness of the training data.\nDisrupts the learning process and skews model behavior: Data poisoning attacks are designed to disrupt the learning process of machine learning models and skew their behavior towards the attacker’s objectives. The poisoned data is typically manipulated with specific goals, such as skewing the model’s behavior towards certain classes, introducing backdoors, or degrading overall performance. These manipulations are not random but targeted to achieve the attacker’s desired outcomes. By introducing label inconsistencies, where the manipulated samples have labels that do not align with their true nature, poisoning attacks can confuse the model during training and lead to biased or incorrect predictions. The disruption caused by poisoned data can have far-reaching consequences, as the compromised model may make flawed decisions or exhibit unintended behavior when deployed in real-world applications.\nImpacts model performance, fairness, and trustworthiness: Poisoned data in the training dataset can have severe implications for machine learning models’ performance, fairness, and trustworthiness. Poisoned data can degrade the accuracy and performance of the trained model, leading to increased misclassifications or errors in predictions. This can have significant consequences, especially in critical applications where the model’s outputs inform important decisions. Moreover, poisoning attacks can introduce biases and fairness issues, causing the model to make discriminatory or unfair decisions for certain subgroups or classes. This undermines machine learning systems’ ethical and social responsibilities and can perpetuate or amplify existing biases. Furthermore, poisoned data erodes the trustworthiness and reliability of the entire ML system. The model’s outputs become questionable and potentially harmful, leading to a loss of confidence in the system’s integrity. The impact of poisoned data can propagate throughout the entire ML pipeline, affecting downstream components and decisions that rely on the compromised model. Addressing these concerns requires robust data governance, regular model auditing, and ongoing monitoring to detect and mitigate the effects of data poisoning attacks.\n\n\nMechanisms of Data Poisoning\nData poisoning attacks can be carried out through various mechanisms, exploiting different ML pipeline vulnerabilities. These mechanisms allow attackers to manipulate the training data and introduce malicious samples that can compromise the model’s performance, fairness, or integrity. Understanding these mechanisms is crucial for developing effective defenses against data poisoning and ensuring the robustness of ML systems. Data poisoning mechanisms can be broadly categorized based on the attacker’s approach and the stage of the ML pipeline they target. Some common mechanisms include modifying training data labels, altering feature values, injecting carefully crafted malicious samples, exploiting data collection and preprocessing vulnerabilities, manipulating data at the source, poisoning data in online learning scenarios, and collaborating with insiders to manipulate data.\nEach of these mechanisms presents unique challenges and requires different mitigation strategies. For example, detecting label manipulation may involve analyzing the distribution of labels and identifying anomalies (Zhou et al. 2018), while preventing feature manipulation may require secure data preprocessing and anomaly detection techniques (Carta et al. 2020). Defending against insider threats may involve strict access control policies and monitoring of data access patterns. Moreover, the effectiveness of data poisoning attacks often depends on the attacker’s knowledge of the ML system, including the model architecture, training algorithms, and data distribution. Attackers may use adversarial machine learning or data synthesis techniques to craft samples that are more likely to bypass detection and achieve their malicious objectives.\n\nZhou, Peng, Xintong Han, Vlad I. Morariu, and Larry S. Davis. 2018. “Learning Rich Features for Image Manipulation Detection.” In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1053–61. IEEE. https://doi.org/10.1109/cvpr.2018.00116.\n\nCarta, Salvatore, Alessandro Sebastian Podda, Diego Reforgiato Recupero, and Roberto Saia. 2020. “A Local Feature Engineering Strategy to Improve Network Anomaly Detection.” Future Internet 12 (10): 177. https://doi.org/10.3390/fi12100177.\n\n\n\n\n\n\nFigure 17.23: Garbage In – Garbage Out. Source: Information Matters\n\n\n\nModifying training data labels: One of the most straightforward mechanisms of data poisoning is modifying the training data labels. In this approach, the attacker selectively changes the labels of a subset of the training samples to mislead the model’s learning process as shown in Figure 17.23. For example, in a binary classification task, the attacker might flip the labels of some positive samples to negative, or vice versa. By introducing such label noise, the attacker aims to degrade the model’s performance or cause it to make incorrect predictions for specific target instances.\nAltering feature values in training data: Another mechanism of data poisoning involves altering the feature values of the training samples without modifying the labels. The attacker carefully crafts the feature values to introduce specific biases or vulnerabilities into the model. For instance, in an image classification task, the attacker might add imperceptible perturbations to a subset of images, causing the model to learn a particular pattern or association. This type of poisoning can create backdoors or trojans in the trained model, which specific input patterns can trigger.\nInjecting carefully crafted malicious samples: In this mechanism, the attacker creates malicious samples designed to poison the model. These samples are crafted to have a specific impact on the model’s behavior while blending in with the legitimate training data. The attacker might use techniques such as adversarial perturbations or data synthesis to generate poisoned samples that are difficult to detect. The attacker aims to manipulate the model’s decision boundaries by injecting these malicious samples into the training data or introducing targeted misclassifications.\nExploiting data collection and preprocessing vulnerabilities: Data poisoning attacks can also exploit the data collection and preprocessing pipeline vulnerabilities. If the data collection process is not secure or there are weaknesses in the data preprocessing steps, an attacker can manipulate the data before it reaches the training phase. For example, if data is collected from untrusted sources or issues in data cleaning or aggregation, an attacker can introduce poisoned samples or manipulate the data to their advantage.\nManipulating data at the source (e.g., sensor data): In some cases, attackers can manipulate the data at its source, such as sensor data or input devices. By tampering with the sensors or manipulating the environment in which data is collected, attackers can introduce poisoned samples or bias the data distribution. For instance, in a self-driving car scenario, an attacker might manipulate the sensors or the environment to feed misleading information into the training data, compromising the model’s ability to make safe and reliable decisions.\n\n\n\n\n\n\nFigure 17.24: Data Poisoning Attack. Source: Sikandar\n\n\n\nPoisoning data in online learning scenarios: Data poisoning attacks can also target ML systems that employ online learning, where the model is continuously updated with new data in real time. In such scenarios, an attacker can gradually inject poisoned samples over time, slowly manipulating the model’s behavior. Online learning systems are particularly vulnerable to data poisoning because they adapt to new data without extensive validation, making it easier for attackers to introduce malicious samples, as shown in Figure 17.24.\nCollaborating with insiders to manipulate data: Sometimes, data poisoning attacks can involve collaboration with insiders with access to the training data. Malicious insiders, such as employees or data providers, can manipulate the data before it is used to train the model. Insider threats are particularly challenging to detect and prevent, as the attackers have legitimate access to the data and can carefully craft the poisoning strategy to evade detection.\nThese are the key mechanisms of data poisoning in ML systems. Attackers often employ these mechanisms to make their attacks more effective and harder to detect. The risk of data poisoning attacks grows as ML systems become increasingly complex and rely on larger datasets from diverse sources. Defending against data poisoning requires a multifaceted approach. ML practitioners and system designers must be aware of the various mechanisms of data poisoning and adopt a comprehensive approach to data security and model resilience. This includes secure data collection, robust data validation, and continuous model performance monitoring. Implementing secure data collection and preprocessing practices is crucial to prevent data poisoning at the source. Data validation and anomaly detection techniques can also help identify and mitigate potential poisoning attempts. Monitoring model performance for signs of data poisoning is also essential to detect and respond to attacks promptly.\n\n\nImpact on ML Systems\nData poisoning attacks can severely affect ML systems, compromising their performance, reliability, and trustworthiness. The impact of data poisoning can manifest in various ways, depending on the attacker’s objectives and the specific mechanism used. Let’s explore each of the potential impacts in detail.\nDegradation of model performance: One of the primary impacts of data poisoning is the degradation of the model’s overall performance. By manipulating the training data, attackers can introduce noise, biases, or inconsistencies that hinder the model’s ability to learn accurate patterns and make reliable predictions. This can reduce accuracy, precision, recall, or other performance metrics. The degradation of model performance can have significant consequences, especially in critical applications such as healthcare, finance, or security, where the reliability of predictions is crucial.\nMisclassification of specific targets: Data poisoning attacks can also be designed to cause the model to misclassify specific target instances. Attackers may introduce carefully crafted poisoned samples similar to the target instances, leading the model to learn incorrect associations. This can result in the model consistently misclassifying the targeted instances, even if it performs well on other inputs. Such targeted misclassification can have severe consequences, such as causing a malware detection system to overlook specific malicious files or leading to the wrong diagnosis in a medical imaging application.\nBackdoors and trojans in trained models: Data poisoning can introduce backdoors or trojans into the trained model. Backdoors are hidden functionalities that allow attackers to trigger specific behaviors or bypass normal authentication mechanisms. On the other hand, Trojans are malicious components embedded within the model that can activate specific input patterns. By poisoning the training data, attackers can create models that appear to perform normally but contain hidden vulnerabilities that can be exploited later. Backdoors and trojans can compromise the integrity and security of the ML system, allowing attackers to gain unauthorized access, manipulate predictions, or exfiltrate sensitive information.\nBiased or unfair model outcomes: Data poisoning attacks can introduce biases or unfairness into the model’s predictions. By manipulating the training data distribution or injecting samples with specific biases, attackers can cause the model to learn and perpetuate discriminatory patterns. This can lead to unfair treatment of certain groups or individuals based on sensitive attributes such as race, gender, or age. Biased models can have severe societal implications, reinforcing existing inequalities and discriminatory practices. Ensuring fairness and mitigating biases is crucial for building trustworthy and ethical ML systems.\nIncreased false positives or false negatives: Data poisoning can also impact the model’s ability to correctly identify positive or negative instances, leading to increased false positives or false negatives. False positives occur when the model incorrectly identifies a negative instance as positive, while false negatives happen when a positive instance is misclassified as negative. The consequences of increased false positives or false negatives can be significant depending on the application. For example, in a fraud detection system, high false positives can lead to unnecessary investigations and customer frustration, while high false negatives can allow fraudulent activities to go undetected.\nCompromised system reliability and trustworthiness: Data poisoning attacks can undermine ML systems’ overall reliability and trustworthiness. When models are trained on poisoned data, their predictions become reliable and trustworthy. This can erode user confidence in the system and lead to a loss of trust in the decisions made by the model. In critical applications where ML systems are relied upon for decision-making, such as autonomous vehicles or medical diagnosis, compromised reliability can have severe consequences, putting lives and property at risk.\nAddressing the impact of data poisoning requires a proactive approach to data security, model testing, and monitoring. Organizations must implement robust measures to ensure the integrity and quality of training data, employ techniques to detect and mitigate poisoning attempts, and continuously monitor the performance and behavior of deployed models. Collaboration between ML practitioners, security experts, and domain specialists is essential to develop comprehensive strategies for preventing and responding to data poisoning attacks.\n\nCase Study 1\nIn 2017, researchers demonstrated a data poisoning attack against a popular toxicity classification model called Perspective (Hosseini et al. 2017). This ML model detects toxic comments online.\n\nHosseini, Hossein, Sreeram Kannan, Baosen Zhang, and Radha Poovendran. 2017. “Deceiving Google’s Perspective Api Built for Detecting Toxic Comments.” ArXiv Preprint abs/1702.08138. https://arxiv.org/abs/1702.08138.\nThe researchers added synthetically generated toxic comments with slight misspellings and grammatical errors to the model’s training data. This slowly corrupted the model, causing it to misclassify increasing numbers of severely toxic inputs as non-toxic over time.\nAfter retraining on the poisoned data, the model’s false negative rate increased from 1.4% to 27% - allowing extremely toxic comments to bypass detection. The researchers warned this stealthy data poisoning could enable the spread of hate speech, harassment, and abuse if deployed against real moderation systems.\nThis case highlights how data poisoning can degrade model accuracy and reliability. For social media platforms, a poisoning attack that impairs toxicity detection could lead to the proliferation of harmful content and distrust of ML moderation systems. The example demonstrates why securing training data integrity and monitoring for poisoning is critical across application domains.\n\n\nCase Study 2\n\n\n\n\n\n\nFigure 17.25: Samples of dirty-label poison data regarding mismatched text/image pairs. Source: Shan\n\n\n\nInterestingly enough, data poisoning attacks are not always malicious (Shan et al. 2023). Nightshade, a tool developed by a team led by Professor Ben Zhao at the University of Chicago, utilizes data poisoning to help artists protect their art against scraping and copyright violations by generative AI models. Artists can use the tool to make subtle modifications to their images before uploading them online, as shown in Figure 17.25.\nWhile these changes are indiscernible to the human eye, they can significantly disrupt the performance of generative AI models when incorporated into the training data. Generative models can be manipulated to generate hallucinations and weird images. For example, with only 300 poisoned images, the University of Chicago researchers could trick the latest Stable Diffusion model into generating images of dogs that look like cats or images of cows when prompted for cars.\nAs the number of poisoned images on the internet increases, the performance of the models that use scraped data will deteriorate exponentially. First, the poisoned data is hard to detect and requires manual elimination. Second, the “poison” spreads quickly to other labels because generative models rely on connections between words and concepts as they generate images. So a poisoned image of a “car” could spread into generated images associated with words like “truck,” “train,” ” bus,” etc.\nOn the other hand, this tool can be used maliciously and can affect legitimate applications of the generative models. This shows the very challenging and novel nature of machine learning attacks.\nFigure 17.26 demonstrates the effects of different levels of data poisoning (50 samples, 100 samples, and 300 samples of poisoned images) on generating images in different categories. Notice how the images start deforming and deviating from the desired category. For example, after 300 poison samples, a car prompt generates a cow.\n\n\n\n\n\n\nFigure 17.26: Data poisoning. Source: Shan et al. (2023))\n\n\nShan, Shawn, Wenxin Ding, Josephine Passananti, Haitao Zheng, and Ben Y Zhao. 2023. “Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models.” ArXiv Preprint abs/2310.13828. https://arxiv.org/abs/2310.13828.\n\n\n\n\n\n\n\n\nExercise 17.3: Poisoning Attacks\n\n\n\n\n\nGet ready to explore the dark side of AI security! In this Colab, you’ll learn about data poisoning – how bad data can trick AI models into making wrong decisions. We’ll focus on a real-world attack against a Support Vector Machine (SVM), observing how the AI’s behavior changes under attack. This hands-on exercise will highlight why protecting AI systems is crucial, especially as they become more integrated into our lives. Think like a hacker, understand the vulnerability, and brainstorm how to defend our AI systems!\n\n\n\n\n\n\n\n\n17.4.3 Distribution Shifts\n\nDefinition and Characteristics\nDistribution shift refers to the phenomenon where the data distribution encountered by an ML model during deployment (inference) differs from the distribution it was trained on, as shown in Figure 17.27. This is not so much an attack as it is that the model’s robustness will vary over time. In other words, the data’s statistical properties, patterns, or underlying assumptions can change between the training and test phases.\n\n\n\n\n\n\nFigure 17.27: The curly brackets enclose the distribution shift between the environments. Here, z stands for the spurious feature, and y stands for label class. Source: Xin\n\n\n\nThe key characteristics of distribution shift include:\nDomain mismatch: The input data during inference comes from a different domain or distribution than the training data. When the input data during inference comes from a domain or distribution different from the training data, it can significantly affect the model’s performance. This is because the model has learned patterns and relationships specific to the training domain, and when applied to a different domain, those learned patterns may not hold. For example, consider a sentiment analysis model trained on movie reviews. Suppose this model is applied to analyze sentiment in tweets. In that case, it may need help to accurately classify the sentiment because the language, grammar, and context of tweets can differ from movie reviews. This domain mismatch can result in poor performance and unreliable predictions, limiting the model’s practical utility.\nTemporal drift: The data distribution evolves, leading to a gradual or sudden shift in the input characteristics. Temporal drift is important because ML models are often deployed in dynamic environments where the data distribution can change over time. If the model is not updated or adapted to these changes, its performance can gradually degrade. For instance, the patterns and behaviors associated with fraudulent activities may evolve in a fraud detection system as fraudsters adapt their techniques. If the model is not retrained or updated to capture these new patterns, it may fail to detect new types of fraud effectively. Temporal drift can lead to a decline in the model’s accuracy and reliability over time, making monitoring and addressing this type of distribution shift crucial.\nContextual changes: The ML model’s context can vary, resulting in different data distributions based on factors such as location, user behavior, or environmental conditions. Contextual changes matter because ML models are often deployed in various contexts or environments that can have different data distributions. If the model cannot generalize well to these different contexts, its performance may improve. For example, consider a computer vision model trained to recognize objects in a controlled lab environment. When deployed in a real-world setting, factors such as lighting conditions, camera angles, or background clutter can vary significantly, leading to a distribution shift. If the model is robust to these contextual changes, it may be able to accurately recognize objects in the new environment, limiting its practical utility.\nUnrepresentative training data: The training data may only partially capture the variability and diversity of the real-world data encountered during deployment. Unrepresentative training data can lead to biased or skewed models that perform poorly on real-world data. Suppose the training data needs to capture the variability and diversity of the real-world data adequately. In that case, the model may learn patterns specific to the training set but needs to generalize better to new, unseen data. This can result in poor performance, biased predictions, and limited model applicability. For instance, if a facial recognition model is trained primarily on images of individuals from a specific demographic group, it may struggle to accurately recognize faces from other demographic groups when deployed in a real-world setting. Ensuring that the training data is representative and diverse is crucial for building models that can generalize well to real-world scenarios.\n\n\n\n\n\n\nFigure 17.28: Concept drift refers to a change in data patterns and relationships over time. Source: Evidently AI\n\n\n\nDistribution shift can manifest in various forms, such as:\nCovariate shift: The distribution of the input features (covariates) changes while the conditional distribution of the target variable given the input remains the same. Covariate shift matters because it can impact the model’s ability to make accurate predictions when the input features (covariates) differ between the training and test data. Even if the relationship between the input features and the target variable remains the same, a change in the distribution of the input features can affect the model’s performance. For example, consider a model trained to predict housing prices based on features like square footage, number of bedrooms, and location. Suppose the distribution of these features in the test data significantly differs from the training data (e.g., the test data contains houses with much larger square footage). In that case, the model’s predictions may become less accurate. Addressing covariate shifts is important to ensure the model’s robustness and reliability when applied to new data.\nConcept drift: The relationship between the input features and the target variable changes over time, altering the underlying concept the model is trying to learn, as shown in Figure 17.28. Concept drift is important because it indicates changes in the fundamental relationship between the input features and the target variable over time. When the underlying concept that the model is trying to learn shifts, its performance can deteriorate if not adapted to the new concept. For instance, in a customer churn prediction model, the factors influencing customer churn may evolve due to market conditions, competitor offerings, or customer preferences. If the model is not updated to capture these changes, its predictions may become less accurate and irrelevant. Detecting and adapting to concept drift is crucial to maintaining the model’s effectiveness and alignment with evolving real-world concepts.\nDomain generalization: The model must generalize to unseen domains or distributions not present during training. Domain generalization is important because it enables ML models to be applied to new, unseen domains without requiring extensive retraining or adaptation. In real-world scenarios, training data that covers all possible domains or distributions that the model may encounter is often infeasible. Domain generalization techniques aim to learn domain-invariant features or models that can generalize well to new domains. For example, consider a model trained to classify images of animals. If the model can learn features invariant to different backgrounds, lighting conditions, or poses, it can generalize well to classify animals in new, unseen environments. Domain generalization is crucial for building models that can be deployed in diverse and evolving real-world settings.\nThe presence of a distribution shift can significantly impact the performance and reliability of ML models, as the models may need help generalizing well to the new data distribution. Detecting and adapting to distribution shifts is crucial to ensure ML systems’ robustness and practical utility in real-world scenarios.\n\n\nMechanisms of Distribution Shifts\nThe mechanisms of distribution shift, such as changes in data sources, temporal evolution, domain-specific variations, selection bias, feedback loops, and adversarial manipulations, are important to understand because they help identify the underlying causes of distribution shift. By understanding these mechanisms, practitioners can develop targeted strategies to mitigate their impact and improve the model’s robustness. Here are some common mechanisms:\n\n\n\n\n\n\nFigure 17.29: Temporal evolution. Source: Białek\n\n\n\nChanges in data sources: Distribution shifts can occur when the data sources used for training and inference differ. For example, if a model is trained on data from one sensor but deployed on data from another sensor with different characteristics, it can lead to a distribution shift.\nTemporal evolution: Over time, the underlying data distribution can evolve due to changes in user behavior, market dynamics, or other temporal factors. For instance, in a recommendation system, user preferences may shift over time, leading to a distribution shift in the input data, as shown in Figure 17.29.\nDomain-specific variations: Different domains or contexts can have distinct data distributions. A model trained on data from one domain may only generalize well to another domain with appropriate adaptation techniques. For example, an image classification model trained on indoor scenes may struggle when applied to outdoor scenes.\nSelection bias: A Distribution shift can arise from selection bias during data collection or sampling. If the training data does not represent the true population or certain subgroups are over- or underrepresented, this can lead to a mismatch between the training and test distributions.\nFeedback loops: In some cases, the predictions or actions taken by an ML model can influence future data distribution. For example, in a dynamic pricing system, the prices set by the model can impact customer behavior, leading to a shift in the data distribution over time.\nAdversarial manipulations: Adversaries can intentionally manipulate the input data to create a distribution shift and deceive the ML model. By introducing carefully crafted perturbations or generating out-of-distribution samples, attackers can exploit the model’s vulnerabilities and cause it to make incorrect predictions.\nUnderstanding the mechanisms of distribution shift is important for developing effective strategies to detect and mitigate its impact on ML systems. By identifying the sources and characteristics of the shift, practitioners can design appropriate techniques, such as domain adaptation, transfer learning, or continual learning, to improve the model’s robustness and performance under distributional changes.\n\n\nImpact on ML Systems\nDistribution shifts can significantly negatively impact the performance and reliability of ML systems. Here are some key ways in which distribution shift can affect ML models:\nDegraded predictive performance: When the data distribution encountered during inference differs from the training distribution, the model’s predictive accuracy can deteriorate. The model may need help generalizing the new data well, leading to increased errors and suboptimal performance.\nReduced reliability and trustworthiness: Distribution shift can undermine the reliability and trustworthiness of ML models. If the model’s predictions become unreliable or inconsistent due to the shift, users may lose confidence in the system’s outputs, leading to potential misuse or disuse of the model.\nBiased predictions: Distribution shift can introduce biases in the model’s predictions. If the training data does not represent the real-world distribution or certain subgroups are underrepresented, the model may make biased predictions that discriminate against certain groups or perpetuate societal biases.\nIncreased uncertainty and risk: Distribution shift introduces additional uncertainty and risk into the ML system. The model’s behavior and performance may become less predictable, making it challenging to assess its reliability and suitability for critical applications. This uncertainty can lead to increased operational risks and potential failures.\nAdaptability challenges: ML models trained on a specific data distribution may need help to adapt to changing environments or new domains. The lack of adaptability can limit the model’s usefulness and applicability in dynamic real-world scenarios where the data distribution evolves.\nMaintenance and update difficulties: Distribution shift can complicate the maintenance and updating of ML models. As the data distribution changes, the model may require frequent retraining or fine-tuning to maintain its performance. This can be time-consuming and resource-intensive, especially if the shift occurs rapidly or continuously.\nVulnerability to adversarial attacks: Distribution shift can make ML models more vulnerable to adversarial attacks. Adversaries can exploit the model’s sensitivity to distributional changes by crafting adversarial examples outside the training distribution, causing the model to make incorrect predictions or behave unexpectedly.\nTo mitigate the impact of distribution shifts, it is crucial to develop robust ML systems that detect and adapt to distributional changes. Techniques such as domain adaptation, transfer learning, and continual learning can help improve the model’s generalization ability across different distributions. ML model monitoring, testing, and updating are also necessary to ensure their performance and reliability during distribution shifts.\n\n\n\n17.4.4 Detection and Mitigation\n\nAdversarial Attacks\nAs you may recall from above, adversarial attacks pose a significant threat to the robustness and reliability of ML systems. These attacks involve crafting carefully designed inputs, known as adversarial examples, to deceive ML models and cause them to make incorrect predictions. To safeguard ML systems against adversarial attacks, developing effective techniques for detecting and mitigating these threats is crucial.\n\nAdversarial Example Detection Techniques\nDetecting adversarial examples is the first line of defense against adversarial attacks. Several techniques have been proposed to identify and flag suspicious inputs that may be adversarial.\nStatistical methods aim to detect adversarial examples by analyzing the statistical properties of the input data. These methods often compare the input data distribution to a reference distribution, such as the training data distribution or a known benign distribution. Techniques like the Kolmogorov-Smirnov (Berger and Zhou 2014) test or the Anderson-Darling test can be used to measure the discrepancy between the distributions and flag inputs that deviate significantly from the expected distribution.\n\nBerger, Vance W, and YanYan Zhou. 2014. “Kolmogorovsmirnov Test: Overview.” Wiley Statsref: Statistics Reference Online.\nKernel density estimation (KDE) is a non-parametric technique used to estimate the probability density function of a dataset. In the context of adversarial example detection, KDE can be used to estimate the density of benign examples in the input space. Adversarial examples often lie in low-density regions and can be detected by comparing their estimated density to a threshold. Inputs with an estimated density below the threshold are flagged as potential adversarial examples.\nAnother technique is feature squeezing (Panda, Chakraborty, and Roy 2019), which reduces the complexity of the input space by applying dimensionality reduction or discretization. The idea behind feature squeezing is that adversarial examples often rely on small, imperceptible perturbations that can be eliminated or reduced through these transformations. Inconsistencies can be detected by comparing the model’s predictions on the original input and the squeezed input, indicating the presence of adversarial examples.\n\nPanda, Priyadarshini, Indranil Chakraborty, and Kaushik Roy. 2019. “Discretization Based Solutions for Secure Machine Learning Against Adversarial Attacks.” #IEEE_O_ACC# 7: 70157–68. https://doi.org/10.1109/access.2019.2919463.\nModel uncertainty estimation techniques aim to quantify the confidence or uncertainty associated with a model’s predictions. Adversarial examples often exploit regions of high uncertainty in the model’s decision boundary. By estimating the uncertainty using techniques like Bayesian neural networks, dropout-based uncertainty estimation, or ensemble methods, inputs with high uncertainty can be flagged as potential adversarial examples.\n\n\nAdversarial Defense Strategies\nOnce adversarial examples are detected, various defense strategies can be employed to mitigate their impact and improve the robustness of ML models.\nAdversarial training is a technique that involves augmenting the training data with adversarial examples and retraining the model on this augmented dataset. Exposing the model to adversarial examples during training teaches it to classify them correctly and becomes more robust to adversarial attacks. Adversarial training can be performed using various attack methods, such as the Fast Gradient Sign Method (FGSM) or Projected Gradient Descent (PGD) (Madry et al. 2017).\n\nMadry, Aleksander, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. “Towards Deep Learning Models Resistant to Adversarial Attacks.” arXiv Preprint arXiv:1706.06083.\n\nPapernot, Nicolas, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. 2016. “Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks.” In 2016 IEEE Symposium on Security and Privacy (SP), 582–97. IEEE; IEEE. https://doi.org/10.1109/sp.2016.41.\nDefensive distillation (Papernot et al. 2016) is a technique that trains a second model (the student model) to mimic the behavior of the original model (the teacher model). The student model is trained on the soft labels produced by the teacher model, which are less sensitive to small perturbations. Using the student model for inference can reduce the impact of adversarial perturbations, as the student model learns to generalize better and is less sensitive to adversarial noise.\nInput preprocessing and transformation techniques aim to remove or mitigate the effect of adversarial perturbations before feeding the input to the ML model. These techniques include image denoising, JPEG compression, random resizing, padding, or applying random transformations to the input data. By reducing the impact of adversarial perturbations, these preprocessing steps can help improve the model’s robustness to adversarial attacks.\nEnsemble methods combine multiple models to make more robust predictions. The ensemble can reduce the impact of adversarial attacks by using a diverse set of models with different architectures, training data, or hyperparameters. Adversarial examples that fool one model may not fool others in the ensemble, leading to more reliable and robust predictions. Model diversification techniques, such as using different preprocessing techniques or feature representations for each model in the ensemble, can further enhance the robustness.\n\n\nRobustness Evaluation and Testing\nConduct thorough evaluation and testing to assess the effectiveness of adversarial defense techniques and measure the robustness of ML models.\nAdversarial robustness metrics quantify the model’s resilience to adversarial attacks. These metrics can include the model’s accuracy on adversarial examples, the average distortion required to fool the model, or the model’s performance under different attack strengths. By comparing these metrics across different models or defense techniques, practitioners can assess and compare their robustness levels.\nStandardized adversarial attack benchmarks and datasets provide a common ground for evaluating and comparing the robustness of ML models. These benchmarks include datasets with pre-generated adversarial examples and tools and frameworks for generating adversarial attacks. Examples of popular adversarial attack benchmarks include the MNIST-C, CIFAR-10-C, and ImageNet-C (Hendrycks and Dietterich 2019) datasets, which contain corrupted or perturbed versions of the original datasets.\n\nHendrycks, Dan, and Thomas Dietterich. 2019. “Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.” arXiv Preprint arXiv:1903.12261.\nPractitioners can develop more robust and resilient ML systems by leveraging these adversarial example detection techniques, defense strategies, and robustness evaluation methods. However, it is important to note that adversarial robustness is an ongoing research area, and no single technique provides complete protection against all types of adversarial attacks. A comprehensive approach that combines multiple defense mechanisms and regular testing is essential to maintain the security and reliability of ML systems in the face of evolving adversarial threats.\n\n\n\nData Poisoning\nRecall that data poisoning is an attack that targets the integrity of the training data used to build ML models. By manipulating or corrupting the training data, attackers can influence the model’s behavior and cause it to make incorrect predictions or perform unintended actions. Detecting and mitigating data poisoning attacks is crucial to ensure the trustworthiness and reliability of ML systems, as shown in Figure 17.30.\n\nAnomaly Detection Techniques for Identifying Poisoned Data\n\n\n\n\n\n\nFigure 17.30: Malicious data injection. Source: Li\n\n\n\nStatistical outlier detection methods identify data points that deviate significantly from most data. These methods assume that poisoned data instances are likely to be statistical outliers. Techniques such as the Z-score method, Tukey’s method, or the [Mahalanobis] distance can be used to measure the deviation of each data point from the central tendency of the dataset. Data points that exceed a predefined threshold are flagged as potential outliers and considered suspicious for data poisoning.\nClustering-based methods group similar data points together based on their features or attributes. The assumption is that poisoned data instances may form distinct clusters or lie far away from the normal data clusters. By applying clustering algorithms like K-means, DBSCAN, or hierarchical clustering, anomalous clusters or data points that do not belong to any cluster can be identified. These anomalous instances are then treated as potentially poisoned data.\n\n\n\n\n\n\nFigure 17.31: Autoencoder. Source: Dertat\n\n\n\nAutoencoders are neural networks trained to reconstruct the input data from a compressed representation, as shown in Figure 17.31. They can be used for anomaly detection by learning the normal patterns in the data and identifying instances that deviate from them. During training, the autoencoder is trained on clean, unpoisoned data. At inference time, the reconstruction error for each data point is computed. Data points with high reconstruction errors are considered abnormal and potentially poisoned, as they do not conform to the learned normal patterns.\n\n\nData Sanitization and Preprocessing Techniques\nData poisoning can be avoided by cleaning data, which involves identifying and removing or correcting noisy, incomplete, or inconsistent data points. Techniques such as data deduplication, missing value imputation, and outlier removal can be applied to improve the quality of the training data. By eliminating or filtering out suspicious or anomalous data points, the impact of poisoned instances can be reduced.\nData validation involves verifying the integrity and consistency of the training data. This can include checking for data type consistency, range validation, and cross-field dependencies. By defining and enforcing data validation rules, anomalous or inconsistent data points indicative of data poisoning can be identified and flagged for further investigation.\nData provenance and lineage tracking involve maintaining a record of data’s origin, transformations, and movements throughout the ML pipeline. By documenting the data sources, preprocessing steps, and any modifications made to the data, practitioners can trace anomalies or suspicious patterns back to their origin. This helps identify potential points of data poisoning and facilitates the investigation and mitigation process.\n\n\nRobust Training Techniques\nRobust optimization techniques can be used to modify the training objective to minimize the impact of outliers or poisoned instances. This can be achieved by using robust loss functions less sensitive to extreme values, such as the Huber loss or the modified Huber loss. Regularization techniques, such as L1 or L2 regularization, can also help in reducing the model’s sensitivity to poisoned data by constraining the model’s complexity and preventing overfitting.\nRobust loss functions are designed to be less sensitive to outliers or noisy data points. Examples include the modified Huber loss, the Tukey loss (Beaton and Tukey 1974), and the trimmed mean loss. These loss functions down-weight or ignore the contribution of abnormal instances during training, reducing their impact on the model’s learning process. Robust objective functions, such as the minimax or distributionally robust objective, aim to optimize the model’s performance under worst-case scenarios or in the presence of adversarial perturbations.\n\nBeaton, Albert E., and John W. Tukey. 1974. “The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data.” Technometrics 16 (2): 147. https://doi.org/10.2307/1267936.\nData augmentation techniques involve generating additional training examples by applying random transformations or perturbations to the existing data Figure 17.32. This helps in increasing the diversity and robustness of the training dataset. By introducing controlled variations in the data, the model becomes less sensitive to specific patterns or artifacts that may be present in poisoned instances. Randomization techniques, such as random subsampling or bootstrap aggregating, can also help reduce the impact of poisoned data by training multiple models on different subsets of the data and combining their predictions.\n\n\n\n\n\n\nFigure 17.32: An image of the number “3” in original form and with basic augmentations applied.\n\n\n\n\n\nSecure and Trusted Data Sourcing\nImplementing the best data collection and curation practices can help mitigate the risk of data poisoning. This includes establishing clear data collection protocols, verifying the authenticity and reliability of data sources, and conducting regular data quality assessments. Sourcing data from trusted and reputable providers and following secure data handling practices can reduce the likelihood of introducing poisoned data into the training pipeline.\nStrong data governance and access control mechanisms are essential to prevent unauthorized modifications or tampering with the training data. This involves defining clear roles and responsibilities for data access, implementing access control policies based on the principle of least privilege, and monitoring and logging data access activities. By restricting access to the training data and maintaining an audit trail, potential data poisoning attempts can be detected and investigated.\nDetecting and mitigating data poisoning attacks requires a multifaceted approach that combines anomaly detection, data sanitization, robust training techniques, and secure data sourcing practices. By implementing these measures, ML practitioners can improve the resilience of their models against data poisoning and ensure the integrity and trustworthiness of the training data. However, it is important to note that data poisoning is an active area of research, and new attack vectors and defense mechanisms continue to emerge. Staying informed about the latest developments and adopting a proactive and adaptive approach to data security is crucial for maintaining the robustness of ML systems.\n\n\n\nDistribution Shifts\n\nDetecting and Mitigating Distribution Shifts\nRecall that distribution shifts occur when the data distribution encountered by a machine learning (ML) model during deployment differs from the distribution it was trained on. These shifts can significantly impact the model’s performance and generalization ability, leading to suboptimal or incorrect predictions. Detecting and mitigating distribution shifts is crucial to ensure the robustness and reliability of ML systems in real-world scenarios.\n\n\nDetection Techniques for Distribution Shifts\nStatistical tests can be used to compare the distributions of the training and test data to identify significant differences. Techniques such as the Kolmogorov-Smirnov test or the Anderson-Darling test measure the discrepancy between two distributions and provide a quantitative assessment of the presence of distribution shift. By applying these tests to the input features or the model’s predictions, practitioners can detect if there is a statistically significant difference between the training and test distributions.\nDivergence metrics quantify the dissimilarity between two probability distributions. Commonly used divergence metrics include the Kullback-Leibler (KL) divergence and the [Jensen-Shannon (JS)] divergence. By calculating the divergence between the training and test data distributions, practitioners can assess the extent of the distribution shift. High divergence values indicate a significant difference between the distributions, suggesting the presence of a distribution shift.\nUncertainty quantification techniques, such as Bayesian neural networks or ensemble methods, can estimate the uncertainty associated with the model’s predictions. When a model is applied to data from a different distribution, its predictions may have higher uncertainty. By monitoring the uncertainty levels, practitioners can detect distribution shifts. If the uncertainty consistently exceeds a predetermined threshold for test samples, it suggests that the model is operating outside its trained distribution.\nIn addition, domain classifiers are trained to distinguish between different domains or distributions. Practitioners can detect distribution shifts by training a classifier to differentiate between the training and test domains. If the domain classifier achieves high accuracy in distinguishing between the two domains, it indicates a significant difference in the underlying distributions. The performance of the domain classifier serves as a measure of the distribution shift.\n\n\nMitigation Techniques for Distribution Shifts\n\n\n\n\n\n\nFigure 17.33: Transfer learning. Source: Bhavsar\n\n\n\nTransfer learning leverages knowledge gained from one domain to improve performance in another, as shown in Figure 17.33. By using pre-trained models or transferring learned features from a source domain to a target domain, transfer learning can help mitigate the impact of distribution shifts. The pre-trained model can be fine-tuned on a small amount of labeled data from the target domain, allowing it to adapt to the new distribution. Transfer learning is particularly effective when the source and target domains share similar characteristics or when labeled data in the target domain is scarce.\nContinual learning, also known as lifelong learning, enables ML models to learn continuously from new data distributions while retaining knowledge from previous distributions. Techniques such as elastic weight consolidation (EWC) (Kirkpatrick et al. 2017) or gradient episodic memory (GEM) (Lopez-Paz and Ranzato 2017) allow models to adapt to evolving data distributions over time. These techniques aim to balance the plasticity of the model (ability to learn from new data) with the stability of the model (retaining previously learned knowledge). By incrementally updating the model with new data and mitigating catastrophic forgetting, continual learning helps models stay robust to distribution shifts.\n\nKirkpatrick, James, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, et al. 2017. “Overcoming Catastrophic Forgetting in Neural Networks.” Proc. Natl. Acad. Sci. 114 (13): 3521–26. https://doi.org/10.1073/pnas.1611835114.\n\nLopez-Paz, David, and Marc’Aurelio Ranzato. 2017. “Gradient Episodic Memory for Continual Learning.” Adv Neural Inf Process Syst 30.\nData augmentation techniques, such as those we have seen previously, involve applying transformations or perturbations to the existing training data to increase its diversity and improve the model’s robustness to distribution shifts. By introducing variations in the data, such as rotations, translations, scaling, or adding noise, data augmentation helps the model learn invariant features and generalize better to unseen distributions. Data augmentation can be performed during training and inference to improve the model’s ability to handle distribution shifts.\nEnsemble methods combine multiple models to make predictions more robust to distribution shifts. By training models on different subsets of the data, using different algorithms, or with different hyperparameters, ensemble methods can capture diverse aspects of the data distribution. When presented with a shifted distribution, the ensemble can leverage the strengths of individual models to make more accurate and stable predictions. Techniques like bagging, boosting, or stacking can create effective ensembles.\nRegularly updating models with new data from the target distribution is crucial to mitigate the impact of distribution shifts. As the data distribution evolves, models should be retrained or fine-tuned on the latest available data to adapt to the changing patterns. Monitoring model performance and data characteristics can help detect when an update is necessary. By keeping the models up to date, practitioners can ensure they remain relevant and accurate in the face of distribution shifts.\nEvaluating models using robust metrics less sensitive to distribution shifts can provide a more reliable assessment of model performance. Metrics such as the area under the precision-recall curve (AUPRC) or the F1 score are more robust to class imbalance and can better capture the model’s performance across different distributions. Additionally, using domain-specific evaluation metrics that align with the desired outcomes in the target domain can provide a more meaningful measure of the model’s effectiveness.\nDetecting and mitigating distribution shifts is an ongoing process that requires continuous monitoring, adaptation, and improvement. By employing a combination of detection techniques and mitigation strategies, ML practitioners can proactively identify and address distribution shifts, ensuring the robustness and reliability of their models in real-world deployments. It is important to note that distribution shifts can take various forms and may require domain-specific approaches depending on the nature of the data and the application. Staying informed about the latest research and best practices in handling distribution shifts is essential for building resilient ML systems.", + "text": "17.4 ML Model Robustness\n\n17.4.1 Adversarial Attacks\n\nDefinition and Characteristics\nAdversarial attacks aim to trick models into making incorrect predictions by providing them with specially crafted, deceptive inputs (called adversarial examples) (Parrish et al. 2023). By adding slight perturbations to input data, adversaries can “hack” a model’s pattern recognition and deceive it. These are sophisticated techniques where slight, often imperceptible alterations to input data can trick an ML model into making a wrong prediction, as shown in Figure 17.18.\n\nParrish, Alicia, Hannah Rose Kirk, Jessica Quaye, Charvi Rastogi, Max Bartolo, Oana Inel, Juan Ciro, et al. 2023. “Adversarial Nibbler: A Data-Centric Challenge for Improving the Safety of Text-to-Image Models.” ArXiv Preprint abs/2305.14384. https://arxiv.org/abs/2305.14384.\n\n\n\n\n\n\nFigure 17.18: A small adversarial noise added to the original image can make the neural network classify the image as a Guacamole instead of an Egyptian cat. Source: Sutanto\n\n\n\nOne can generate prompts that lead to unsafe images in text-to-image models like DALLE (Ramesh et al. 2021) or Stable Diffusion (Rombach et al. 2022). For example, by altering the pixel values of an image, attackers can deceive a facial recognition system into identifying a face as a different person.\n\nRamesh, Aditya, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021. “Zero-Shot Text-to-Image Generation.” In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event, edited by Marina Meila and Tong Zhang, 139:8821–31. Proceedings of Machine Learning Research. PMLR. http://proceedings.mlr.press/v139/ramesh21a.html.\n\nRombach, Robin, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjorn Ommer. 2022. “High-Resolution Image Synthesis with Latent Diffusion Models.” In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. https://doi.org/10.1109/cvpr52688.2022.01042.\nAdversarial attacks exploit the way ML models learn and make decisions during inference. These models work on the principle of recognizing patterns in data. An adversary crafts special inputs with perturbations to mislead the model’s pattern recognition---essentially ‘hacking’ the model’s perceptions.\nAdversarial attacks fall under different scenarios:\n\nWhitebox Attacks: The attacker fully knows the target model’s internal workings, including the training data, parameters, and architecture (Ye and Hamidi 2021). This comprehensive access creates favorable conditions for attackers to exploit the model’s vulnerabilities. The attacker can use specific and subtle weaknesses to craft effective adversarial examples.\nBlackbox Attacks: In contrast to white-box attacks, black-box attacks involve the attacker having little to no knowledge of the target model (Guo et al. 2019). To carry out the attack, the adversarial actor must carefully observe the model’s output behavior.\nGreybox Attacks: These fall between blackbox and whitebox attacks. The attacker has only partial knowledge about the target model’s internal design (Xu et al. 2021). For example, the attacker could have knowledge about training data but not the architecture or parameters. In the real world, practical attacks fall under black black-box box grey-boxes.\n\n\nYe, Linfeng, and Shayan Mohajer Hamidi. 2021. “Thundernna: A White Box Adversarial Attack.” arXiv Preprint arXiv:2111.12305.\n\nGuo, Chuan, Jacob Gardner, Yurong You, Andrew Gordon Wilson, and Kilian Weinberger. 2019. “Simple Black-Box Adversarial Attacks.” In International Conference on Machine Learning, 2484–93. PMLR.\n\nXu, Ying, Xu Zhong, Antonio Jimeno Yepes, and Jey Han Lau. 2021. “Grey-Box Adversarial Attack and Defence for Sentiment Classification.” arXiv Preprint arXiv:2103.11576.\nThe landscape of machine learning models is complex and broad, especially given their relatively recent integration into commercial applications. This rapid adoption, while transformative, has brought to light numerous vulnerabilities within these models. Consequently, various adversarial attack methods have emerged, each strategically exploiting different aspects of different models. Below, we highlight a subset of these methods, showcasing the multifaceted nature of adversarial attacks on machine learning models:\n\nGenerative Adversarial Networks (GANs) are deep learning models that consist of two networks competing against each other: a generator and a discriminator (Goodfellow et al. 2020). The generator tries to synthesize realistic data while the discriminator evaluates whether they are real or fake. GANs can be used to craft adversarial examples. The generator network is trained to produce inputs that the target model misclassifies. These GAN-generated images can then attack a target classifier or detection model. The generator and the target model are engaged in a competitive process, with the generator continually improving its ability to create deceptive examples and the target model enhancing its resistance to such examples. GANs provide a powerful framework for crafting complex and diverse adversarial inputs, illustrating the adaptability of generative models in the adversarial landscape.\nTransfer Learning Adversarial Attacks exploit the knowledge transferred from a pre-trained model to a target model, creating adversarial examples that can deceive both models. These attacks pose a growing concern, particularly when adversaries have knowledge of the feature extractor but lack access to the classification head (the part or layer responsible for making the final classifications). Referred to as “headless attacks,” these transferable adversarial strategies leverage the expressive capabilities of feature extractors to craft perturbations while being oblivious to the label space or training data. The existence of such attacks underscores the importance of developing robust defenses for transfer learning applications, especially since pre-trained models are commonly used (Abdelkader et al. 2020).\n\n\nGoodfellow, Ian, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2020. “Generative Adversarial Networks.” Commun. ACM 63 (11): 139–44. https://doi.org/10.1145/3422622.\n\nAbdelkader, Ahmed, Michael J. Curry, Liam Fowl, Tom Goldstein, Avi Schwarzschild, Manli Shu, Christoph Studer, and Chen Zhu. 2020. “Headless Horseman: Adversarial Attacks on Transfer Learning Models.” In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3087–91. IEEE. https://doi.org/10.1109/icassp40776.2020.9053181.\n\n\nMechanisms of Adversarial Attacks\n\n\n\n\n\n\nFigure 17.19: Gradient-Based Attacks. Source: Ivezic\n\n\n\nGradient-based Attacks\nOne prominent category of adversarial attacks is gradient-based attacks. These attacks leverage the gradients of the ML model’s loss function to craft adversarial examples. The Fast Gradient Sign Method (FGSM) is a well-known technique in this category. FGSM perturbs the input data by adding small noise in the gradient direction, aiming to maximize the model’s prediction error. FGSM can quickly generate adversarial examples, as shown in Figure 17.19, by taking a single step in the gradient direction.\nAnother variant, the Projected Gradient Descent (PGD) attack, extends FGSM by iteratively applying the gradient update step, allowing for more refined and powerful adversarial examples. The Jacobian-based Saliency Map Attack (JSMA) is another gradient-based approach that identifies the most influential input features and perturbs them to create adversarial examples.\nOptimization-based Attacks\nThese attacks formulate the generation of adversarial examples as an optimization problem. The Carlini and Wagner (C&W) attack is a prominent example in this category. It finds the smallest perturbation that can cause misclassification while maintaining the perceptual similarity to the original input. The C&W attack employs an iterative optimization process to minimize the perturbation while maximizing the model’s prediction error.\nAnother optimization-based approach is the Elastic Net Attack to DNNs (EAD), which incorporates elastic net regularization to generate adversarial examples with sparse perturbations.\nTransfer-based Attacks\nTransfer-based attacks exploit the transferability property of adversarial examples. Transferability refers to the phenomenon where adversarial examples crafted for one ML model can often fool other models, even if they have different architectures or were trained on different datasets. This enables attackers to generate adversarial examples using a surrogate model and then transfer them to the target model without requiring direct access to its parameters or gradients. Transfer-based attacks highlight the generalization of adversarial vulnerabilities across different models and the potential for black-box attacks.\nPhysical-world Attacks\nPhysical-world attacks bring adversarial examples into the realm of real-world scenarios. These attacks involve creating physical objects or manipulations that can deceive ML models when captured by sensors or cameras. Adversarial patches, for example, are small, carefully designed patches that can be placed on objects to fool object detection or classification models. When attached to real-world objects, these patches can cause models to misclassify or fail to detect the objects accurately. Adversarial objects, such as 3D-printed sculptures or modified road signs, can also be crafted to deceive ML systems in physical environments.\nSummary\nTable 17.2 a concise overview of the different categories of adversarial attacks, including gradient-based attacks (FGSM, PGD, JSMA), optimization-based attacks (C&W, EAD), transfer-based attacks, and physical-world attacks (adversarial patches and objects). Each attack is briefly described, highlighting its key characteristics and mechanisms.\n\n\n\nTable 17.2: Different attack types on ML models.\n\n\n\n\n\n\n\n\n\n\nAttack Category\nAttack Name\nDescription\n\n\n\n\nGradient-based\nFast Gradient Sign Method (FGSM) Projected Gradient Descent (PGD) Jacobian-based Saliency Map Attack (JSMA)\nPerturbs input data by adding small noise in the gradient direction to maximize prediction error. Extends FGSM by iteratively applying the gradient update step for more refined adversarial examples. Identifies influential input features and perturbs them to create adversarial examples.\n\n\nOptimization-based\nCarlini and Wagner (C&W) Attack Elastic Net Attack to DNNs (EAD)\nFinds the smallest perturbation that causes misclassification while maintaining perceptual similarity. Incorporates elastic net regularization to generate adversarial examples with sparse perturbations.\n\n\nTransfer-based\nTransferability-based Attacks\nExploits the transferability of adversarial examples across different models, enabling black-box attacks.\n\n\nPhysical-world\nAdversarial Patches Adversarial Objects\nSmall, carefully designed patches placed on objects to fool object detection or classification models. Physical objects (e.g., 3D-printed sculptures, modified road signs) crafted to deceive ML systems in real-world scenarios.\n\n\n\n\n\n\nThe mechanisms of adversarial attacks reveal the intricate interplay between the ML model’s decision boundaries, the input data, and the attacker’s objectives. By carefully manipulating the input data, attackers can exploit the model’s sensitivities and blind spots, leading to incorrect predictions. The success of adversarial attacks highlights the need for a deeper understanding of ML models’ robustness and generalization properties.\nDefending against adversarial attacks requires a multifaceted approach. Adversarial training is one common defense strategy in which models are trained on adversarial examples to improve robustness. Exposing the model to adversarial examples during training teaches it to classify them correctly and become more resilient to attacks. Defensive distillation, input preprocessing, and ensemble methods are other techniques that can help mitigate the impact of adversarial attacks.\nAs adversarial machine learning evolves, researchers explore new attack mechanisms and develop more sophisticated defenses. The arms race between attackers and defenders drives the need for constant innovation and vigilance in securing ML systems against adversarial threats. Understanding the mechanisms of adversarial attacks is crucial for developing robust and reliable ML models that can withstand the ever-evolving landscape of adversarial examples.\n\n\nImpact on ML Systems\nAdversarial attacks on machine learning systems have emerged as a significant concern in recent years, highlighting the potential vulnerabilities and risks associated with the widespread adoption of ML technologies. These attacks involve carefully crafted perturbations to input data that can deceive or mislead ML models, leading to incorrect predictions or misclassifications, as shown in Figure 17.20. The impact of adversarial attacks on ML systems is far-reaching and can have serious consequences in various domains.\nOne striking example of the impact of adversarial attacks was demonstrated by researchers in 2017. They experimented with small black and white stickers on stop signs (Eykholt et al. 2017). To the human eye, these stickers did not obscure the sign or prevent its interpretability. However, when images of the sticker-modified stop signs were fed into standard traffic sign classification ML models, a shocking result emerged. The models misclassified the stop signs as speed limit signs over 85% of the time.\n\nEykholt, Kevin, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. 2017. “Robust Physical-World Attacks on Deep Learning Models.” ArXiv Preprint abs/1707.08945. https://arxiv.org/abs/1707.08945.\nThis demonstration shed light on the alarming potential of simple adversarial stickers to trick ML systems into misreading critical road signs. The implications of such attacks in the real world are significant, particularly in the context of autonomous vehicles. If deployed on actual roads, these adversarial stickers could cause self-driving cars to misinterpret stop signs as speed limits, leading to dangerous situations, as shown in Figure 17.21. Researchers warned that this could result in rolling stops or unintended acceleration into intersections, endangering public safety.\n\n\n\n\n\n\nFigure 17.20: Adversarial example generation applied to GoogLeNet (Szegedy et al., 2014a) on ImageNet. Source: Goodfellow\n\n\n\n\n\n\n\n\n\nFigure 17.21: Graffiti on a stop sign tricked a self-driving car into thinking it was a 45 mph speed limit sign. Source: Eykholt\n\n\n\nThe case study of the adversarial stickers on stop signs provides a concrete illustration of how adversarial examples exploit how ML models recognize patterns. By subtly manipulating the input data in ways that are invisible to humans, attackers can induce incorrect predictions and create serious risks, especially in safety-critical applications like autonomous vehicles. The attack’s simplicity highlights the vulnerability of ML models to even minor changes in the input, emphasizing the need for robust defenses against such threats.\nThe impact of adversarial attacks extends beyond the degradation of model performance. These attacks raise significant security and safety concerns, particularly in domains where ML models are relied upon for critical decision-making. In healthcare applications, adversarial attacks on medical imaging models could lead to misdiagnosis or incorrect treatment recommendations, jeopardizing patient well-being (M.-J. Tsai, Lin, and Lee 2023). In financial systems, adversarial attacks could enable fraud or manipulation of trading algorithms, resulting in substantial economic losses.\n\nTsai, Min-Jen, Ping-Yi Lin, and Ming-En Lee. 2023. “Adversarial Attacks on Medical Image Classification.” Cancers 15 (17): 4228. https://doi.org/10.3390/cancers15174228.\n\nFursov, Ivan, Matvey Morozov, Nina Kaploukhaya, Elizaveta Kovtun, Rodrigo Rivera-Castro, Gleb Gusev, Dmitry Babaev, Ivan Kireev, Alexey Zaytsev, and Evgeny Burnaev. 2021. “Adversarial Attacks on Deep Models for Financial Transaction Records.” In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining, 2868–78. ACM. https://doi.org/10.1145/3447548.3467145.\nMoreover, adversarial vulnerabilities undermine the trustworthiness and interpretability of ML models. If carefully crafted perturbations can easily fool models, confidence in their predictions and decisions erodes. Adversarial examples expose the models’ reliance on superficial patterns and the inability to capture the true underlying concepts, challenging the reliability of ML systems (Fursov et al. 2021).\nDefending against adversarial attacks often requires additional computational resources and can impact the overall system performance. Techniques like adversarial training, where models are trained on adversarial examples to improve robustness, can significantly increase training time and computational requirements (Bai et al. 2021). Runtime detection and mitigation mechanisms, such as input preprocessing (Addepalli et al. 2020) or prediction consistency checks, introduce latency and affect the real-time performance of ML systems.\n\nBai, Tao, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang. 2021. “Recent Advances in Adversarial Training for Adversarial Robustness.” arXiv Preprint arXiv:2102.01356.\n\nAddepalli, Sravanti, B. S. Vivek, Arya Baburaj, Gaurang Sriramanan, and R. Venkatesh Babu. 2020. “Towards Achieving Adversarial Robustness by Enforcing Feature Consistency Across Bit Planes.” In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1020–29. IEEE. https://doi.org/10.1109/cvpr42600.2020.00110.\nThe presence of adversarial vulnerabilities also complicates the deployment and maintenance of ML systems. System designers and operators must consider the potential for adversarial attacks and incorporate appropriate defenses and monitoring mechanisms. Regular updates and retraining of models become necessary to adapt to new adversarial techniques and maintain system security and performance over time.\nThe impact of adversarial attacks on ML systems is significant and multifaceted. These attacks expose ML models’ vulnerabilities, from degrading model performance and raising security and safety concerns to challenging model trustworthiness and interpretability. Developers and researchers must prioritize the development of robust defenses and countermeasures to mitigate the risks posed by adversarial attacks. By addressing these challenges, we can build more secure, reliable, and trustworthy ML systems that can withstand the ever-evolving landscape of adversarial threats.\n\n\n\n\n\n\nExercise 17.2: Adversarial Attacks\n\n\n\n\n\nGet ready to become an AI adversary! In this Colab, you’ll become a white-box hacker, learning to craft attacks that deceive image classification models. We’ll focus on the Fast Gradient Sign Method (FGSM), where you’ll weaponize a model’s gradients against it! You’ll deliberately distort images with tiny perturbations, observing how they increasingly fool the AI more intensely. This hands-on exercise highlights the importance of building secure AI – a critical skill as AI integrates into cars and healthcare. The Colab directly ties into the Robust AI chapter of your book, moving adversarial attacks from theory into your own hands-on experience.\n\nThink you can outsmart an AI? In this Colab, learn how to trick image classification models with adversarial attacks. We’ll use methods like FGSM to change images and subtly fool the AI. Discover how to design deceptive image patches and witness the surprising vulnerability of these powerful models. This is crucial knowledge for building truly robust AI systems!\n\n\n\n\n\n\n\n17.4.2 Data Poisoning\n\nDefinition and Characteristics\nData poisoning is an attack where the training data is tampered with, leading to a compromised model (Biggio, Nelson, and Laskov 2012), as shown in Figure 17.22. Attackers can modify existing training examples, insert new malicious data points, or influence the data collection process. The poisoned data is labeled in such a way as to skew the model’s learned behavior. This can be particularly damaging in applications where ML models make automated decisions based on learned patterns. Beyond training sets, poisoning tests, and validation data can allow adversaries to boost reported model performance artificially.\n\nBiggio, Battista, Blaine Nelson, and Pavel Laskov. 2012. “Poisoning Attacks Against Support Vector Machines.” In Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012. icml.cc / Omnipress. http://icml.cc/2012/papers/880.pdf.\n\n\n\n\n\n\nFigure 17.22: NightShade’s poisoning effects on Stable Diffusion. Source: TOMÉ\n\n\n\nThe process usually involves the following steps:\n\nInjection: The attacker adds incorrect or misleading examples into the training set. These examples are often designed to look normal to cursory inspection but have been carefully crafted to disrupt the learning process.\nTraining: The ML model trains on this manipulated dataset and develops skewed understandings of the data patterns.\nDeployment: Once the model is deployed, the corrupted training leads to flawed decision-making or predictable vulnerabilities the attacker can exploit.\n\nThe impact of data poisoning extends beyond classification errors or accuracy drops. In critical applications like healthcare, such alterations can lead to significant trust and safety issues (Marulli, Marrone, and Verde 2022). Later, we will discuss a few case studies of these issues.\n\nMarulli, Fiammetta, Stefano Marrone, and Laura Verde. 2022. “Sensitivity of Machine Learning Approaches to Fake and Untrusted Data in Healthcare Domain.” Journal of Sensor and Actuator Networks 11 (2): 21. https://doi.org/10.3390/jsan11020021.\n\nOprea, Alina, Anoop Singhal, and Apostol Vassilev. 2022. “Poisoning Attacks Against Machine Learning: Can Machine Learning Be Trustworthy?” Computer 55 (11): 94–99. https://doi.org/10.1109/mc.2022.3190787.\nThere are six main categories of data poisoning (Oprea, Singhal, and Vassilev 2022):\n\nAvailability Attacks: These attacks aim to compromise the overall functionality of a model. They cause it to misclassify most testing samples, rendering the model unusable for practical applications. An example is label flipping, where labels of a specific, targeted class are replaced with labels from a different one.\nTargeted Attacks: In contrast to availability attacks, targeted attacks aim to compromise a small number of the testing samples. So, the effect is localized to a limited number of classes, while the model maintains the same original level of accuracy for the majority of the classes. The targeted nature of the attack requires the attacker to possess knowledge of the model’s classes, making detecting these attacks more challenging.\nBackdoor Attacks: In these attacks, an adversary targets specific patterns in the data. The attacker introduces a backdoor (a malicious, hidden trigger or pattern) into the training data, such as manipulating certain features in structured data or manipulating a pattern of pixels at a fixed position. This causes the model to associate the malicious pattern with specific labels. As a result, when the model encounters test samples that contain a malicious pattern, it makes false predictions.\nSubpopulation Attacks: Attackers selectively choose to compromise a subset of the testing samples while maintaining accuracy on the rest of the samples. You can think of these attacks as a combination of availability and targeted attacks: performing availability attacks (performance degradation) within the scope of a targeted subset. Although subpopulation attacks may seem very similar to targeted attacks, the two have clear differences:\nScope: While targeted attacks target a selected set of samples, subpopulation attacks target a general subpopulation with similar feature representations. For example, in a targeted attack, an actor inserts manipulated images of a ‘speed bump’ warning sign (with carefully crafted perturbations or patterns), which causes an autonomous car to fail to recognize such a sign and slow down. On the other hand, manipulating all samples of people with a British accent so that a speech recognition model would misclassify a British person’s speech is an example of a subpopulation attack.\nKnowledge: While targeted attacks require a high degree of familiarity with the data, subpopulation attacks require less intimate knowledge to be effective.\n\nThe characteristics of data poisoning include:\nSubtle and hard-to-detect manipulations of training data: Data poisoning often involves subtle manipulations of the training data that are carefully crafted to be difficult to detect through casual inspection. Attackers employ sophisticated techniques to ensure that the poisoned samples blend seamlessly with the legitimate data, making them easier to identify with thorough analysis. These manipulations can target specific features or attributes of the data, such as altering numerical values, modifying categorical labels, or introducing carefully designed patterns. The goal is to influence the model’s learning process while evading detection, allowing the poisoned data to subtly corrupt the model’s behavior.\nCan be performed by insiders or external attackers: Data poisoning attacks can be carried out by various actors, including malicious insiders with access to the training data and external attackers who find ways to influence the data collection or preprocessing pipeline. Insiders pose a significant threat because they often have privileged access and knowledge of the system, enabling them to introduce poisoned data without raising suspicions. On the other hand, external attackers may exploit vulnerabilities in data sourcing, crowdsourcing platforms, or data aggregation processes to inject poisoned samples into the training dataset. This highlights the importance of implementing strong access controls, data governance policies, and monitoring mechanisms to mitigate the risk of insider threats and external attacks.\nExploits vulnerabilities in data collection and preprocessing: Data poisoning attacks often exploit vulnerabilities in the machine learning pipeline’s data collection and preprocessing stages. Attackers carefully design poisoned samples to evade common data validation techniques, ensuring that the manipulated data still falls within acceptable ranges, follows expected distributions, or maintains consistency with other features. This allows the poisoned data to pass through data preprocessing steps without detection. Furthermore, poisoning attacks can take advantage of weaknesses in data preprocessing, such as inadequate data cleaning, insufficient outlier detection, or lack of integrity checks. Attackers may also exploit the lack of robust data provenance and lineage tracking mechanisms to introduce poisoned data without leaving a traceable trail. Addressing these vulnerabilities requires rigorous data validation, anomaly detection, and data provenance tracking techniques to ensure the integrity and trustworthiness of the training data.\nDisrupts the learning process and skews model behavior: Data poisoning attacks are designed to disrupt the learning process of machine learning models and skew their behavior towards the attacker’s objectives. The poisoned data is typically manipulated with specific goals, such as skewing the model’s behavior towards certain classes, introducing backdoors, or degrading overall performance. These manipulations are not random but targeted to achieve the attacker’s desired outcomes. By introducing label inconsistencies, where the manipulated samples have labels that do not align with their true nature, poisoning attacks can confuse the model during training and lead to biased or incorrect predictions. The disruption caused by poisoned data can have far-reaching consequences, as the compromised model may make flawed decisions or exhibit unintended behavior when deployed in real-world applications.\nImpacts model performance, fairness, and trustworthiness: Poisoned data in the training dataset can have severe implications for machine learning models’ performance, fairness, and trustworthiness. Poisoned data can degrade the accuracy and performance of the trained model, leading to increased misclassifications or errors in predictions. This can have significant consequences, especially in critical applications where the model’s outputs inform important decisions. Moreover, poisoning attacks can introduce biases and fairness issues, causing the model to make discriminatory or unfair decisions for certain subgroups or classes. This undermines machine learning systems’ ethical and social responsibilities and can perpetuate or amplify existing biases. Furthermore, poisoned data erodes the trustworthiness and reliability of the entire ML system. The model’s outputs become questionable and potentially harmful, leading to a loss of confidence in the system’s integrity. The impact of poisoned data can propagate throughout the entire ML pipeline, affecting downstream components and decisions that rely on the compromised model. Addressing these concerns requires robust data governance, regular model auditing, and ongoing monitoring to detect and mitigate the effects of data poisoning attacks.\n\n\nMechanisms of Data Poisoning\nData poisoning attacks can be carried out through various mechanisms, exploiting different ML pipeline vulnerabilities. These mechanisms allow attackers to manipulate the training data and introduce malicious samples that can compromise the model’s performance, fairness, or integrity. Understanding these mechanisms is crucial for developing effective defenses against data poisoning and ensuring the robustness of ML systems. Data poisoning mechanisms can be broadly categorized based on the attacker’s approach and the stage of the ML pipeline they target. Some common mechanisms include modifying training data labels, altering feature values, injecting carefully crafted malicious samples, exploiting data collection and preprocessing vulnerabilities, manipulating data at the source, poisoning data in online learning scenarios, and collaborating with insiders to manipulate data.\nEach of these mechanisms presents unique challenges and requires different mitigation strategies. For example, detecting label manipulation may involve analyzing the distribution of labels and identifying anomalies (Zhou et al. 2018), while preventing feature manipulation may require secure data preprocessing and anomaly detection techniques (Carta et al. 2020). Defending against insider threats may involve strict access control policies and monitoring of data access patterns. Moreover, the effectiveness of data poisoning attacks often depends on the attacker’s knowledge of the ML system, including the model architecture, training algorithms, and data distribution. Attackers may use adversarial machine learning or data synthesis techniques to craft samples that are more likely to bypass detection and achieve their malicious objectives.\n\nZhou, Peng, Xintong Han, Vlad I. Morariu, and Larry S. Davis. 2018. “Learning Rich Features for Image Manipulation Detection.” In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1053–61. IEEE. https://doi.org/10.1109/cvpr.2018.00116.\n\nCarta, Salvatore, Alessandro Sebastian Podda, Diego Reforgiato Recupero, and Roberto Saia. 2020. “A Local Feature Engineering Strategy to Improve Network Anomaly Detection.” Future Internet 12 (10): 177. https://doi.org/10.3390/fi12100177.\n\n\n\n\n\n\nFigure 17.23: Garbage In – Garbage Out. Source: Information Matters\n\n\n\nModifying training data labels: One of the most straightforward mechanisms of data poisoning is modifying the training data labels. In this approach, the attacker selectively changes the labels of a subset of the training samples to mislead the model’s learning process as shown in Figure 17.23. For example, in a binary classification task, the attacker might flip the labels of some positive samples to negative, or vice versa. By introducing such label noise, the attacker degrades the model’s performance or cause it to make incorrect predictions for specific target instances.\nAltering feature values in training data: Another mechanism of data poisoning involves altering the feature values of the training samples without modifying the labels. The attacker carefully crafts the feature values to introduce specific biases or vulnerabilities into the model. For instance, in an image classification task, the attacker might add imperceptible perturbations to a subset of images, causing the model to learn a particular pattern or association. This type of poisoning can create backdoors or trojans in the trained model, which specific input patterns can trigger.\nInjecting carefully crafted malicious samples: In this mechanism, the attacker creates malicious samples designed to poison the model. These samples are crafted to have a specific impact on the model’s behavior while blending in with the legitimate training data. The attacker might use techniques such as adversarial perturbations or data synthesis to generate poisoned samples that are difficult to detect. The attacker manipulates the model’s decision boundaries by injecting these malicious samples into the training data or introducing targeted misclassifications.\nExploiting data collection and preprocessing vulnerabilities: Data poisoning attacks can also exploit the data collection and preprocessing pipeline vulnerabilities. If the data collection process is not secure or there are weaknesses in the data preprocessing steps, an attacker can manipulate the data before it reaches the training phase. For example, if data is collected from untrusted sources or issues in data cleaning or aggregation, an attacker can introduce poisoned samples or manipulate the data to their advantage.\nManipulating data at the source (e.g., sensor data): In some cases, attackers can manipulate the data at its source, such as sensor data or input devices. By tampering with the sensors or manipulating the environment in which data is collected, attackers can introduce poisoned samples or bias the data distribution. For instance, in a self-driving car scenario, an attacker might manipulate the sensors or the environment to feed misleading information into the training data, compromising the model’s ability to make safe and reliable decisions.\n\n\n\n\n\n\nFigure 17.24: Data Poisoning Attack. Source: Sikandar\n\n\n\nPoisoning data in online learning scenarios: Data poisoning attacks can also target ML systems that employ online learning, where the model is continuously updated with new data in real time. In such scenarios, an attacker can gradually inject poisoned samples over time, slowly manipulating the model’s behavior. Online learning systems are particularly vulnerable to data poisoning because they adapt to new data without extensive validation, making it easier for attackers to introduce malicious samples, as shown in Figure 17.24.\nCollaborating with insiders to manipulate data: Sometimes, data poisoning attacks can involve collaboration with insiders with access to the training data. Malicious insiders, such as employees or data providers, can manipulate the data before it is used to train the model. Insider threats are particularly challenging to detect and prevent, as the attackers have legitimate access to the data and can carefully craft the poisoning strategy to evade detection.\nThese are the key mechanisms of data poisoning in ML systems. Attackers often employ these mechanisms to make their attacks more effective and harder to detect. The risk of data poisoning attacks grows as ML systems become increasingly complex and rely on larger datasets from diverse sources. Defending against data poisoning requires a multifaceted approach. ML practitioners and system designers must be aware of the various mechanisms of data poisoning and adopt a comprehensive approach to data security and model resilience. This includes secure data collection, robust data validation, and continuous model performance monitoring. Implementing secure data collection and preprocessing practices is crucial to prevent data poisoning at the source. Data validation and anomaly detection techniques can also help identify and mitigate potential poisoning attempts. Monitoring model performance for signs of data poisoning is also essential to detect and respond to attacks promptly.\n\n\nImpact on ML Systems\nData poisoning attacks can severely affect ML systems, compromising their performance, reliability, and trustworthiness. The impact of data poisoning can manifest in various ways, depending on the attacker’s objectives and the specific mechanism used. Let’s explore each of the potential impacts in detail.\nDegradation of model performance: One of the primary impacts of data poisoning is the degradation of the model’s overall performance. By manipulating the training data, attackers can introduce noise, biases, or inconsistencies that hinder the model’s ability to learn accurate patterns and make reliable predictions. This can reduce accuracy, precision, recall, or other performance metrics. The degradation of model performance can have significant consequences, especially in critical applications such as healthcare, finance, or security, where the reliability of predictions is crucial.\nMisclassification of specific targets: Data poisoning attacks can also be designed to cause the model to misclassify specific target instances. Attackers may introduce carefully crafted poisoned samples similar to the target instances, leading the model to learn incorrect associations. This can result in the model consistently misclassifying the targeted instances, even if it performs well on other inputs. Such targeted misclassification can have severe consequences, such as causing a malware detection system to overlook specific malicious files or leading to the wrong diagnosis in a medical imaging application.\nBackdoors and trojans in trained models: Data poisoning can introduce backdoors or trojans into the trained model. Backdoors are hidden functionalities that allow attackers to trigger specific behaviors or bypass normal authentication mechanisms. On the other hand, Trojans are malicious components embedded within the model that can activate specific input patterns. By poisoning the training data, attackers can create models that appear to perform normally but contain hidden vulnerabilities that can be exploited later. Backdoors and trojans can compromise the integrity and security of the ML system, allowing attackers to gain unauthorized access, manipulate predictions, or exfiltrate sensitive information.\nBiased or unfair model outcomes: Data poisoning attacks can introduce biases or unfairness into the model’s predictions. By manipulating the training data distribution or injecting samples with specific biases, attackers can cause the model to learn and perpetuate discriminatory patterns. This can lead to unfair treatment of certain groups or individuals based on sensitive attributes such as race, gender, or age. Biased models can have severe societal implications, reinforcing existing inequalities and discriminatory practices. Ensuring fairness and mitigating biases is crucial for building trustworthy and ethical ML systems.\nIncreased false positives or false negatives: Data poisoning can also impact the model’s ability to correctly identify positive or negative instances, leading to increased false positives or false negatives. False positives occur when the model incorrectly identifies a negative instance as positive, while false negatives happen when a positive instance is misclassified as negative. The consequences of increased false positives or false negatives can be significant depending on the application. For example, in a fraud detection system, high false positives can lead to unnecessary investigations and customer frustration, while high false negatives can allow fraudulent activities to go undetected.\nCompromised system reliability and trustworthiness: Data poisoning attacks can undermine ML systems’ overall reliability and trustworthiness. When models are trained on poisoned data, their predictions become reliable and trustworthy. This can erode user confidence in the system and lead to a loss of trust in the decisions made by the model. In critical applications where ML systems are relied upon for decision-making, such as autonomous vehicles or medical diagnosis, compromised reliability can have severe consequences, putting lives and property at risk.\nAddressing the impact of data poisoning requires a proactive approach to data security, model testing, and monitoring. Organizations must implement robust measures to ensure the integrity and quality of training data, employ techniques to detect and mitigate poisoning attempts, and continuously monitor the performance and behavior of deployed models. Collaboration between ML practitioners, security experts, and domain specialists is essential to develop comprehensive strategies for preventing and responding to data poisoning attacks.\n\nCase Study 1\nIn 2017, researchers demonstrated a data poisoning attack against a popular toxicity classification model called Perspective (Hosseini et al. 2017). This ML model detects toxic comments online.\n\nHosseini, Hossein, Sreeram Kannan, Baosen Zhang, and Radha Poovendran. 2017. “Deceiving Google’s Perspective Api Built for Detecting Toxic Comments.” ArXiv Preprint abs/1702.08138. https://arxiv.org/abs/1702.08138.\nThe researchers added synthetically generated toxic comments with slight misspellings and grammatical errors to the model’s training data. This slowly corrupted the model, causing it to misclassify increasing numbers of severely toxic inputs as non-toxic over time.\nAfter retraining on the poisoned data, the model’s false negative rate increased from 1.4% to 27% - allowing extremely toxic comments to bypass detection. The researchers warned this stealthy data poisoning could enable the spread of hate speech, harassment, and abuse if deployed against real moderation systems.\nThis case highlights how data poisoning can degrade model accuracy and reliability. For social media platforms, a poisoning attack that impairs toxicity detection could lead to the proliferation of harmful content and distrust of ML moderation systems. The example demonstrates why securing training data integrity and monitoring for poisoning is critical across application domains.\n\n\nCase Study 2\n\n\n\n\n\n\nFigure 17.25: Samples of dirty-label poison data regarding mismatched text/image pairs. Source: Shan\n\n\n\nInterestingly enough, data poisoning attacks are not always malicious (Shan et al. 2023). Nightshade, a tool developed by a team led by Professor Ben Zhao at the University of Chicago, utilizes data poisoning to help artists protect their art against scraping and copyright violations by generative AI models. Artists can use the tool to make subtle modifications to their images before uploading them online, as shown in Figure 17.25.\nWhile these changes are indiscernible to the human eye, they can significantly disrupt the performance of generative AI models when incorporated into the training data. Generative models can be manipulated to generate hallucinations and weird images. For example, with only 300 poisoned images, the University of Chicago researchers could trick the latest Stable Diffusion model into generating images of dogs that look like cats or images of cows when prompted for cars.\nAs the number of poisoned images on the internet increases, the performance of the models that use scraped data will deteriorate exponentially. First, the poisoned data is hard to detect and requires manual elimination. Second, the “poison” spreads quickly to other labels because generative models rely on connections between words and concepts as they generate images. So a poisoned image of a “car” could spread into generated images associated with words like “truck,” “train,” ” bus,” etc.\nOn the other hand, this tool can be used maliciously and can affect legitimate applications of the generative models. This shows the very challenging and novel nature of machine learning attacks.\nFigure 17.26 demonstrates the effects of different levels of data poisoning (50 samples, 100 samples, and 300 samples of poisoned images) on generating images in different categories. Notice how the images start deforming and deviating from the desired category. For example, after 300 poison samples, a car prompt generates a cow.\n\n\n\n\n\n\nFigure 17.26: Data poisoning. Source: Shan et al. (2023))\n\n\nShan, Shawn, Wenxin Ding, Josephine Passananti, Haitao Zheng, and Ben Y Zhao. 2023. “Prompt-Specific Poisoning Attacks on Text-to-Image Generative Models.” ArXiv Preprint abs/2310.13828. https://arxiv.org/abs/2310.13828.\n\n\n\n\n\n\n\n\nExercise 17.3: Poisoning Attacks\n\n\n\n\n\nGet ready to explore the dark side of AI security! In this Colab, you’ll learn about data poisoning – how bad data can trick AI models into making wrong decisions. We’ll focus on a real-world attack against a Support Vector Machine (SVM), observing how the AI’s behavior changes under attack. This hands-on exercise will highlight why protecting AI systems is crucial, especially as they become more integrated into our lives. Think like a hacker, understand the vulnerability, and brainstorm how to defend our AI systems!\n\n\n\n\n\n\n\n\n17.4.3 Distribution Shifts\n\nDefinition and Characteristics\nDistribution shift refers to the phenomenon where the data distribution encountered by an ML model during deployment (inference) differs from the distribution it was trained on, as shown in Figure 17.27. This is not so much an attack as it is that the model’s robustness will vary over time. In other words, the data’s statistical properties, patterns, or underlying assumptions can change between the training and test phases.\n\n\n\n\n\n\nFigure 17.27: The curly brackets enclose the distribution shift between the environments. Here, z stands for the spurious feature, and y stands for label class. Source: Xin\n\n\n\nThe key characteristics of distribution shift include:\nDomain mismatch: The input data during inference comes from a different domain or distribution than the training data. When the input data during inference comes from a domain or distribution different from the training data, it can significantly affect the model’s performance. This is because the model has learned patterns and relationships specific to the training domain, and when applied to a different domain, those learned patterns may not hold. For example, consider a sentiment analysis model trained on movie reviews. Suppose this model is applied to analyze sentiment in tweets. In that case, it may need help to accurately classify the sentiment because the language, grammar, and context of tweets can differ from movie reviews. This domain mismatch can result in poor performance and unreliable predictions, limiting the model’s practical utility.\nTemporal drift: The data distribution evolves, leading to a gradual or sudden shift in the input characteristics. Temporal drift is important because ML models are often deployed in dynamic environments where the data distribution can change over time. If the model is not updated or adapted to these changes, its performance can gradually degrade. For instance, the patterns and behaviors associated with fraudulent activities may evolve in a fraud detection system as fraudsters adapt their techniques. If the model is not retrained or updated to capture these new patterns, it may fail to detect new types of fraud effectively. Temporal drift can lead to a decline in the model’s accuracy and reliability over time, making monitoring and addressing this type of distribution shift crucial.\nContextual changes: The ML model’s context can vary, resulting in different data distributions based on factors such as location, user behavior, or environmental conditions. Contextual changes matter because ML models are often deployed in various contexts or environments that can have different data distributions. If the model cannot generalize well to these different contexts, its performance may improve. For example, consider a computer vision model trained to recognize objects in a controlled lab environment. When deployed in a real-world setting, factors such as lighting conditions, camera angles, or background clutter can vary significantly, leading to a distribution shift. If the model is robust to these contextual changes, it may be able to accurately recognize objects in the new environment, limiting its practical utility.\nUnrepresentative training data: The training data may only partially capture the variability and diversity of the real-world data encountered during deployment. Unrepresentative training data can lead to biased or skewed models that perform poorly on real-world data. Suppose the training data needs to capture the variability and diversity of the real-world data adequately. In that case, the model may learn patterns specific to the training set but needs to generalize better to new, unseen data. This can result in poor performance, biased predictions, and limited model applicability. For instance, if a facial recognition model is trained primarily on images of individuals from a specific demographic group, it may struggle to accurately recognize faces from other demographic groups when deployed in a real-world setting. Ensuring that the training data is representative and diverse is crucial for building models that can generalize well to real-world scenarios.\n\n\n\n\n\n\nFigure 17.28: Concept drift refers to a change in data patterns and relationships over time. Source: Evidently AI\n\n\n\nDistribution shift can manifest in various forms, such as:\nCovariate shift: The distribution of the input features (covariates) changes while the conditional distribution of the target variable given the input remains the same. Covariate shift matters because it can impact the model’s ability to make accurate predictions when the input features (covariates) differ between the training and test data. Even if the relationship between the input features and the target variable remains the same, a change in the distribution of the input features can affect the model’s performance. For example, consider a model trained to predict housing prices based on features like square footage, number of bedrooms, and location. Suppose the distribution of these features in the test data significantly differs from the training data (e.g., the test data contains houses with much larger square footage). In that case, the model’s predictions may become less accurate. Addressing covariate shifts is important to ensure the model’s robustness and reliability when applied to new data.\nConcept drift: The relationship between the input features and the target variable changes over time, altering the underlying concept the model is trying to learn, as shown in Figure 17.28. Concept drift is important because it indicates changes in the fundamental relationship between the input features and the target variable over time. When the underlying concept that the model is trying to learn shifts, its performance can deteriorate if not adapted to the new concept. For instance, in a customer churn prediction model, the factors influencing customer churn may evolve due to market conditions, competitor offerings, or customer preferences. If the model is not updated to capture these changes, its predictions may become less accurate and irrelevant. Detecting and adapting to concept drift is crucial to maintaining the model’s effectiveness and alignment with evolving real-world concepts.\nDomain generalization: The model must generalize to unseen domains or distributions not present during training. Domain generalization is important because it enables ML models to be applied to new, unseen domains without requiring extensive retraining or adaptation. In real-world scenarios, training data that covers all possible domains or distributions that the model may encounter is often infeasible. Domain generalization techniques aim to learn domain-invariant features or models that can generalize well to new domains. For example, consider a model trained to classify images of animals. If the model can learn features invariant to different backgrounds, lighting conditions, or poses, it can generalize well to classify animals in new, unseen environments. Domain generalization is crucial for building models that can be deployed in diverse and evolving real-world settings.\nThe presence of a distribution shift can significantly impact the performance and reliability of ML models, as the models may need help generalizing well to the new data distribution. Detecting and adapting to distribution shifts is crucial to ensure ML systems’ robustness and practical utility in real-world scenarios.\n\n\nMechanisms of Distribution Shifts\nThe mechanisms of distribution shift, such as changes in data sources, temporal evolution, domain-specific variations, selection bias, feedback loops, and adversarial manipulations, are important to understand because they help identify the underlying causes of distribution shift. By understanding these mechanisms, practitioners can develop targeted strategies to mitigate their impact and improve the model’s robustness. Here are some common mechanisms:\n\n\n\n\n\n\nFigure 17.29: Temporal evolution. Source: Białek\n\n\n\nChanges in data sources: Distribution shifts can occur when the data sources used for training and inference differ. For example, if a model is trained on data from one sensor but deployed on data from another sensor with different characteristics, it can lead to a distribution shift.\nTemporal evolution: Over time, the underlying data distribution can evolve due to changes in user behavior, market dynamics, or other temporal factors. For instance, in a recommendation system, user preferences may shift over time, leading to a distribution shift in the input data, as shown in Figure 17.29.\nDomain-specific variations: Different domains or contexts can have distinct data distributions. A model trained on data from one domain may only generalize well to another domain with appropriate adaptation techniques. For example, an image classification model trained on indoor scenes may struggle when applied to outdoor scenes.\nSelection bias: A Distribution shift can arise from selection bias during data collection or sampling. If the training data does not represent the true population or certain subgroups are over- or underrepresented, this can lead to a mismatch between the training and test distributions.\nFeedback loops: In some cases, the predictions or actions taken by an ML model can influence future data distribution. For example, in a dynamic pricing system, the prices set by the model can impact customer behavior, leading to a shift in the data distribution over time.\nAdversarial manipulations: Adversaries can intentionally manipulate the input data to create a distribution shift and deceive the ML model. By introducing carefully crafted perturbations or generating out-of-distribution samples, attackers can exploit the model’s vulnerabilities and cause it to make incorrect predictions.\nUnderstanding the mechanisms of distribution shift is important for developing effective strategies to detect and mitigate its impact on ML systems. By identifying the sources and characteristics of the shift, practitioners can design appropriate techniques, such as domain adaptation, transfer learning, or continual learning, to improve the model’s robustness and performance under distributional changes.\n\n\nImpact on ML Systems\nDistribution shifts can significantly negatively impact the performance and reliability of ML systems. Here are some key ways in which distribution shift can affect ML models:\nDegraded predictive performance: When the data distribution encountered during inference differs from the training distribution, the model’s predictive accuracy can deteriorate. The model may need help generalizing the new data well, leading to increased errors and suboptimal performance.\nReduced reliability and trustworthiness: Distribution shift can undermine the reliability and trustworthiness of ML models. If the model’s predictions become unreliable or inconsistent due to the shift, users may lose confidence in the system’s outputs, leading to potential misuse or disuse of the model.\nBiased predictions: Distribution shift can introduce biases in the model’s predictions. If the training data does not represent the real-world distribution or certain subgroups are underrepresented, the model may make biased predictions that discriminate against certain groups or perpetuate societal biases.\nIncreased uncertainty and risk: Distribution shift introduces additional uncertainty and risk into the ML system. The model’s behavior and performance may become less predictable, making it challenging to assess its reliability and suitability for critical applications. This uncertainty can lead to increased operational risks and potential failures.\nAdaptability challenges: ML models trained on a specific data distribution may need help to adapt to changing environments or new domains. The lack of adaptability can limit the model’s usefulness and applicability in dynamic real-world scenarios where the data distribution evolves.\nMaintenance and update difficulties: Distribution shift can complicate the maintenance and updating of ML models. As the data distribution changes, the model may require frequent retraining or fine-tuning to maintain its performance. This can be time-consuming and resource-intensive, especially if the shift occurs rapidly or continuously.\nVulnerability to adversarial attacks: Distribution shift can make ML models more vulnerable to adversarial attacks. Adversaries can exploit the model’s sensitivity to distributional changes by crafting adversarial examples outside the training distribution, causing the model to make incorrect predictions or behave unexpectedly.\nTo mitigate the impact of distribution shifts, it is crucial to develop robust ML systems that detect and adapt to distributional changes. Techniques such as domain adaptation, transfer learning, and continual learning can help improve the model’s generalization ability across different distributions. ML model monitoring, testing, and updating are also necessary to ensure their performance and reliability during distribution shifts.\n\n\n\n17.4.4 Detection and Mitigation\n\nAdversarial Attacks\nAs you may recall from above, adversarial attacks pose a significant threat to the robustness and reliability of ML systems. These attacks involve crafting carefully designed inputs, known as adversarial examples, to deceive ML models and cause them to make incorrect predictions. To safeguard ML systems against adversarial attacks, developing effective techniques for detecting and mitigating these threats is crucial.\n\nAdversarial Example Detection Techniques\nDetecting adversarial examples is the first line of defense against adversarial attacks. Several techniques have been proposed to identify and flag suspicious inputs that may be adversarial.\nStatistical methods aim to detect adversarial examples by analyzing the statistical properties of the input data. These methods often compare the input data distribution to a reference distribution, such as the training data distribution or a known benign distribution. Techniques like the Kolmogorov-Smirnov (Berger and Zhou 2014) test or the Anderson-Darling test can be used to measure the discrepancy between the distributions and flag inputs that deviate significantly from the expected distribution.\n\nBerger, Vance W, and YanYan Zhou. 2014. “Kolmogorovsmirnov Test: Overview.” Wiley Statsref: Statistics Reference Online.\nKernel density estimation (KDE) is a non-parametric technique used to estimate the probability density function of a dataset. In the context of adversarial example detection, KDE can be used to estimate the density of benign examples in the input space. Adversarial examples often lie in low-density regions and can be detected by comparing their estimated density to a threshold. Inputs with an estimated density below the threshold are flagged as potential adversarial examples.\nAnother technique is feature squeezing (Panda, Chakraborty, and Roy 2019), which reduces the complexity of the input space by applying dimensionality reduction or discretization. The idea behind feature squeezing is that adversarial examples often rely on small, imperceptible perturbations that can be eliminated or reduced through these transformations. Inconsistencies can be detected by comparing the model’s predictions on the original input and the squeezed input, indicating the presence of adversarial examples.\n\nPanda, Priyadarshini, Indranil Chakraborty, and Kaushik Roy. 2019. “Discretization Based Solutions for Secure Machine Learning Against Adversarial Attacks.” #IEEE_O_ACC# 7: 70157–68. https://doi.org/10.1109/access.2019.2919463.\nModel uncertainty estimation techniques aim to quantify the confidence or uncertainty associated with a model’s predictions. Adversarial examples often exploit regions of high uncertainty in the model’s decision boundary. By estimating the uncertainty using techniques like Bayesian neural networks, dropout-based uncertainty estimation, or ensemble methods, inputs with high uncertainty can be flagged as potential adversarial examples.\n\n\nAdversarial Defense Strategies\nOnce adversarial examples are detected, various defense strategies can be employed to mitigate their impact and improve the robustness of ML models.\nAdversarial training is a technique that involves augmenting the training data with adversarial examples and retraining the model on this augmented dataset. Exposing the model to adversarial examples during training teaches it to classify them correctly and becomes more robust to adversarial attacks. Adversarial training can be performed using various attack methods, such as the Fast Gradient Sign Method (FGSM) or Projected Gradient Descent (PGD) (Madry et al. 2017).\n\nMadry, Aleksander, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017. “Towards Deep Learning Models Resistant to Adversarial Attacks.” arXiv Preprint arXiv:1706.06083.\n\nPapernot, Nicolas, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami. 2016. “Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks.” In 2016 IEEE Symposium on Security and Privacy (SP), 582–97. IEEE; IEEE. https://doi.org/10.1109/sp.2016.41.\nDefensive distillation (Papernot et al. 2016) is a technique that trains a second model (the student model) to mimic the behavior of the original model (the teacher model). The student model is trained on the soft labels produced by the teacher model, which are less sensitive to small perturbations. Using the student model for inference can reduce the impact of adversarial perturbations, as the student model learns to generalize better and is less sensitive to adversarial noise.\nInput preprocessing and transformation techniques aim to remove or mitigate the effect of adversarial perturbations before feeding the input to the ML model. These techniques include image denoising, JPEG compression, random resizing, padding, or applying random transformations to the input data. By reducing the impact of adversarial perturbations, these preprocessing steps can help improve the model’s robustness to adversarial attacks.\nEnsemble methods combine multiple models to make more robust predictions. The ensemble can reduce the impact of adversarial attacks by using a diverse set of models with different architectures, training data, or hyperparameters. Adversarial examples that fool one model may not fool others in the ensemble, leading to more reliable and robust predictions. Model diversification techniques, such as using different preprocessing techniques or feature representations for each model in the ensemble, can further enhance the robustness.\n\n\nRobustness Evaluation and Testing\nConduct thorough evaluation and testing to assess the effectiveness of adversarial defense techniques and measure the robustness of ML models.\nAdversarial robustness metrics quantify the model’s resilience to adversarial attacks. These metrics can include the model’s accuracy on adversarial examples, the average distortion required to fool the model, or the model’s performance under different attack strengths. By comparing these metrics across different models or defense techniques, practitioners can assess and compare their robustness levels.\nStandardized adversarial attack benchmarks and datasets provide a common ground for evaluating and comparing the robustness of ML models. These benchmarks include datasets with pre-generated adversarial examples and tools and frameworks for generating adversarial attacks. Examples of popular adversarial attack benchmarks include the MNIST-C, CIFAR-10-C, and ImageNet-C (Hendrycks and Dietterich 2019) datasets, which contain corrupted or perturbed versions of the original datasets.\n\nHendrycks, Dan, and Thomas Dietterich. 2019. “Benchmarking Neural Network Robustness to Common Corruptions and Perturbations.” arXiv Preprint arXiv:1903.12261.\nPractitioners can develop more robust and resilient ML systems by leveraging these adversarial example detection techniques, defense strategies, and robustness evaluation methods. However, it is important to note that adversarial robustness is an ongoing research area, and no single technique provides complete protection against all types of adversarial attacks. A comprehensive approach that combines multiple defense mechanisms and regular testing is essential to maintain the security and reliability of ML systems in the face of evolving adversarial threats.\n\n\n\nData Poisoning\nRecall that data poisoning is an attack that targets the integrity of the training data used to build ML models. By manipulating or corrupting the training data, attackers can influence the model’s behavior and cause it to make incorrect predictions or perform unintended actions. Detecting and mitigating data poisoning attacks is crucial to ensure the trustworthiness and reliability of ML systems, as shown in Figure 17.30.\n\nAnomaly Detection Techniques for Identifying Poisoned Data\n\n\n\n\n\n\nFigure 17.30: Malicious data injection. Source: Li\n\n\n\nStatistical outlier detection methods identify data points that deviate significantly from most data. These methods assume that poisoned data instances are likely to be statistical outliers. Techniques such as the Z-score method, Tukey’s method, or the [Mahalanobis] distance can be used to measure the deviation of each data point from the central tendency of the dataset. Data points that exceed a predefined threshold are flagged as potential outliers and considered suspicious for data poisoning.\nClustering-based methods group similar data points together based on their features or attributes. The assumption is that poisoned data instances may form distinct clusters or lie far away from the normal data clusters. By applying clustering algorithms like K-means, DBSCAN, or hierarchical clustering, anomalous clusters or data points that do not belong to any cluster can be identified. These anomalous instances are then treated as potentially poisoned data.\n\n\n\n\n\n\nFigure 17.31: Autoencoder. Source: Dertat\n\n\n\nAutoencoders are neural networks trained to reconstruct the input data from a compressed representation, as shown in Figure 17.31. They can be used for anomaly detection by learning the normal patterns in the data and identifying instances that deviate from them. During training, the autoencoder is trained on clean, unpoisoned data. At inference time, the reconstruction error for each data point is computed. Data points with high reconstruction errors are considered abnormal and potentially poisoned, as they do not conform to the learned normal patterns.\n\n\nData Sanitization and Preprocessing Techniques\nData poisoning can be avoided by cleaning data, which involves identifying and removing or correcting noisy, incomplete, or inconsistent data points. Techniques such as data deduplication, missing value imputation, and outlier removal can be applied to improve the quality of the training data. By eliminating or filtering out suspicious or anomalous data points, the impact of poisoned instances can be reduced.\nData validation involves verifying the integrity and consistency of the training data. This can include checking for data type consistency, range validation, and cross-field dependencies. By defining and enforcing data validation rules, anomalous or inconsistent data points indicative of data poisoning can be identified and flagged for further investigation.\nData provenance and lineage tracking involve maintaining a record of data’s origin, transformations, and movements throughout the ML pipeline. By documenting the data sources, preprocessing steps, and any modifications made to the data, practitioners can trace anomalies or suspicious patterns back to their origin. This helps identify potential points of data poisoning and facilitates the investigation and mitigation process.\n\n\nRobust Training Techniques\nRobust optimization techniques can be used to modify the training objective to minimize the impact of outliers or poisoned instances. This can be achieved by using robust loss functions less sensitive to extreme values, such as the Huber loss or the modified Huber loss. Regularization techniques, such as L1 or L2 regularization, can also help in reducing the model’s sensitivity to poisoned data by constraining the model’s complexity and preventing overfitting.\nRobust loss functions are designed to be less sensitive to outliers or noisy data points. Examples include the modified Huber loss, the Tukey loss (Beaton and Tukey 1974), and the trimmed mean loss. These loss functions down-weight or ignore the contribution of abnormal instances during training, reducing their impact on the model’s learning process. Robust objective functions, such as the minimax or distributionally robust objective, aim to optimize the model’s performance under worst-case scenarios or in the presence of adversarial perturbations.\n\nBeaton, Albert E., and John W. Tukey. 1974. “The Fitting of Power Series, Meaning Polynomials, Illustrated on Band-Spectroscopic Data.” Technometrics 16 (2): 147. https://doi.org/10.2307/1267936.\nData augmentation techniques involve generating additional training examples by applying random transformations or perturbations to the existing data Figure 17.32. This helps in increasing the diversity and robustness of the training dataset. By introducing controlled variations in the data, the model becomes less sensitive to specific patterns or artifacts that may be present in poisoned instances. Randomization techniques, such as random subsampling or bootstrap aggregating, can also help reduce the impact of poisoned data by training multiple models on different subsets of the data and combining their predictions.\n\n\n\n\n\n\nFigure 17.32: An image of the number “3” in original form and with basic augmentations applied.\n\n\n\n\n\nSecure and Trusted Data Sourcing\nImplementing the best data collection and curation practices can help mitigate the risk of data poisoning. This includes establishing clear data collection protocols, verifying the authenticity and reliability of data sources, and conducting regular data quality assessments. Sourcing data from trusted and reputable providers and following secure data handling practices can reduce the likelihood of introducing poisoned data into the training pipeline.\nStrong data governance and access control mechanisms are essential to prevent unauthorized modifications or tampering with the training data. This involves defining clear roles and responsibilities for data access, implementing access control policies based on the principle of least privilege, and monitoring and logging data access activities. By restricting access to the training data and maintaining an audit trail, potential data poisoning attempts can be detected and investigated.\nDetecting and mitigating data poisoning attacks requires a multifaceted approach that combines anomaly detection, data sanitization, robust training techniques, and secure data sourcing practices. By implementing these measures, ML practitioners can improve the resilience of their models against data poisoning and ensure the integrity and trustworthiness of the training data. However, it is important to note that data poisoning is an active area of research, and new attack vectors and defense mechanisms continue to emerge. Staying informed about the latest developments and adopting a proactive and adaptive approach to data security is crucial for maintaining the robustness of ML systems.\n\n\n\nDistribution Shifts\n\nDetecting and Mitigating Distribution Shifts\nRecall that distribution shifts occur when the data distribution encountered by a machine learning (ML) model during deployment differs from the distribution it was trained on. These shifts can significantly impact the model’s performance and generalization ability, leading to suboptimal or incorrect predictions. Detecting and mitigating distribution shifts is crucial to ensure the robustness and reliability of ML systems in real-world scenarios.\n\n\nDetection Techniques for Distribution Shifts\nStatistical tests can be used to compare the distributions of the training and test data to identify significant differences. Techniques such as the Kolmogorov-Smirnov test or the Anderson-Darling test measure the discrepancy between two distributions and provide a quantitative assessment of the presence of distribution shift. By applying these tests to the input features or the model’s predictions, practitioners can detect if there is a statistically significant difference between the training and test distributions.\nDivergence metrics quantify the dissimilarity between two probability distributions. Commonly used divergence metrics include the Kullback-Leibler (KL) divergence and the [Jensen-Shannon (JS)] divergence. By calculating the divergence between the training and test data distributions, practitioners can assess the extent of the distribution shift. High divergence values indicate a significant difference between the distributions, suggesting the presence of a distribution shift.\nUncertainty quantification techniques, such as Bayesian neural networks or ensemble methods, can estimate the uncertainty associated with the model’s predictions. When a model is applied to data from a different distribution, its predictions may have higher uncertainty. By monitoring the uncertainty levels, practitioners can detect distribution shifts. If the uncertainty consistently exceeds a predetermined threshold for test samples, it suggests that the model is operating outside its trained distribution.\nIn addition, domain classifiers are trained to distinguish between different domains or distributions. Practitioners can detect distribution shifts by training a classifier to differentiate between the training and test domains. If the domain classifier achieves high accuracy in distinguishing between the two domains, it indicates a significant difference in the underlying distributions. The performance of the domain classifier serves as a measure of the distribution shift.\n\n\nMitigation Techniques for Distribution Shifts\n\n\n\n\n\n\nFigure 17.33: Transfer learning. Source: Bhavsar\n\n\n\nTransfer learning leverages knowledge gained from one domain to improve performance in another, as shown in Figure 17.33. By using pre-trained models or transferring learned features from a source domain to a target domain, transfer learning can help mitigate the impact of distribution shifts. The pre-trained model can be fine-tuned on a small amount of labeled data from the target domain, allowing it to adapt to the new distribution. Transfer learning is particularly effective when the source and target domains share similar characteristics or when labeled data in the target domain is scarce.\nContinual learning, also known as lifelong learning, enables ML models to learn continuously from new data distributions while retaining knowledge from previous distributions. Techniques such as elastic weight consolidation (EWC) (Kirkpatrick et al. 2017) or gradient episodic memory (GEM) (Lopez-Paz and Ranzato 2017) allow models to adapt to evolving data distributions over time. These techniques aim to balance the plasticity of the model (ability to learn from new data) with the stability of the model (retaining previously learned knowledge). By incrementally updating the model with new data and mitigating catastrophic forgetting, continual learning helps models stay robust to distribution shifts.\n\nKirkpatrick, James, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, et al. 2017. “Overcoming Catastrophic Forgetting in Neural Networks.” Proc. Natl. Acad. Sci. 114 (13): 3521–26. https://doi.org/10.1073/pnas.1611835114.\n\nLopez-Paz, David, and Marc’Aurelio Ranzato. 2017. “Gradient Episodic Memory for Continual Learning.” Adv Neural Inf Process Syst 30.\nData augmentation techniques, such as those we have seen previously, involve applying transformations or perturbations to the existing training data to increase its diversity and improve the model’s robustness to distribution shifts. By introducing variations in the data, such as rotations, translations, scaling, or adding noise, data augmentation helps the model learn invariant features and generalize better to unseen distributions. Data augmentation can be performed during training and inference to improve the model’s ability to handle distribution shifts.\nEnsemble methods combine multiple models to make predictions more robust to distribution shifts. By training models on different subsets of the data, using different algorithms, or with different hyperparameters, ensemble methods can capture diverse aspects of the data distribution. When presented with a shifted distribution, the ensemble can leverage the strengths of individual models to make more accurate and stable predictions. Techniques like bagging, boosting, or stacking can create effective ensembles.\nRegularly updating models with new data from the target distribution is crucial to mitigate the impact of distribution shifts. As the data distribution evolves, models should be retrained or fine-tuned on the latest available data to adapt to the changing patterns. Monitoring model performance and data characteristics can help detect when an update is necessary. By keeping the models up to date, practitioners can ensure they remain relevant and accurate in the face of distribution shifts.\nEvaluating models using robust metrics less sensitive to distribution shifts can provide a more reliable assessment of model performance. Metrics such as the area under the precision-recall curve (AUPRC) or the F1 score are more robust to class imbalance and can better capture the model’s performance across different distributions. Additionally, using domain-specific evaluation metrics that align with the desired outcomes in the target domain can provide a more meaningful measure of the model’s effectiveness.\nDetecting and mitigating distribution shifts is an ongoing process that requires continuous monitoring, adaptation, and improvement. By employing a combination of detection techniques and mitigation strategies, ML practitioners can proactively identify and address distribution shifts, ensuring the robustness and reliability of their models in real-world deployments. It is important to note that distribution shifts can take various forms and may require domain-specific approaches depending on the nature of the data and the application. Staying informed about the latest research and best practices in handling distribution shifts is essential for building resilient ML systems.", "crumbs": [ "Advanced Topics", "17  Robust AI" @@ -1951,7 +1951,7 @@ "href": "contents/robust_ai/robust_ai.html#tools-and-frameworks", "title": "17  Robust AI", "section": "17.6 Tools and Frameworks", - "text": "17.6 Tools and Frameworks\nGiven the significance or importance of developing robust AI systems, in recent years, researchers and practitioners have developed a wide range of tools and frameworks to understand how hardware faults manifest and propagate to impact ML systems. These tools and frameworks play a crucial role in evaluating the resilience of ML systems to hardware faults by simulating various fault scenarios and analyzing their impact on the system’s performance. This enables designers to identify potential vulnerabilities and develop effective mitigation strategies, ultimately creating more robust and reliable ML systems that can operate safely despite hardware faults. This section provides an overview of widely used fault models in the literature and the tools and frameworks developed to evaluate the impact of such faults on ML systems.\n\n17.6.1 Fault Models and Error Models\nAs discussed previously, hardware faults can manifest in various ways, including transient, permanent, and intermittent faults. In addition to the type of fault under study, how the fault manifests is also important. For example, does the fault happen in a memory cell or during the computation of a functional unit? Is the impact on a single bit, or does it impact multiple bits? Does the fault propagate all the way and impact the application (causing an error), or does it get masked quickly and is considered benign? All these details impact what is known as the fault model, which plays a major role in simulating and measuring what happens to a system when a fault occurs.\nTo effectively study and understand the impact of hardware faults on ML systems, it is essential to understand the concepts of fault models and error models. A fault model describes how a hardware fault manifests itself in the system, while an error model represents how the fault propagates and affects the system’s behavior.\nFault models can be categorized based on various characteristics:\n\nDuration: Transient faults occur briefly and then disappear, while permanent faults persist indefinitely. Intermittent faults occur sporadically and may be difficult to diagnose.\nLocation: Faults can occur in hardware parts, such as memory cells, functional units, or interconnects.\nGranularity: Faults can affect a single bit (e.g., bitflip) or multiple bits (e.g., burst errors) within a hardware component.\n\nOn the other hand, error models describe how a fault propagates through the system and manifests as an error. An error may cause the system to deviate from its expected behavior, leading to incorrect results or even system failures. Error models can be defined at different levels of abstraction, from the hardware level (e.g., register-level bitflips) to the software level (e.g., corrupted weights or activations in an ML model).\nThe fault model (or error model, typically the more applicable terminology in understanding the robustness of an ML system) plays a major role in simulating and measuring what happens to a system when a fault occurs. The chosen model informs the assumptions made about the system being studied. For example, a system focusing on single-bit transient errors (Sangchoolie, Pattabiraman, and Karlsson 2017) would not be well-suited to understand the impact of permanent, multi-bit flip errors (Wilkening et al. 2014), as it is designed assuming a different model altogether.\n\nWilkening, Mark, Vilas Sridharan, Si Li, Fritz Previlon, Sudhanva Gurumurthi, and David R. Kaeli. 2014. “Calculating Architectural Vulnerability Factors for Spatial Multi-Bit Transient Faults.” In 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture, 293–305. IEEE; IEEE. https://doi.org/10.1109/micro.2014.15.\nFurthermore, implementing an error model is also an important consideration, particularly regarding where an error is said to occur in the compute stack. For instance, a single-bit flip model at the architectural register level differs from a single-bit flip in the weight of a model at the PyTorch level. Although both target a similar error model, the former would usually be modeled in an architecturally accurate simulator (like gem5 [binkert2011gem5]), which captures error propagation compared to the latter, focusing on value propagation through a model.\nRecent research has shown that certain characteristics of error models may exhibit similar behaviors across different levels of abstraction (Sangchoolie, Pattabiraman, and Karlsson 2017) (Papadimitriou and Gizopoulos 2021). For example, single-bit errors are generally more problematic than multi-bit errors, regardless of whether they are modeled at the hardware or software level. However, other characteristics, such as error masking (Mohanram and Touba 2003) as shown in Figure 17.37, may not always be accurately captured by software-level models, as they can hide underlying system effects.\n\nSangchoolie, Behrooz, Karthik Pattabiraman, and Johan Karlsson. 2017. “One Bit Is (Not) Enough: An Empirical Study of the Impact of Single and Multiple Bit-Flip Errors.” In 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 97–108. IEEE; IEEE. https://doi.org/10.1109/dsn.2017.30.\n\nPapadimitriou, George, and Dimitris Gizopoulos. 2021. “Demystifying the System Vulnerability Stack: Transient Fault Effects Across the Layers.” In 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 902–15. IEEE; IEEE. https://doi.org/10.1109/isca52012.2021.00075.\n\nMohanram, K., and N. A. Touba. 2003. “Partial Error Masking to Reduce Soft Error Failure Rate in Logic Circuits.” In Proceedings. 16th IEEE Symposium on Computer Arithmetic, 433–40. IEEE; IEEE Comput. Soc. https://doi.org/10.1109/dftvs.2003.1250141.\n\n\n\n\n\n\nFigure 17.37: Example of error masking in microarchitectural components (Ko 2021)\n\n\nKo, Yohan. 2021. “Characterizing System-Level Masking Effects Against Soft Errors.” Electronics 10 (18): 2286. https://doi.org/10.3390/electronics10182286.\n\n\nSome tools, such as Fidelity (He, Balaprakash, and Li 2020), aim to bridge the gap between hardware-level and software-level error models by mapping patterns between the two levels of abstraction (Cheng et al. 2016). This allows for more accurate modeling of hardware faults in software-based tools, essential for developing robust and reliable ML systems. Lower-level tools typically represent more accurate error propagation characteristics but must be faster in simulating many errors due to the complex nature of hardware system designs. On the other hand, higher-level tools, such as those implemented in ML frameworks like PyTorch or TensorFlow, which we will discuss soon in the later sections, are often faster and more efficient for evaluating the robustness of ML systems.\n\nCheng, Eric, Shahrzad Mirkhani, Lukasz G. Szafaryn, Chen-Yong Cher, Hyungmin Cho, Kevin Skadron, Mircea R. Stan, et al. 2016. “Clear: uC/u Ross u-l/u Ayer uE/u Xploration for uA/u Rchitecting uR/u Esilience - Combining Hardware and Software Techniques to Tolerate Soft Errors in Processor Cores.” In Proceedings of the 53rd Annual Design Automation Conference, 1–6. ACM. https://doi.org/10.1145/2897937.2897996.\nIn the following subsections, we will discuss various hardware-based and software-based fault injection methods and tools, highlighting their capabilities, limitations, and the fault and error models they support.\n\n\n17.6.2 Hardware-based Fault Injection\nAn error injection tool is a tool that allows the user to implement a particular error model, such as a transient single-bit flip during inference Figure 17.38. Most error injection tools are software-based, as software-level tools are faster for ML robustness studies. However, hardware-based fault injection methods are still important for grounding the higher-level error models, as they are considered the most accurate way to study the impact of faults on ML systems by directly manipulating the hardware to introduce faults. These methods allow researchers to observe the system’s behavior under real-world fault conditions. Both software-based and hardware-based error injection tools are described in this section in more detail.\n\n\n\n\n\n\nFigure 17.38: Hardware errors can occur due to a variety of reasons and at different times and/or locations in a system, which can be explored when studying the impact of hardware-based errors on systems (Ahmadilivani et al. 2024)\n\n\nAhmadilivani, Mohammad Hasan, Mahdi Taheri, Jaan Raik, Masoud Daneshtalab, and Maksim Jenihhin. 2024. “A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks.” ACM Comput. Surv. 56 (6): 1–39. https://doi.org/10.1145/3638242.\n\n\n\nMethods\nTwo of the most common hardware-based fault injection methods are FPGA-based fault injection and radiation or beam testing.\nFPGA-based Fault Injection: Field-Programmable Gate Arrays (FPGAs) are reconfigurable integrated circuits that can be programmed to implement various hardware designs. In the context of fault injection, FPGAs offer high precision and accuracy, as researchers can target specific bits or sets of bits within the hardware. By modifying the FPGA configuration, faults can be introduced at specific locations and times during the execution of an ML model. FPGA-based fault injection allows for fine-grained control over the fault model, enabling researchers to study the impact of different types of faults, such as single-bit flips or multi-bit errors. This level of control makes FPGA-based fault injection a valuable tool for understanding the resilience of ML systems to hardware faults.\nRadiation or Beam Testing: Radiation or beam testing (Velazco, Foucard, and Peronnard 2010) involves exposing the hardware running an ML model to high-energy particles, such as protons or neutrons as illustrated in Figure 17.39. These particles can cause bitflips or other types of faults in the hardware, mimicking the effects of real-world radiation-induced faults. Beam testing is widely regarded as a highly accurate method for measuring the error rate induced by particle strikes on a running application. It provides a realistic representation of the faults in real-world environments, particularly in applications exposed to high radiation levels, such as space systems or particle physics experiments. However, unlike FPGA-based fault injection, beam testing could be more precise in targeting specific bits or components within the hardware, as it might be difficult to aim the beam of particles to a particular bit in the hardware. Despite being quite expensive from a research standpoint, beam testing is a well-regarded industry practice for reliability.\n\nVelazco, Raoul, Gilles Foucard, and Paul Peronnard. 2010. “Combining Results of Accelerated Radiation Tests and Fault Injections to Predict the Error Rate of an Application Implemented in SRAM-Based FPGAs.” IEEE Trans. Nucl. Sci. 57 (6): 3500–3505. https://doi.org/10.1109/tns.2010.2087355.\n\n\n\n\n\n\n\nFigure 17.39: Radiation test setup for semiconductor components (Lee et al. 2022) Source: JD Instrument\n\n\nLee, Minwoong, Namho Lee, Huijeong Gwon, Jongyeol Kim, Younggwan Hwang, and Seongik Cho. 2022. “Design of Radiation-Tolerant High-Speed Signal Processing Circuit for Detecting Prompt Gamma Rays by Nuclear Explosion.” Electronics 11 (18): 2970. https://doi.org/10.3390/electronics11182970.\n\n\n\n\nLimitations\nDespite their high accuracy, hardware-based fault injection methods have several limitations that can hinder their widespread adoption:\nCost: FPGA-based fault injection and beam testing require specialized hardware and facilities, which can be expensive to set up and maintain. The cost of these methods can be a significant barrier for researchers and organizations with limited resources.\nScalability: Hardware-based methods are generally slower and less scalable than software-based methods. Injecting faults and collecting data on hardware can take time, limiting the number of experiments performed within a given timeframe. This can be particularly challenging when studying the resilience of large-scale ML systems or conducting statistical analyses that require many fault injection experiments.\nFlexibility: Hardware-based methods may not be as flexible as software-based methods in terms of the range of fault models and error models they can support. Modifying the hardware configuration or the experimental setup to accommodate different fault models can be more challenging and time-consuming than software-based methods.\nDespite these limitations, hardware-based fault injection methods remain essential tools for validating the accuracy of software-based methods and for studying the impact of faults on ML systems in realistic settings. By combining hardware-based and software-based methods, researchers can gain a more comprehensive understanding of ML systems’ resilience to hardware faults and develop effective mitigation strategies.\n\n\n\n17.6.3 Software-based Fault Injection Tools\nWith the rapid development of ML frameworks in recent years, software-based fault injection tools have gained popularity in studying the resilience of ML systems to hardware faults. These tools simulate the effects of hardware faults by modifying the software representation of the ML model or the underlying computational graph. The rise of ML frameworks such as TensorFlow, PyTorch, and Keras has facilitated the development of fault injection tools that are tightly integrated with these frameworks, making it easier for researchers to conduct fault injection experiments and analyze the results.\n\nAdvantages and Trade-offs\nSoftware-based fault injection tools offer several advantages over hardware-based methods:\nSpeed: Software-based tools are generally faster than hardware-based methods, as they do not require the modification of physical hardware or the setup of specialized equipment. This allows researchers to conduct more fault injection experiments in a shorter time, enabling more comprehensive analyses of the resilience of ML systems.\nFlexibility: Software-based tools are more flexible than hardware-based methods in terms of the range of fault and error models they can support. Researchers can easily modify the fault injection tool’s software implementation to accommodate different fault models or to target specific components of the ML system.\nAccessibility: Software-based tools are more accessible than hardware-based methods, as they do not require specialized hardware or facilities. This makes it easier for researchers and practitioners to conduct fault injection experiments and study the resilience of ML systems, even with limited resources.\n\n\nLimitations\nSoftware-based fault injection tools also have some limitations compared to hardware-based methods:\nAccuracy: Software-based tools may not always capture the full range of effects that hardware faults can have on the system. As these tools operate at a higher level of abstraction, they may need to catch up on some of the low-level hardware interactions and error propagation mechanisms that can impact the behavior of the ML system.\nFidelity: Software-based tools may provide a different level of Fidelity than hardware-based methods in terms of representing real-world fault conditions. The accuracy of the results obtained from software-based fault injection experiments may depend on how closely the software model approximates the actual hardware behavior.\n\n\n\n\n\n\nFigure 17.40: Comparison of techniques at layers of abstraction. Source: MAVFI\n\n\n\n\n\nTypes of Fault Injection Tools\nSoftware-based fault injection tools can be categorized based on their target frameworks or use cases. Here, we will discuss some of the most popular tools in each category:\nAres (Reagen et al. 2018), a fault injection tool initially developed for the Keras framework in 2018, emerged as one of the first tools to study the impact of hardware faults on deep neural networks (DNNs) in the context of the rising popularity of ML frameworks in the mid-to-late 2010s. The tool was validated against a DNN accelerator implemented in silicon, demonstrating its effectiveness in modeling hardware faults. Ares provides a comprehensive study on the impact of hardware faults in both weights and activation values, characterizing the effects of single-bit flips and bit-error rates (BER) on hardware structures. Later, the Ares framework was extended to support the PyTorch ecosystem, enabling researchers to investigate hardware faults in a more modern setting and further extending its utility in the field.\n\nReagen, Brandon, Udit Gupta, Lillian Pentecost, Paul Whatmough, Sae Kyu Lee, Niamh Mulholland, David Brooks, and Gu-Yeon Wei. 2018. “Ares: A Framework for Quantifying the Resilience of Deep Neural Networks.” In 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), 1–6. IEEE. https://doi.org/10.1109/dac.2018.8465834.\n\n\n\n\n\n\nFigure 17.41: Hardware bitflips in ML workloads can cause phantom objects and misclassifications, which can erroneously be used downstream by larger systems, such as in autonomous driving. Shown above is a correct and faulty version of the same image using the PyTorchFI injection framework.\n\n\n\nPyTorchFI (Mahmoud et al. 2020), a fault injection tool specifically designed for the PyTorch framework, was developed in 2020 in collaboration with Nvidia Research. It enables the injection of faults into the weights, activations, and gradients of PyTorch models, supporting a wide range of fault models. By leveraging the GPU acceleration capabilities of PyTorch, PyTorchFI provides a fast and efficient implementation for conducting fault injection experiments on large-scale ML systems, as shown in Figure 17.41. The tool’s speed and ease of use have led to widespread adoption in the community, resulting in multiple developer-led projects, such as PyTorchALFI by Intel xColabs, which focuses on safety in automotive environments. Follow-up PyTorch-centric tools for fault injection include Dr. DNA by Meta (Ma et al. 2024) (which further facilitates the Pythonic programming model for ease of use), and the GoldenEye framework (Mahmoud et al. 2022), which incorporates novel numerical datatypes (such as AdaptivFloat (Tambe et al. 2020) and BlockFloat in the context of hardware bit flips.\n\nMahmoud, Abdulrahman, Neeraj Aggarwal, Alex Nobbe, Jose Rodrigo Sanchez Vicarte, Sarita V. Adve, Christopher W. Fletcher, Iuri Frosio, and Siva Kumar Sastry Hari. 2020. “PyTorchFI: A Runtime Perturbation Tool for DNNs.” In 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-w), 25–31. IEEE; IEEE. https://doi.org/10.1109/dsn-w50199.2020.00014.\n\nMa, Dongning, Fred Lin, Alban Desmaison, Joel Coburn, Daniel Moore, Sriram Sankar, and Xun Jiao. 2024. “Dr. DNA: Combating Silent Data Corruptions in Deep Learning Using Distribution of Neuron Activations.” In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3, 239–52. ACM. https://doi.org/10.1145/3620666.3651349.\n\nMahmoud, Abdulrahman, Thierry Tambe, Tarek Aloui, David Brooks, and Gu-Yeon Wei. 2022. “GoldenEye: A Platform for Evaluating Emerging Numerical Data Formats in DNN Accelerators.” In 2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 206–14. IEEE. https://doi.org/10.1109/dsn53405.2022.00031.\n\nTambe, Thierry, En-Yu Yang, Zishen Wan, Yuntian Deng, Vijay Janapa Reddi, Alexander Rush, David Brooks, and Gu-Yeon Wei. 2020. “Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings for Resilient Deep Learning Inference.” In 2020 57th ACM/IEEE Design Automation Conference (DAC), 1–6. IEEE; IEEE. https://doi.org/10.1109/dac18072.2020.9218516.\n\nChen, Zitao, Niranjhana Narayanan, Bo Fang, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben. 2020. “TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications.” In 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), 426–35. IEEE; IEEE. https://doi.org/10.1109/issre5003.2020.00047.\n\nChen, Zitao, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben. 2019. “iBinFI/i: An Efficient Fault Injector for Safety-Critical Machine Learning Systems.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. SC ’19. New York, NY, USA: ACM. https://doi.org/10.1145/3295500.3356177.\nTensorFI (Chen et al. 2020), or the TensorFlow Fault Injector, is a fault injection tool developed specifically for the TensorFlow framework. Analogous to Ares and PyTorchFI, TensorFI is considered the state-of-the-art tool for ML robustness studies in the TensorFlow ecosystem. It allows researchers to inject faults into the computational graph of TensorFlow models and study their impact on the model’s performance, supporting a wide range of fault models. One of the key benefits of TensorFI is its ability to evaluate the resilience of various ML models, not just DNNs. Further advancements, such as BinFi (Chen et al. 2019), provide a mechanism to speed up error injection experiments by focusing on the “important” bits in the system, accelerating the process of ML robustness analysis and prioritizing the critical components of a model.\nNVBitFI (T. Tsai et al. 2021), a general-purpose fault injection tool developed by Nvidia for their GPU platforms, operates at a lower level compared to framework-specific tools like Ares, PyTorchFI, and TensorFlow. While these tools focus on various deep learning platforms to implement and perform robustness analysis, NVBitFI targets the underlying hardware assembly code for fault injection. This allows researchers to inject faults into any application running on Nvidia GPUs, making it a versatile tool for studying the resilience of ML systems and other GPU-accelerated applications. By enabling users to inject errors at the architectural level, NVBitFI provides a more general-purpose fault model that is not restricted to just ML models. As Nvidia’s GPU systems are commonly used in many ML-based systems, NVBitFI is a valuable tool for comprehensive fault injection analysis across various applications.\n\nTsai, Timothy, Siva Kumar Sastry Hari, Michael Sullivan, Oreste Villa, and Stephen W. Keckler. 2021. “NVBitFI: Dynamic Fault Injection for GPUs.” In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 284–91. IEEE; IEEE. https://doi.org/10.1109/dsn48987.2021.00041.\n\nDomain-specific Examples\nDomain-specific fault injection tools have been developed to address various ML application domains’ unique challenges and requirements, such as autonomous vehicles and robotics. This section highlights three domain-specific fault injection tools: DriveFI and PyTorchALFI for autonomous vehicles and MAVFI for uncrewed aerial vehicles (UAVs). These tools enable researchers to inject hardware faults into these complex systems’ perception, control, and other subsystems, allowing them to study the impact of faults on system performance and safety. The development of these software-based fault injection tools has greatly expanded the capabilities of the ML community to develop more robust and reliable systems that can operate safely and effectively in the presence of hardware faults.\nDriveFI (Jha et al. 2019) is a fault injection tool designed for autonomous vehicles. It enables the injection of hardware faults into the perception and control pipelines of autonomous vehicle systems, allowing researchers to study the impact of these faults on the system’s performance and safety. DriveFI has been integrated with industry-standard autonomous driving platforms, such as Nvidia DriveAV and Baidu Apollo, making it a valuable tool for evaluating the resilience of autonomous vehicle systems.\n\nJha, Saurabh, Subho Banerjee, Timothy Tsai, Siva K. S. Hari, Michael B. Sullivan, Zbigniew T. Kalbarczyk, Stephen W. Keckler, and Ravishankar K. Iyer. 2019. “ML-Based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection.” In 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 112–24. IEEE; IEEE. https://doi.org/10.1109/dsn.2019.00025.\n\nGräfe, Ralf, Qutub Syed Sha, Florian Geissler, and Michael Paulitsch. 2023. “Large-Scale Application of Fault Injection into PyTorch Models -an Extension to PyTorchFI for Validation Efficiency.” In 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-s), 56–62. IEEE; IEEE. https://doi.org/10.1109/dsn-s58398.2023.00025.\nPyTorchALFI (Gräfe et al. 2023) is an extension of PyTorchFI developed by Intel xColabs for the autonomous vehicle domain. It builds upon PyTorchFI’s fault injection capabilities. It adds features specifically tailored for evaluating the resilience of autonomous vehicle systems, such as the ability to inject faults into the camera and LiDAR sensor data.\nMAVFI (Hsiao et al. 2023) is a fault injection tool designed for the robotics domain, specifically for uncrewed aerial vehicles (UAVs). MAVFI is built on top of the Robot Operating System (ROS) framework and allows researchers to inject faults into the various components of a UAV system, such as sensors, actuators, and control algorithms. By evaluating the impact of these faults on the UAV’s performance and stability, researchers can develop more resilient and fault-tolerant UAV systems.\n\nHsiao, Yu-Shun, Zishen Wan, Tianyu Jia, Radhika Ghosal, Abdulrahman Mahmoud, Arijit Raychowdhury, David Brooks, Gu-Yeon Wei, and Vijay Janapa Reddi. 2023. “MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles.” In 2023 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 1–6. IEEE; IEEE. https://doi.org/10.23919/date56975.2023.10137246.\nThe development of software-based fault injection tools has greatly expanded the capabilities of researchers and practitioners to study the resilience of ML systems to hardware faults. By leveraging the speed, flexibility, and accessibility of these tools, the ML community can develop more robust and reliable systems that can operate safely and effectively in the presence of hardware faults.\n\n\n\n\n17.6.4 Bridging the Gap between Hardware and Software Error Models\nWhile software-based fault injection tools offer many advantages in speed, flexibility, and accessibility, they may not always accurately capture the full range of effects that hardware faults can have on the system. This is because software-based tools operate at a higher level of abstraction than hardware-based methods and may miss some of the low-level hardware interactions and error propagation mechanisms that can impact the behavior of the ML system.\nAs Bolchini et al. (2023) illustrates in their work, hardware errors can manifest in complex spatial distribution patterns that are challenging to fully replicate with software-based fault injection alone. They identify four distinct patterns: (a) single point, where the fault corrupts a single value in a feature map; (b) same row, where the fault corrupts a partial or entire row in a single feature map; (c) bullet wake, where the fault corrupts the same location across multiple feature maps; and (d) shatter glass, which combines the effects of same row and bullet wake patterns, as shown in Figure 17.42. These intricate error propagation mechanisms highlight the need for hardware-aware fault injection techniques to accurately assess the resilience of ML systems.\n\n\n\n\n\n\nFigure 17.42: Hardware errors may manifest themselves in different ways at the software level, as classified by Bolchini et al. (Bolchini et al. 2023)\n\n\nBolchini, Cristiana, Luca Cassano, Antonio Miele, and Alessandro Toschi. 2023. “Fast and Accurate Error Simulation for CNNs Against Soft Errors.” IEEE Trans. Comput. 72 (4): 984–97. https://doi.org/10.1109/tc.2022.3184274.\n\n\nResearchers have developed tools to address this issue by bridging the gap between low-level hardware error models and higher-level software error models. One such tool is Fidelity, designed to map patterns between hardware-level faults and their software-level manifestations.\n\nFidelity: Bridging the Gap\nFidelity (He, Balaprakash, and Li 2020) is a tool for accurately modeling hardware faults in software-based fault injection experiments. It achieves this by carefully studying the relationship between hardware-level faults and their impact on the software representation of the ML system.\n\nHe, Yi, Prasanna Balaprakash, and Yanjing Li. 2020. “FIdelity: Efficient Resilience Analysis Framework for Deep Learning Accelerators.” In 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 270–81. IEEE; IEEE. https://doi.org/10.1109/micro50266.2020.00033.\nThe key insights behind Fidelity are:\n\nFault Propagation: Fidelity models how faults propagate through the hardware and manifest as errors in the software-visible state of the system. By understanding these propagation patterns, Fidelity can more accurately simulate the effects of hardware faults in software-based experiments.\nFault Equivalence: Fidelity identifies equivalent classes of hardware faults that produce similar software-level errors. This allows researchers to design software-based fault models that are representative of the underlying hardware faults without the need to model every possible hardware fault individually.\nLayered Approach: Fidelity employs a layered approach to fault modeling, where the effects of hardware faults are propagated through multiple levels of abstraction, from the hardware to the software level. This approach ensures that the software-based fault models are grounded in the actual behavior of the hardware.\n\nBy incorporating these insights, Fidelity enables software-based fault injection tools to capture the effects of hardware faults on ML systems accurately. This is particularly important for safety-critical applications, where the system’s resilience to hardware faults is paramount.\n\n\nImportance of Capturing True Hardware Behavior\nCapturing true hardware behavior in software-based fault injection tools is crucial for several reasons:\n\nAccuracy: By accurately modeling the effects of hardware faults, software-based tools can provide more reliable insights into the resilience of ML systems. This is essential for designing and validating fault-tolerant systems that can operate safely and effectively in the presence of hardware faults.\nReproducibility: When software-based tools accurately capture hardware behavior, fault injection experiments become more reproducible across different platforms and environments. This is important for the scientific study of ML system resilience, as it allows researchers to compare and validate results across different studies and implementations.\nEfficiency: Software-based tools that capture true hardware behavior can be more efficient in their fault injection experiments by focusing on the most representative and impactful fault models. This allows researchers to cover a wider range of fault scenarios and system configurations with limited computational resources.\nMitigation Strategies: Understanding how hardware faults manifest at the software level is crucial for developing effective mitigation strategies. By accurately capturing hardware behavior, software-based fault injection tools can help researchers identify the most vulnerable components of the ML system and design targeted hardening techniques to improve resilience.\n\nTools like Fidelity are vital in advancing the state-of-the-art in ML system resilience research. These tools enable researchers to conduct more accurate, reproducible, and efficient fault injection experiments by bridging the gap between hardware and software error models. As the complexity and criticality of ML systems continue to grow, the importance of capturing true hardware behavior in software-based fault injection tools will only become more apparent.\nOngoing research in this area aims to refine the mapping between hardware and software error models and develop new techniques for efficiently simulating hardware faults in software-based experiments. As these tools mature, they will provide the ML community with increasingly powerful and accessible means to study and improve the resilience of ML systems to hardware faults.", + "text": "17.6 Tools and Frameworks\nGiven the significance or importance of developing robust AI systems, in recent years, researchers and practitioners have developed a wide range of tools and frameworks to understand how hardware faults manifest and propagate to impact ML systems. These tools and frameworks play a crucial role in evaluating the resilience of ML systems to hardware faults by simulating various fault scenarios and analyzing their impact on the system’s performance. This enables designers to identify potential vulnerabilities and develop effective mitigation strategies, ultimately creating more robust and reliable ML systems that can operate safely despite hardware faults. This section provides an overview of widely used fault models in the literature and the tools and frameworks developed to evaluate the impact of such faults on ML systems.\n\n17.6.1 Fault Models and Error Models\nAs discussed previously, hardware faults can manifest in various ways, including transient, permanent, and intermittent faults. In addition to the type of fault under study, how the fault manifests is also important. For example, does the fault happen in a memory cell or during the computation of a functional unit? Is the impact on a single bit, or does it impact multiple bits? Does the fault propagate all the way and impact the application (causing an error), or does it get masked quickly and is considered benign? All these details impact what is known as the fault model, which plays a major role in simulating and measuring what happens to a system when a fault occurs.\nTo effectively study and understand the impact of hardware faults on ML systems, it is essential to understand the concepts of fault models and error models. A fault model describes how a hardware fault manifests itself in the system, while an error model represents how the fault propagates and affects the system’s behavior.\nFault models can be categorized based on various characteristics:\n\nDuration: Transient faults occur briefly and then disappear, while permanent faults persist indefinitely. Intermittent faults occur sporadically and may be difficult to diagnose.\nLocation: Faults can occur in hardware parts, such as memory cells, functional units, or interconnects.\nGranularity: Faults can affect a single bit (e.g., bitflip) or multiple bits (e.g., burst errors) within a hardware component.\n\nOn the other hand, error models describe how a fault propagates through the system and manifests as an error. An error may cause the system to deviate from its expected behavior, leading to incorrect results or even system failures. Error models can be defined at different levels of abstraction, from the hardware level (e.g., register-level bitflips) to the software level (e.g., corrupted weights or activations in an ML model).\nThe fault model (or error model, typically the more applicable terminology in understanding the robustness of an ML system) plays a major role in simulating and measuring what happens to a system when a fault occurs. The chosen model informs the assumptions made about the system being studied. For example, a system focusing on single-bit transient errors (Sangchoolie, Pattabiraman, and Karlsson 2017) would not be well-suited to understand the impact of permanent, multi-bit flip errors (Wilkening et al. 2014), as it is designed assuming a different model altogether.\n\nWilkening, Mark, Vilas Sridharan, Si Li, Fritz Previlon, Sudhanva Gurumurthi, and David R. Kaeli. 2014. “Calculating Architectural Vulnerability Factors for Spatial Multi-Bit Transient Faults.” In 2014 47th Annual IEEE/ACM International Symposium on Microarchitecture, 293–305. IEEE; IEEE. https://doi.org/10.1109/micro.2014.15.\nFurthermore, implementing an error model is also an important consideration, particularly regarding where an error is said to occur in the compute stack. For instance, a single-bit flip model at the architectural register level differs from a single-bit flip in the weight of a model at the PyTorch level. Although both target a similar error model, the former would usually be modeled in an architecturally accurate simulator (like gem5 [binkert2011gem5]), which captures error propagation compared to the latter, focusing on value propagation through a model.\nRecent research has shown that certain characteristics of error models may exhibit similar behaviors across different levels of abstraction (Sangchoolie, Pattabiraman, and Karlsson 2017) (Papadimitriou and Gizopoulos 2021). For example, single-bit errors are generally more problematic than multi-bit errors, regardless of whether they are modeled at the hardware or software level. However, other characteristics, such as error masking (Mohanram and Touba 2003) as shown in Figure 17.37, may not always be accurately captured by software-level models, as they can hide underlying system effects.\n\nSangchoolie, Behrooz, Karthik Pattabiraman, and Johan Karlsson. 2017. “One Bit Is (Not) Enough: An Empirical Study of the Impact of Single and Multiple Bit-Flip Errors.” In 2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 97–108. IEEE; IEEE. https://doi.org/10.1109/dsn.2017.30.\n\nPapadimitriou, George, and Dimitris Gizopoulos. 2021. “Demystifying the System Vulnerability Stack: Transient Fault Effects Across the Layers.” In 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA), 902–15. IEEE; IEEE. https://doi.org/10.1109/isca52012.2021.00075.\n\nMohanram, K., and N. A. Touba. 2003. “Partial Error Masking to Reduce Soft Error Failure Rate in Logic Circuits.” In Proceedings. 16th IEEE Symposium on Computer Arithmetic, 433–40. IEEE; IEEE Comput. Soc. https://doi.org/10.1109/dftvs.2003.1250141.\n\n\n\n\n\n\nFigure 17.37: Example of error masking in microarchitectural components (Ko 2021)\n\n\nKo, Yohan. 2021. “Characterizing System-Level Masking Effects Against Soft Errors.” Electronics 10 (18): 2286. https://doi.org/10.3390/electronics10182286.\n\n\nSome tools, such as Fidelity (He, Balaprakash, and Li 2020), aim to bridge the gap between hardware-level and software-level error models by mapping patterns between the two levels of abstraction (Cheng et al. 2016). This allows for more accurate modeling of hardware faults in software-based tools, essential for developing robust and reliable ML systems. Lower-level tools typically represent more accurate error propagation characteristics but must be faster in simulating many errors due to the complex nature of hardware system designs. On the other hand, higher-level tools, such as those implemented in ML frameworks like PyTorch or TensorFlow, which we will discuss soon in the later sections, are often faster and more efficient for evaluating the robustness of ML systems.\n\nCheng, Eric, Shahrzad Mirkhani, Lukasz G. Szafaryn, Chen-Yong Cher, Hyungmin Cho, Kevin Skadron, Mircea R. Stan, et al. 2016. “Clear: uC/u Ross u-l/u Ayer uE/u Xploration for uA/u Rchitecting uR/u Esilience - Combining Hardware and Software Techniques to Tolerate Soft Errors in Processor Cores.” In Proceedings of the 53rd Annual Design Automation Conference, 1–6. ACM. https://doi.org/10.1145/2897937.2897996.\nIn the following subsections, we will discuss various hardware-based and software-based fault injection methods and tools, highlighting their capabilities, limitations, and the fault and error models they support.\n\n\n17.6.2 Hardware-based Fault Injection\nAn error injection tool is a tool that allows the user to implement a particular error model, such as a transient single-bit flip during inference Figure 17.38. Most error injection tools are software-based, as software-level tools are faster for ML robustness studies. However, hardware-based fault injection methods are still important for grounding the higher-level error models, as they are considered the most accurate way to study the impact of faults on ML systems by directly manipulating the hardware to introduce faults. These methods allow researchers to observe the system’s behavior under real-world fault conditions. Both software-based and hardware-based error injection tools are described in this section in more detail.\n\n\n\n\n\n\nFigure 17.38: Hardware errors can occur due to a variety of reasons and at different times and/or locations in a system, which can be explored when studying the impact of hardware-based errors on systems (Ahmadilivani et al. 2024)\n\n\nAhmadilivani, Mohammad Hasan, Mahdi Taheri, Jaan Raik, Masoud Daneshtalab, and Maksim Jenihhin. 2024. “A Systematic Literature Review on Hardware Reliability Assessment Methods for Deep Neural Networks.” ACM Comput. Surv. 56 (6): 1–39. https://doi.org/10.1145/3638242.\n\n\n\nMethods\nTwo of the most common hardware-based fault injection methods are FPGA-based fault injection and radiation or beam testing.\nFPGA-based Fault Injection: Field-Programmable Gate Arrays (FPGAs) are reconfigurable integrated circuits that can be programmed to implement various hardware designs. In the context of fault injection, FPGAs offer high precision and accuracy, as researchers can target specific bits or sets of bits within the hardware. By modifying the FPGA configuration, faults can be introduced at specific locations and times during the execution of an ML model. FPGA-based fault injection allows for fine-grained control over the fault model, enabling researchers to study the impact of different types of faults, such as single-bit flips or multi-bit errors. This level of control makes FPGA-based fault injection a valuable tool for understanding the resilience of ML systems to hardware faults.\nRadiation or Beam Testing: Radiation or beam testing (Velazco, Foucard, and Peronnard 2010) involves exposing the hardware running an ML model to high-energy particles, such as protons or neutrons as illustrated in Figure 17.39. These particles can cause bitflips or other types of faults in the hardware, mimicking the effects of real-world radiation-induced faults. Beam testing is widely regarded as a highly accurate method for measuring the error rate induced by particle strikes on a running application. It provides a realistic representation of the faults in real-world environments, particularly in applications exposed to high radiation levels, such as space systems or particle physics experiments. However, unlike FPGA-based fault injection, beam testing could be more precise in targeting specific bits or components within the hardware, as it might be difficult to aim the beam of particles to a particular bit in the hardware. Despite being quite expensive from a research standpoint, beam testing is a well-regarded industry practice for reliability.\n\nVelazco, Raoul, Gilles Foucard, and Paul Peronnard. 2010. “Combining Results of Accelerated Radiation Tests and Fault Injections to Predict the Error Rate of an Application Implemented in SRAM-Based FPGAs.” IEEE Trans. Nucl. Sci. 57 (6): 3500–3505. https://doi.org/10.1109/tns.2010.2087355.\n\n\n\n\n\n\n\nFigure 17.39: Radiation test setup for semiconductor components (Lee et al. 2022) Source: JD Instrument\n\n\nLee, Minwoong, Namho Lee, Huijeong Gwon, Jongyeol Kim, Younggwan Hwang, and Seongik Cho. 2022. “Design of Radiation-Tolerant High-Speed Signal Processing Circuit for Detecting Prompt Gamma Rays by Nuclear Explosion.” Electronics 11 (18): 2970. https://doi.org/10.3390/electronics11182970.\n\n\n\n\nLimitations\nDespite their high accuracy, hardware-based fault injection methods have several limitations that can hinder their widespread adoption:\nCost: FPGA-based fault injection and beam testing require specialized hardware and facilities, which can be expensive to set up and maintain. The cost of these methods can be a significant barrier for researchers and organizations with limited resources.\nScalability: Hardware-based methods are generally slower and less scalable than software-based methods. Injecting faults and collecting data on hardware can take time, limiting the number of experiments performed within a given timeframe. This can be particularly challenging when studying the resilience of large-scale ML systems or conducting statistical analyses that require many fault injection experiments.\nFlexibility: Hardware-based methods may not be as flexible as software-based methods in terms of the range of fault models and error models they can support. Modifying the hardware configuration or the experimental setup to accommodate different fault models can be more challenging and time-consuming than software-based methods.\nDespite these limitations, hardware-based fault injection methods remain essential tools for validating the accuracy of software-based methods and for studying the impact of faults on ML systems in realistic settings. By combining hardware-based and software-based methods, researchers can gain a more comprehensive understanding of ML systems’ resilience to hardware faults and develop effective mitigation strategies.\n\n\n\n17.6.3 Software-based Fault Injection Tools\nWith the rapid development of ML frameworks in recent years, software-based fault injection tools have gained popularity in studying the resilience of ML systems to hardware faults. These tools simulate the effects of hardware faults by modifying the software representation of the ML model or the underlying computational graph. The rise of ML frameworks such as TensorFlow, PyTorch, and Keras has facilitated the development of fault injection tools that are tightly integrated with these frameworks, making it easier for researchers to conduct fault injection experiments and analyze the results.\n\nAdvantages and Trade-offs\nSoftware-based fault injection tools offer several advantages over hardware-based methods:\nSpeed: Software-based tools are generally faster than hardware-based methods, as they do not require the modification of physical hardware or the setup of specialized equipment. This allows researchers to conduct more fault injection experiments in a shorter time, enabling more comprehensive analyses of the resilience of ML systems.\nFlexibility: Software-based tools are more flexible than hardware-based methods in terms of the range of fault and error models they can support. Researchers can easily modify the fault injection tool’s software implementation to accommodate different fault models or to target specific components of the ML system.\nAccessibility: Software-based tools are more accessible than hardware-based methods, as they do not require specialized hardware or facilities. This makes it easier for researchers and practitioners to conduct fault injection experiments and study the resilience of ML systems, even with limited resources.\n\n\nLimitations\nSoftware-based fault injection tools also have some limitations compared to hardware-based methods:\nAccuracy: Software-based tools may not always capture the full range of effects that hardware faults can have on the system. As these tools operate at a higher level of abstraction, they may need to catch up on some of the low-level hardware interactions and error propagation mechanisms that can impact the behavior of the ML system.\nFidelity: Software-based tools may provide a different level of Fidelity than hardware-based methods in terms of representing real-world fault conditions. The accuracy of the results obtained from software-based fault injection experiments may depend on how closely the software model approximates the actual hardware behavior.\n\n\n\n\n\n\nFigure 17.40: Comparison of techniques at layers of abstraction. Source: MAVFI\n\n\n\n\n\nTypes of Fault Injection Tools\nSoftware-based fault injection tools can be categorized based on their target frameworks or use cases. Here, we will discuss some of the most popular tools in each category:\nAres (Reagen et al. 2018), a fault injection tool initially developed for the Keras framework in 2018, emerged as one of the first tools to study the impact of hardware faults on deep neural networks (DNNs) in the context of the rising popularity of ML frameworks in the mid-to-late 2010s. The tool was validated against a DNN accelerator implemented in silicon, demonstrating its effectiveness in modeling hardware faults. Ares provides a comprehensive study on the impact of hardware faults in both weights and activation values, characterizing the effects of single-bit flips and bit-error rates (BER) on hardware structures. Later, the Ares framework was extended to support the PyTorch ecosystem, enabling researchers to investigate hardware faults in a more modern setting and further extending its utility in the field.\n\nReagen, Brandon, Udit Gupta, Lillian Pentecost, Paul Whatmough, Sae Kyu Lee, Niamh Mulholland, David Brooks, and Gu-Yeon Wei. 2018. “Ares: A Framework for Quantifying the Resilience of Deep Neural Networks.” In 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC), 1–6. IEEE. https://doi.org/10.1109/dac.2018.8465834.\n\n\n\n\n\n\nFigure 17.41: Hardware bitflips in ML workloads can cause phantom objects and misclassifications, which can erroneously be used downstream by larger systems, such as in autonomous driving. Shown above is a correct and faulty version of the same image using the PyTorchFI injection framework.\n\n\n\nPyTorchFI (Mahmoud et al. 2020), a fault injection tool specifically designed for the PyTorch framework, was developed in 2020 in collaboration with Nvidia Research. It enables the injection of faults into the weights, activations, and gradients of PyTorch models, supporting a wide range of fault models. By leveraging the GPU acceleration capabilities of PyTorch, PyTorchFI provides a fast and efficient implementation for conducting fault injection experiments on large-scale ML systems, as shown in Figure 17.41. The tool’s speed and ease of use have led to widespread adoption in the community, resulting in multiple developer-led projects, such as PyTorchALFI by Intel xColabs, which focuses on safety in automotive environments. Follow-up PyTorch-centric tools for fault injection include Dr. DNA by Meta (Ma et al. 2024) (which further facilitates the Pythonic programming model for ease of use), and the GoldenEye framework (Mahmoud et al. 2022), which incorporates novel numerical datatypes (such as AdaptivFloat (Tambe et al. 2020) and BlockFloat in the context of hardware bit flips.\n\nMahmoud, Abdulrahman, Neeraj Aggarwal, Alex Nobbe, Jose Rodrigo Sanchez Vicarte, Sarita V. Adve, Christopher W. Fletcher, Iuri Frosio, and Siva Kumar Sastry Hari. 2020. “PyTorchFI: A Runtime Perturbation Tool for DNNs.” In 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-w), 25–31. IEEE; IEEE. https://doi.org/10.1109/dsn-w50199.2020.00014.\n\nMa, Dongning, Fred Lin, Alban Desmaison, Joel Coburn, Daniel Moore, Sriram Sankar, and Xun Jiao. 2024. “Dr. DNA: Combating Silent Data Corruptions in Deep Learning Using Distribution of Neuron Activations.” In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3, 239–52. ACM. https://doi.org/10.1145/3620666.3651349.\n\nMahmoud, Abdulrahman, Thierry Tambe, Tarek Aloui, David Brooks, and Gu-Yeon Wei. 2022. “GoldenEye: A Platform for Evaluating Emerging Numerical Data Formats in DNN Accelerators.” In 2022 52nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 206–14. IEEE. https://doi.org/10.1109/dsn53405.2022.00031.\n\nTambe, Thierry, En-Yu Yang, Zishen Wan, Yuntian Deng, Vijay Janapa Reddi, Alexander Rush, David Brooks, and Gu-Yeon Wei. 2020. “Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings for Resilient Deep Learning Inference.” In 2020 57th ACM/IEEE Design Automation Conference (DAC), 1–6. IEEE; IEEE. https://doi.org/10.1109/dac18072.2020.9218516.\n\nChen, Zitao, Niranjhana Narayanan, Bo Fang, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben. 2020. “TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications.” In 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE), 426–35. IEEE; IEEE. https://doi.org/10.1109/issre5003.2020.00047.\n\nChen, Zitao, Guanpeng Li, Karthik Pattabiraman, and Nathan DeBardeleben. 2019. “iBinFI/i: An Efficient Fault Injector for Safety-Critical Machine Learning Systems.” In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. SC ’19. New York, NY, USA: ACM. https://doi.org/10.1145/3295500.3356177.\nTensorFI (Chen et al. 2020), or the TensorFlow Fault Injector, is a fault injection tool developed specifically for the TensorFlow framework. Analogous to Ares and PyTorchFI, TensorFI is considered the state-of-the-art tool for ML robustness studies in the TensorFlow ecosystem. It allows researchers to inject faults into the computational graph of TensorFlow models and study their impact on the model’s performance, supporting a wide range of fault models. One of the key benefits of TensorFI is its ability to evaluate the resilience of various ML models, not just DNNs. Further advancements, such as BinFi (Chen et al. 2019), provide a mechanism to speed up error injection experiments by focusing on the “important” bits in the system, accelerating the process of ML robustness analysis and prioritizing the critical components of a model.\nNVBitFI (T. Tsai et al. 2021), a general-purpose fault injection tool developed by Nvidia for their GPU platforms, operates at a lower level compared to framework-specific tools like Ares, PyTorchFI, and TensorFlow. While these tools focus on various deep learning platforms to implement and perform robustness analysis, NVBitFI targets the underlying hardware assembly code for fault injection. This allows researchers to inject faults into any application running on Nvidia GPUs, making it a versatile tool for studying the resilience of ML systems and other GPU-accelerated applications. By enabling users to inject errors at the architectural level, NVBitFI provides a more general-purpose fault model that is not restricted to just ML models. As Nvidia’s GPU systems are commonly used in many ML-based systems, NVBitFI is a valuable tool for comprehensive fault injection analysis across various applications.\n\nTsai, Timothy, Siva Kumar Sastry Hari, Michael Sullivan, Oreste Villa, and Stephen W. Keckler. 2021. “NVBitFI: Dynamic Fault Injection for GPUs.” In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 284–91. IEEE; IEEE. https://doi.org/10.1109/dsn48987.2021.00041.\n\nDomain-specific Examples\nDomain-specific fault injection tools have been developed to address various ML application domains’ unique challenges and requirements, such as autonomous vehicles and robotics. This section highlights three domain-specific fault injection tools: DriveFI and PyTorchALFI for autonomous vehicles and MAVFI for uncrewed aerial vehicles (UAVs). These tools enable researchers to inject hardware faults into these complex systems’ perception, control, and other subsystems, allowing them to study the impact of faults on system performance and safety. The development of these software-based fault injection tools has greatly expanded the capabilities of the ML community to develop more robust and reliable systems that can operate safely and effectively in the presence of hardware faults.\nDriveFI (Jha et al. 2019) is a fault injection tool designed for autonomous vehicles. It enables the injection of hardware faults into the perception and control pipelines of autonomous vehicle systems, allowing researchers to study the impact of these faults on the system’s performance and safety. DriveFI has been integrated with industry-standard autonomous driving platforms, such as Nvidia DriveAV and Baidu Apollo, making it a valuable tool for evaluating the resilience of autonomous vehicle systems.\n\nJha, Saurabh, Subho Banerjee, Timothy Tsai, Siva K. S. Hari, Michael B. Sullivan, Zbigniew T. Kalbarczyk, Stephen W. Keckler, and Ravishankar K. Iyer. 2019. “ML-Based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection.” In 2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), 112–24. IEEE; IEEE. https://doi.org/10.1109/dsn.2019.00025.\n\nGräfe, Ralf, Qutub Syed Sha, Florian Geissler, and Michael Paulitsch. 2023. “Large-Scale Application of Fault Injection into PyTorch Models -an Extension to PyTorchFI for Validation Efficiency.” In 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-s), 56–62. IEEE; IEEE. https://doi.org/10.1109/dsn-s58398.2023.00025.\nPyTorchALFI (Gräfe et al. 2023) is an extension of PyTorchFI developed by Intel xColabs for the autonomous vehicle domain. It builds upon PyTorchFI’s fault injection capabilities. It adds features specifically tailored for evaluating the resilience of autonomous vehicle systems, such as the ability to inject faults into the camera and LiDAR sensor data.\nMAVFI (Hsiao et al. 2023) is a fault injection tool designed for the robotics domain, specifically for uncrewed aerial vehicles (UAVs). MAVFI is built on top of the Robot Operating System (ROS) framework and allows researchers to inject faults into the various components of a UAV system, such as sensors, actuators, and control algorithms. By evaluating the impact of these faults on the UAV’s performance and stability, researchers can develop more resilient and fault-tolerant UAV systems.\n\nHsiao, Yu-Shun, Zishen Wan, Tianyu Jia, Radhika Ghosal, Abdulrahman Mahmoud, Arijit Raychowdhury, David Brooks, Gu-Yeon Wei, and Vijay Janapa Reddi. 2023. “MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and Recovery for Micro Aerial Vehicles.” In 2023 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 1–6. IEEE; IEEE. https://doi.org/10.23919/date56975.2023.10137246.\nThe development of software-based fault injection tools has greatly expanded the capabilities of researchers and practitioners to study the resilience of ML systems to hardware faults. By leveraging the speed, flexibility, and accessibility of these tools, the ML community can develop more robust and reliable systems that can operate safely and effectively in the presence of hardware faults.\n\n\n\n\n17.6.4 Bridging the Gap between Hardware and Software Error Models\nWhile software-based fault injection tools offer many advantages in speed, flexibility, and accessibility, they may not always accurately capture the full range of effects that hardware faults can have on the system. This is because software-based tools operate at a higher level of abstraction than hardware-based methods and may miss some of the low-level hardware interactions and error propagation mechanisms that can impact the behavior of the ML system.\nAs Bolchini et al. (2023) illustrates in their work, hardware errors can manifest in complex spatial distribution patterns that are challenging to fully replicate with software-based fault injection alone. They identify four distinct patterns: (a) single point, where the fault corrupts a single value in a feature map; (b) same row, where the fault corrupts a partial or entire row in a single feature map; (c) bullet wake, where the fault corrupts the same location across multiple feature maps; and (d) shatter glass, which combines the effects of same row and bullet wake patterns, as shown in Figure 17.42. These intricate error propagation mechanisms highlight the need for hardware-aware fault injection techniques to accurately assess the resilience of ML systems.\n\n\n\n\n\n\nFigure 17.42: Hardware errors may manifest themselves in different ways at the software level, as classified by Bolchini et al. (Bolchini et al. 2023)\n\n\nBolchini, Cristiana, Luca Cassano, Antonio Miele, and Alessandro Toschi. 2023. “Fast and Accurate Error Simulation for CNNs Against Soft Errors.” IEEE Trans. Comput. 72 (4): 984–97. https://doi.org/10.1109/tc.2022.3184274.\n\n\nResearchers have developed tools to address this issue by bridging the gap between low-level hardware error models and higher-level software error models. One such tool is Fidelity, designed to map patterns between hardware-level faults and their software-level manifestations.\n\nFidelity: Bridging the Gap\nFidelity (He, Balaprakash, and Li 2020) is a tool for accurately modeling hardware faults in software-based fault injection experiments. It achieves this by carefully studying the relationship between hardware-level faults and their impact on the software representation of the ML system.\n\nHe, Yi, Prasanna Balaprakash, and Yanjing Li. 2020. “FIdelity: Efficient Resilience Analysis Framework for Deep Learning Accelerators.” In 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), 270–81. IEEE; IEEE. https://doi.org/10.1109/micro50266.2020.00033.\nThe key insights behind Fidelity are:\n\nFault Propagation: Fidelity models how faults propagate through the hardware and manifest as errors in the software-visible state of the system. By understanding these propagation patterns, Fidelity can more accurately simulate the effects of hardware faults in software-based experiments.\nFault Equivalence: Fidelity identifies equivalent classes of hardware faults that produce similar software-level errors. This allows researchers to design software-based fault models that are representative of the underlying hardware faults without the need to model every possible hardware fault individually.\nLayered Approach: Fidelity employs a layered approach to fault modeling, where the effects of hardware faults are propagated through multiple levels of abstraction, from the hardware to the software level. This approach ensures that the software-based fault models are grounded in the actual behavior of the hardware.\n\nBy incorporating these insights, Fidelity enables software-based fault injection tools to capture the effects of hardware faults on ML systems accurately. This is particularly important for safety-critical applications, where the system’s resilience to hardware faults is paramount.\n\n\nImportance of Capturing True Hardware Behavior\nCapturing true hardware behavior in software-based fault injection tools is crucial for several reasons:\n\nAccuracy: By accurately modeling the effects of hardware faults, software-based tools can provide more reliable insights into the resilience of ML systems. This is essential for designing and validating fault-tolerant systems that can operate safely and effectively in the presence of hardware faults.\nReproducibility: When software-based tools accurately capture hardware behavior, fault injection experiments become more reproducible across different platforms and environments. This is important for the scientific study of ML system resilience, as it allows researchers to compare and validate results across different studies and implementations.\nEfficiency: Software-based tools that capture true hardware behavior can be more efficient in their fault injection experiments by focusing on the most representative and impactful fault models. This allows researchers to cover a wider range of fault scenarios and system configurations with limited computational resources.\nMitigation Strategies: Understanding how hardware faults manifest at the software level is crucial for developing effective mitigation strategies. By accurately capturing hardware behavior, software-based fault injection tools can help researchers identify the most vulnerable components of the ML system and design targeted hardening techniques to improve resilience.\n\nTools like Fidelity are vital in advancing the state-of-the-art in ML system resilience research. These tools enable researchers to conduct more accurate, reproducible, and efficient fault injection experiments by bridging the gap between hardware and software error models. As the complexity and criticality of ML systems continue to grow, the importance of capturing true hardware behavior in software-based fault injection tools will only become more apparent.\nOngoing research in this area seeks to refine the mapping between hardware and software error models and develop new techniques for efficiently simulating hardware faults in software-based experiments. As these tools mature, they will provide the ML community with increasingly powerful and accessible means to study and improve the resilience of ML systems to hardware faults.", "crumbs": [ "Advanced Topics", "17  Robust AI" @@ -2017,7 +2017,7 @@ "href": "contents/ai_for_good/ai_for_good.html#agriculture", "title": "19  AI for Good", "section": "19.2 Agriculture", - "text": "19.2 Agriculture\nAgriculture is essential to achieving many of the UN Sustainable Development Goals, including eradicating Hunger and malnutrition, promoting economic growth, and using natural resources sustainably. TinyML can be a valuable tool to help advance sustainable agriculture, especially for smallholder farmers in developing regions.\nTinyML solutions can provide real-time monitoring and data analytics for crop health and growing conditions - all without reliance on connectivity infrastructure. For example, low-cost camera modules connected to microcontrollers can monitor for disease, pests, and nutritional deficiencies. TinyML algorithms can analyze the images to detect issues early before they spread and damage yields. Precision monitoring can optimize inputs like water, fertilizer, and pesticides - improving efficiency and sustainability.\nOther sensors, such as GPS units and accelerometers, can track microclimate conditions, soil humidity, and livestock wellbeing. Local real-time data helps farmers respond and adapt better to changes in the field. TinyML analytics at the edge avoids lag, network disruptions, and the high data costs of cloud-based systems. Localized systems allow customization of specific crops, diseases, and regional issues.\nWidespread TinyML applications can help digitize smallholder farms to increase productivity, incomes, and resilience. The low cost of hardware and minimal connectivity requirements make solutions accessible. Projects across the developing world have shown the benefits:\n\nMicrosoft’s FarmBeats project is an end-to-end approach to enable data-driven farming by using low-cost sensors, drones, and vision and machine learning algorithms. The project aims to solve the problem of limited adoption of technology in farming due to the need for more power and internet connectivity in farms and the farmers’ limited technology savviness. The project aims to increase farm productivity and reduce costs by coupling data with farmers’ knowledge and intuition about their farms. The project has successfully enabled actionable insights from data by building artificial intelligence (AI) or machine learning (ML) models based on fused data sets.\nIn Sub-Saharan Africa, off-the-shelf cameras and edge AI have cut cassava disease losses from 40% to 5%, protecting a staple crop (Ramcharan et al. 2017).\nIn Indonesia, sensors monitor microclimates across rice paddies, optimizing water usage even with erratic rains (Tirtalistyani, Murtiningrum, and Kanwar 2022).\n\n\nRamcharan, Amanda, Kelsee Baranowski, Peter McCloskey, Babuali Ahmed, James Legg, and David P. Hughes. 2017. “Deep Learning for Image-Based Cassava Disease Detection.” Front. Plant Sci. 8 (October): 1852. https://doi.org/10.3389/fpls.2017.01852.\n\nTirtalistyani, Rose, Murtiningrum Murtiningrum, and Rameshwar S. Kanwar. 2022. “Indonesia Rice Irrigation System: Time for Innovation.” Sustainability 14 (19): 12477. https://doi.org/10.3390/su141912477.\nWith greater investment and integration into rural advisory services, TinyML could transform small-scale agriculture and improve farmers’ livelihoods worldwide. The technology effectively brings the benefits of precision agriculture to disconnected regions most in need.\n\n\n\n\n\n\nExercise 19.1: Crop Yield Modeling\n\n\n\n\n\nThis exercise teaches you how to predict crop yields in Nepal by combining satellite data (Sentinel-2), climate data (WorldClim), and on-the-ground measurements. You’ll use a machine learning algorithm called XGBoost Regressor to build a model, split the data for training and testing, and fine-tune the model parameters for the best performance. This notebook lays the foundation for implementing TinyML in the agriculture domain. Consider how you could adapt this process for smaller datasets, fewer features, and simplified models to make it compatible with the power and memory constraints of TinyML devices.", + "text": "19.2 Agriculture\nAgriculture is essential to achieving many of the UN Sustainable Development Goals, including eradicating Hunger and malnutrition, promoting economic growth, and using natural resources sustainably. TinyML can be a valuable tool to help advance sustainable agriculture, especially for smallholder farmers in developing regions.\nTinyML solutions can provide real-time monitoring and data analytics for crop health and growing conditions - all without reliance on connectivity infrastructure. For example, low-cost camera modules connected to microcontrollers can monitor for disease, pests, and nutritional deficiencies. TinyML algorithms can analyze the images to detect issues early before they spread and damage yields. Precision monitoring can optimize inputs like water, fertilizer, and pesticides - improving efficiency and sustainability.\nOther sensors, such as GPS units and accelerometers, can track microclimate conditions, soil humidity, and livestock wellbeing. Local real-time data helps farmers respond and adapt better to changes in the field. TinyML analytics at the edge avoids lag, network disruptions, and the high data costs of cloud-based systems. Localized systems allow customization of specific crops, diseases, and regional issues.\nWidespread TinyML applications can help digitize smallholder farms to increase productivity, incomes, and resilience. The low cost of hardware and minimal connectivity requirements make solutions accessible. Projects across the developing world have shown the benefits:\n\nMicrosoft’s FarmBeats project is an end-to-end approach to enable data-driven farming by using low-cost sensors, drones, and vision and machine learning algorithms. The project seeks to solve the problem of limited adoption of technology in farming due to the need for more power and internet connectivity in farms and the farmers’ limited technology savviness. The project strives to increase farm productivity and reduce costs by coupling data with farmers’ knowledge and intuition about their farms. The project has successfully enabled actionable insights from data by building artificial intelligence (AI) or machine learning (ML) models based on fused data sets.\nIn Sub-Saharan Africa, off-the-shelf cameras and edge AI have cut cassava disease losses from 40% to 5%, protecting a staple crop (Ramcharan et al. 2017).\nIn Indonesia, sensors monitor microclimates across rice paddies, optimizing water usage even with erratic rains (Tirtalistyani, Murtiningrum, and Kanwar 2022).\n\n\nRamcharan, Amanda, Kelsee Baranowski, Peter McCloskey, Babuali Ahmed, James Legg, and David P. Hughes. 2017. “Deep Learning for Image-Based Cassava Disease Detection.” Front. Plant Sci. 8 (October): 1852. https://doi.org/10.3389/fpls.2017.01852.\n\nTirtalistyani, Rose, Murtiningrum Murtiningrum, and Rameshwar S. Kanwar. 2022. “Indonesia Rice Irrigation System: Time for Innovation.” Sustainability 14 (19): 12477. https://doi.org/10.3390/su141912477.\nWith greater investment and integration into rural advisory services, TinyML could transform small-scale agriculture and improve farmers’ livelihoods worldwide. The technology effectively brings the benefits of precision agriculture to disconnected regions most in need.\n\n\n\n\n\n\nExercise 19.1: Crop Yield Modeling\n\n\n\n\n\nThis exercise teaches you how to predict crop yields in Nepal by combining satellite data (Sentinel-2), climate data (WorldClim), and on-the-ground measurements. You’ll use a machine learning algorithm called XGBoost Regressor to build a model, split the data for training and testing, and fine-tune the model parameters for the best performance. This notebook lays the foundation for implementing TinyML in the agriculture domain. Consider how you could adapt this process for smaller datasets, fewer features, and simplified models to make it compatible with the power and memory constraints of TinyML devices.", "crumbs": [ "Social Impact", "19  AI for Good" @@ -2028,7 +2028,7 @@ "href": "contents/ai_for_good/ai_for_good.html#healthcare", "title": "19  AI for Good", "section": "19.3 Healthcare", - "text": "19.3 Healthcare\n\n19.3.1 Expanding Access\nUniversal health coverage and quality care remain out of reach for millions worldwide. In many regions, more medical professionals are required to Access basic diagnosis and treatment. Additionally, healthcare infrastructure like clinics, hospitals, and utilities to power complex equipment needs to be improved. These gaps disproportionately impact marginalized communities, exacerbating health disparities.\nTinyML offers a promising technological solution to help expand Access to quality healthcare globally. TinyML refers to the ability to deploy machine learning algorithms on microcontrollers, tiny chips with processing power, memory, and connectivity. TinyML enables real-time data analysis and intelligence in low-powered, compact devices.\nThis creates opportunities for transformative medical tools that are portable, affordable, and accessible. TinyML software and hardware can be optimized to run even in resource-constrained environments. For example, a TinyML system could analyze symptoms or make diagnostic predictions using minimal computing power, no continuous internet connectivity, and a battery or solar power source. These capabilities can bring medical-grade screening and monitoring directly to underserved patients.\n\n\n19.3.2 Early Diagnosis\nEarly detection of diseases is one major application. Small sensors paired with TinyML software can identify symptoms before conditions escalate or visible signs appear. For instance, cough monitors with embedded machine learning can pick up on acoustic patterns indicative of respiratory illness, malaria, or tuberculosis. Detecting diseases at onset improves outcomes and reduces healthcare costs.\nA detailed example could be given for TinyML monitoring pneumonia in children. Pneumonia is a leading cause of death for children under 5, and detecting it early is critical. A startup called Respira xColabs has developed a low-cost wearable audio sensor that uses TinyML algorithms to analyze coughs and identify symptoms of respiratory illnesses like pneumonia. The device contains a microphone sensor and microcontroller that runs a neural network model trained to classify respiratory sounds. It can identify features like wheezing, crackling, and stridor that may indicate pneumonia. The device is designed to be highly accessible - it has a simple strap, requires no battery or charging, and results are provided through LED lights and audio cues.\nAnother example involves researchers at UNIFEI in Brazil who have developed a low-cost device that leverages TinyML to monitor heart rhythms. Their innovative solution addresses a critical need - atrial fibrillation and other heart rhythm abnormalities often go undiagnosed due to the prohibitive cost and limited availability of screening tools. The device overcomes these barriers through its ingenious design. It uses an off-the-shelf microcontroller that costs only a few dollars, along with a basic pulse sensor. By minimizing complexity, the device becomes accessible to under-resourced populations. The TinyML algorithm running locally on the microcontroller analyzes pulse data in real-time to detect irregular heart rhythms. This life-saving heart monitoring device demonstrates how TinyML enables powerful AI capabilities to be deployed in cost-effective, user-friendly designs.\nTinyML’s versatility also shows promise for tackling infectious diseases. Researchers have proposed applying TinyML to identify malaria-spreading mosquitoes by their wingbeat sounds. When equipped with microphones, small microcontrollers can run advanced audio classification models to determine mosquito species. This compact, low-power solution produces results in real time, suitable for remote field use. By making entomology analytics affordable and accessible, TinyML could revolutionize monitoring insects that endanger human health. TinyML is expanding healthcare access for vulnerable communities from heart disease to malaria.\n\n\n19.3.3 Infectious Disease Control\nMosquitoes remain the most deadly disease vector worldwide, transmitting illnesses that infect over one billion people annually (“Vector-Borne Diseases,” n.d.). Diseases like malaria, dengue, and Zika are especially prevalent in resource-limited regions lacking robust infrastructure for mosquito control. Monitoring local mosquito populations is essential to prevent outbreaks and properly target interventions.\n\n“Vector-Borne Diseases.” n.d. https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases.\nTraditional monitoring methods are expensive, labor-intensive, and difficult to deploy remotely. The proposed TinyML solution aims to overcome these barriers. Small microphones coupled with machine learning algorithms can classify mosquitoes by species based on minute differences in wing oscillations. The TinyML software runs efficiently on low-cost microcontrollers, eliminating the need for continuous connectivity.\nA collaborative research team from the University of Khartoum and the ICTP is exploring an innovative solution using TinyML. In a recent paper, they presented a low-cost device that can identify disease-spreading mosquito species through their wing beat sounds (Altayeb, Zennaro, and Rovai 2022).\n\nAltayeb, Moez, Marco Zennaro, and Marcelo Rovai. 2022. “Classifying Mosquito Wingbeat Sound Using TinyML.” In Proceedings of the 2022 ACM Conference on Information Technology for Social Good, 132–37. ACM. https://doi.org/10.1145/3524458.3547258.\nThis portable, self-contained system shows great promise for entomology. The researchers suggest it could revolutionize insect monitoring and vector control strategies in remote areas. TinyML could significantly bolster malaria eradication efforts by providing cheaper, easier mosquito analytics. Its versatility and minimal power needs make it ideal for field use in isolated, off-grid regions with scarce resources but high disease burden.\n\n\n19.3.4 TinyML Design Contest in Healthcare\nThe first TinyML contest in healthcare, TDC’22 (Jia et al. 2023), was held in 2022 to motivate participating teams to design AI/ML algorithms for detecting life-threatening ventricular arrhythmias (VAs) and deploy them on Implantable Cardioverter Defibrillators (ICDs). VAs are the main cause of sudden cardiac death (SCD). People at high risk of SCD rely on the ICD to deliver proper and timely defibrillation treatment (i.e., shocking the heart back into normal rhythm) when experiencing life-threatening VAs.\n\nJia, Zhenge, Dawei Li, Xiaowei Xu, Na Li, Feng Hong, Lichuan Ping, and Yiyu Shi. 2023. “Life-Threatening Ventricular Arrhythmia Detection Challenge in Implantable Cardioverterdefibrillators.” Nature Machine Intelligence 5 (5): 554–55. https://doi.org/10.1038/s42256-023-00659-9.\nAn on-device algorithm for early and timely life-threatening VA detection will increase the chances of survival. The proposed AI/ML algorithm needed to be deployed and executed on an extremely low-power and resource-constrained microcontroller (MCU) (a $10 development board with an ARM Cortex-M4 core at 80 MHz, 256 kB of flash memory and 64 kB of SRAM). The submitted designs were evaluated by metrics measured on the MCU for (1) detection performance, (2) inference latency, and (3) memory occupation by the program of AI/ML algorithms.\nThe champion, GaTech EIC Lab, obtained 0.972 in \\(F_\\beta\\) (F1 score with a higher weight to recall), 1.747 ms in latency, and 26.39 kB in memory footprint with a deep neural network. An ICD with an on-device VA detection algorithm was implanted in a clinical trial.\n\n\n\n\n\n\nExercise 19.2: Clinical Data: Unlocking Insights with Named Entity Recognition\n\n\n\n\n\nIn this exercise, you’ll learn about Named Entity Recognition (NER), a powerful tool for extracting valuable information from clinical text. Using Spark NLP, a specialized library for healthcare NLP, we’ll explore how NER models like BiLSTM-CNN-Char and BERT can automatically identify important medical entities such as diagnoses, medications, test results, and more. You’ll get hands-on experience applying these techniques with a special focus on oncology-related data extraction, helping you unlock insights about cancer types and treatment details from patient records.", + "text": "19.3 Healthcare\n\n19.3.1 Expanding Access\nUniversal health coverage and quality care remain out of reach for millions worldwide. In many regions, more medical professionals are required to Access basic diagnosis and treatment. Additionally, healthcare infrastructure like clinics, hospitals, and utilities to power complex equipment needs to be improved. These gaps disproportionately impact marginalized communities, exacerbating health disparities.\nTinyML offers a promising technological solution to help expand Access to quality healthcare globally. TinyML refers to the ability to deploy machine learning algorithms on microcontrollers, tiny chips with processing power, memory, and connectivity. TinyML enables real-time data analysis and intelligence in low-powered, compact devices.\nThis creates opportunities for transformative medical tools that are portable, affordable, and accessible. TinyML software and hardware can be optimized to run even in resource-constrained environments. For example, a TinyML system could analyze symptoms or make diagnostic predictions using minimal computing power, no continuous internet connectivity, and a battery or solar power source. These capabilities can bring medical-grade screening and monitoring directly to underserved patients.\n\n\n19.3.2 Early Diagnosis\nEarly detection of diseases is one major application. Small sensors paired with TinyML software can identify symptoms before conditions escalate or visible signs appear. For instance, cough monitors with embedded machine learning can pick up on acoustic patterns indicative of respiratory illness, malaria, or tuberculosis. Detecting diseases at onset improves outcomes and reduces healthcare costs.\nA detailed example could be given for TinyML monitoring pneumonia in children. Pneumonia is a leading cause of death for children under 5, and detecting it early is critical. A startup called Respira xColabs has developed a low-cost wearable audio sensor that uses TinyML algorithms to analyze coughs and identify symptoms of respiratory illnesses like pneumonia. The device contains a microphone sensor and microcontroller that runs a neural network model trained to classify respiratory sounds. It can identify features like wheezing, crackling, and stridor that may indicate pneumonia. The device is designed to be highly accessible - it has a simple strap, requires no battery or charging, and results are provided through LED lights and audio cues.\nAnother example involves researchers at UNIFEI in Brazil who have developed a low-cost device that leverages TinyML to monitor heart rhythms. Their innovative solution addresses a critical need - atrial fibrillation and other heart rhythm abnormalities often go undiagnosed due to the prohibitive cost and limited availability of screening tools. The device overcomes these barriers through its ingenious design. It uses an off-the-shelf microcontroller that costs only a few dollars, along with a basic pulse sensor. By minimizing complexity, the device becomes accessible to under-resourced populations. The TinyML algorithm running locally on the microcontroller analyzes pulse data in real-time to detect irregular heart rhythms. This life-saving heart monitoring device demonstrates how TinyML enables powerful AI capabilities to be deployed in cost-effective, user-friendly designs.\nTinyML’s versatility also shows promise for tackling infectious diseases. Researchers have proposed applying TinyML to identify malaria-spreading mosquitoes by their wingbeat sounds. When equipped with microphones, small microcontrollers can run advanced audio classification models to determine mosquito species. This compact, low-power solution produces results in real time, suitable for remote field use. By making entomology analytics affordable and accessible, TinyML could revolutionize monitoring insects that endanger human health. TinyML is expanding healthcare access for vulnerable communities from heart disease to malaria.\n\n\n19.3.3 Infectious Disease Control\nMosquitoes remain the most deadly disease vector worldwide, transmitting illnesses that infect over one billion people annually (“Vector-Borne Diseases,” n.d.). Diseases like malaria, dengue, and Zika are especially prevalent in resource-limited regions lacking robust infrastructure for mosquito control. Monitoring local mosquito populations is essential to prevent outbreaks and properly target interventions.\n\n“Vector-Borne Diseases.” n.d. https://www.who.int/news-room/fact-sheets/detail/vector-borne-diseases.\nTraditional monitoring methods are expensive, labor-intensive, and difficult to deploy remotely. The proposed TinyML solution overcomes these barriers. Small microphones coupled with machine learning algorithms can classify mosquitoes by species based on minute differences in wing oscillations. The TinyML software runs efficiently on low-cost microcontrollers, eliminating the need for continuous connectivity.\nA collaborative research team from the University of Khartoum and the ICTP is exploring an innovative solution using TinyML. In a recent paper, they presented a low-cost device that can identify disease-spreading mosquito species through their wing beat sounds (Altayeb, Zennaro, and Rovai 2022).\n\nAltayeb, Moez, Marco Zennaro, and Marcelo Rovai. 2022. “Classifying Mosquito Wingbeat Sound Using TinyML.” In Proceedings of the 2022 ACM Conference on Information Technology for Social Good, 132–37. ACM. https://doi.org/10.1145/3524458.3547258.\nThis portable, self-contained system shows great promise for entomology. The researchers suggest it could revolutionize insect monitoring and vector control strategies in remote areas. TinyML could significantly bolster malaria eradication efforts by providing cheaper, easier mosquito analytics. Its versatility and minimal power needs make it ideal for field use in isolated, off-grid regions with scarce resources but high disease burden.\n\n\n19.3.4 TinyML Design Contest in Healthcare\nThe first TinyML contest in healthcare, TDC’22 (Jia et al. 2023), was held in 2022 to motivate participating teams to design AI/ML algorithms for detecting life-threatening ventricular arrhythmias (VAs) and deploy them on Implantable Cardioverter Defibrillators (ICDs). VAs are the main cause of sudden cardiac death (SCD). People at high risk of SCD rely on the ICD to deliver proper and timely defibrillation treatment (i.e., shocking the heart back into normal rhythm) when experiencing life-threatening VAs.\n\nJia, Zhenge, Dawei Li, Xiaowei Xu, Na Li, Feng Hong, Lichuan Ping, and Yiyu Shi. 2023. “Life-Threatening Ventricular Arrhythmia Detection Challenge in Implantable Cardioverterdefibrillators.” Nature Machine Intelligence 5 (5): 554–55. https://doi.org/10.1038/s42256-023-00659-9.\nAn on-device algorithm for early and timely life-threatening VA detection will increase the chances of survival. The proposed AI/ML algorithm needed to be deployed and executed on an extremely low-power and resource-constrained microcontroller (MCU) (a $10 development board with an ARM Cortex-M4 core at 80 MHz, 256 kB of flash memory and 64 kB of SRAM). The submitted designs were evaluated by metrics measured on the MCU for (1) detection performance, (2) inference latency, and (3) memory occupation by the program of AI/ML algorithms.\nThe champion, GaTech EIC Lab, obtained 0.972 in \\(F_\\beta\\) (F1 score with a higher weight to recall), 1.747 ms in latency, and 26.39 kB in memory footprint with a deep neural network. An ICD with an on-device VA detection algorithm was implanted in a clinical trial.\n\n\n\n\n\n\nExercise 19.2: Clinical Data: Unlocking Insights with Named Entity Recognition\n\n\n\n\n\nIn this exercise, you’ll learn about Named Entity Recognition (NER), a powerful tool for extracting valuable information from clinical text. Using Spark NLP, a specialized library for healthcare NLP, we’ll explore how NER models like BiLSTM-CNN-Char and BERT can automatically identify important medical entities such as diagnoses, medications, test results, and more. You’ll get hands-on experience applying these techniques with a special focus on oncology-related data extraction, helping you unlock insights about cancer types and treatment details from patient records.", "crumbs": [ "Social Impact", "19  AI for Good" @@ -2138,7 +2138,7 @@ "href": "contents/conclusion/conclusion.html", "title": "20  Conclusion", "section": "", - "text": "20.1 Introduction\nThis book examines the rapidly evolving field of ML systems (Chapter 2). We focus on systems because while there are many resources on ML models and algorithms, more needs to be understood about how to build the systems that run them.\nTo draw an analogy, consider the process of building a car. While many resources are available on the various components of a car, such as the engine, transmission, and suspension, there is often a need for more understanding about how to assemble these components into a functional vehicle. Just as a car requires a well-designed and properly integrated system to operate efficiently and reliably, ML models also require a robust and carefully constructed system to deliver their full potential. Moreover, there is a lot of nuance in building ML systems, given their specific use case. For example, a Formula 1 race car must be assembled differently from an everyday Prius consumer car.\nOur journey started by tracing ML’s historical trajectory, from its theoretical foundations to its current state as a transformative force across industries (Chapter 3). This journey has highlighted the remarkable progress in the field, challenges, and opportunities.\nThroughout this book, we have looked into the intricacies of ML systems, examining the critical components and best practices necessary to create a seamless and efficient pipeline. From data preprocessing and model training to deployment and monitoring, we have provided insights and guidance to help readers navigate the complex landscape of ML system development.\nML systems involve complex workflows, spanning various topics from data engineering to model deployment on diverse systems (Chapter 4). By providing an overview of these ML system components, we have aimed to showcase the tremendous depth and breadth of the field and expertise that is needed. Understanding the intricacies of ML workflows is crucial for practitioners and researchers alike, as it enables them to navigate the landscape effectively and develop robust, efficient, and impactful ML solutions.\nBy focusing on the systems aspect of ML, we aim to bridge the gap between theoretical knowledge and practical implementation. Just as a healthy human body system allows the organs to function optimally, a well-designed ML system enables the models to consistently deliver accurate and reliable results. This book aims to empower readers with the knowledge and tools necessary to build ML systems that showcase the underlying models’ power and ensure smooth integration and operation, much like a well-functioning human body.", + "text": "20.1 Introduction\nThis book examines the rapidly evolving field of ML systems (Chapter 2). We focus on systems because while there are many resources on ML models and algorithms, more needs to be understood about how to build the systems that run them.\nTo draw an analogy, consider the process of building a car. While many resources are available on the various components of a car, such as the engine, transmission, and suspension, there is often a need for more understanding about how to assemble these components into a functional vehicle. Just as a car requires a well-designed and properly integrated system to operate efficiently and reliably, ML models also require a robust and carefully constructed system to deliver their full potential. Moreover, there is a lot of nuance in building ML systems, given their specific use case. For example, a Formula 1 race car must be assembled differently from an everyday Prius consumer car.\nOur journey started by tracing ML’s historical trajectory, from its theoretical foundations to its current state as a transformative force across industries (Chapter 3). This journey has highlighted the remarkable progress in the field, challenges, and opportunities.\nThroughout this book, we have looked into the intricacies of ML systems, examining the critical components and best practices necessary to create a seamless and efficient pipeline. From data preprocessing and model training to deployment and monitoring, we have provided insights and guidance to help readers navigate the complex landscape of ML system development.\nML systems involve complex workflows, spanning various topics from data engineering to model deployment on diverse systems (Chapter 4). By providing an overview of these ML system components, we have aimed to showcase the tremendous depth and breadth of the field and expertise that is needed. Understanding the intricacies of ML workflows is crucial for practitioners and researchers alike, as it enables them to navigate the landscape effectively and develop robust, efficient, and impactful ML solutions.\nBy focusing on the systems aspect of ML, we aim to bridge the gap between theoretical knowledge and practical implementation. Just as a healthy human body system allows the organs to function optimally, a well-designed ML system enables the models to consistently deliver accurate and reliable results. This book’s goal is to empower readers with the knowledge and tools necessary to build ML systems that showcase the underlying models’ power and ensure smooth integration and operation, much like a well-functioning human body.", "crumbs": [ "Closing", "20  Conclusion" @@ -2545,7 +2545,7 @@ "href": "contents/labs/arduino/nicla_vision/image_classification/image_classification.html#computer-vision", "title": "Image Classification", "section": "Computer Vision", - "text": "Computer Vision\nAt its core, computer vision aims to enable machines to interpret and make decisions based on visual data from the world, essentially mimicking the capability of the human optical system. Conversely, AI is a broader field encompassing machine learning, natural language processing, and robotics, among other technologies. When you bring AI algorithms into computer vision projects, you supercharge the system’s ability to understand, interpret, and react to visual stimuli.\nWhen discussing Computer Vision projects applied to embedded devices, the most common applications that come to mind are Image Classification and Object Detection.\n\nBoth models can be implemented on tiny devices like the Arduino Nicla Vision and used on real projects. In this chapter, we will cover Image Classification.", + "text": "Computer Vision\nAt its core, computer vision enables machines to interpret and make decisions based on visual data from the world, essentially mimicking the capability of the human optical system. Conversely, AI is a broader field encompassing machine learning, natural language processing, and robotics, among other technologies. When you bring AI algorithms into computer vision projects, you supercharge the system’s ability to understand, interpret, and react to visual stimuli.\nWhen discussing Computer Vision projects applied to embedded devices, the most common applications that come to mind are Image Classification and Object Detection.\n\nBoth models can be implemented on tiny devices like the Arduino Nicla Vision and used on real projects. In this chapter, we will cover Image Classification.", "crumbs": [ "Nicla Vision", "Image Classification" @@ -3876,7 +3876,7 @@ "href": "contents/labs/shared/kws_feature_eng/kws_feature_eng.html#the-kws", "title": "KWS Feature Engineering", "section": "The KWS", - "text": "The KWS\nThe most common TinyML application is Keyword Spotting (KWS), a subset of the broader field of speech recognition. While general speech recognition aims to transcribe all spoken words into text, Keyword Spotting focuses on detecting specific “keywords” or “wake words” in a continuous audio stream. The system is trained to recognize these keywords as predefined phrases or words, such as yes or no. In short, KWS is a specialized form of speech recognition with its own set of challenges and requirements.\nHere a typical KWS Process using MFCC Feature Converter:\n\n\nApplications of KWS\n\nVoice Assistants: In devices like Amazon’s Alexa or Google Home, KWS is used to detect the wake word (“Alexa” or “Hey Google”) to activate the device.\nVoice-Activated Controls: In automotive or industrial settings, KWS can be used to initiate specific commands like “Start engine” or “Turn off lights.”\nSecurity Systems: Voice-activated security systems may use KWS to authenticate users based on a spoken passphrase.\nTelecommunication Services: Customer service lines may use KWS to route calls based on spoken keywords.\n\n\n\nDifferences from General Speech Recognition\n\nComputational Efficiency: KWS is usually designed to be less computationally intensive than full speech recognition, as it only needs to recognize a small set of phrases.\nReal-time Processing: KWS often operates in real-time and is optimized for low-latency detection of keywords.\nResource Constraints: KWS models are often designed to be lightweight, so they can run on devices with limited computational resources, like microcontrollers or mobile phones.\nFocused Task: While general speech recognition models are trained to handle a broad range of vocabulary and accents, KWS models are fine-tuned to recognize specific keywords, often in noisy environments accurately.", + "text": "The KWS\nThe most common TinyML application is Keyword Spotting (KWS), a subset of the broader field of speech recognition. While general speech recognition transcribes all spoken words into text, Keyword Spotting focuses on detecting specific “keywords” or “wake words” in a continuous audio stream. The system is trained to recognize these keywords as predefined phrases or words, such as yes or no. In short, KWS is a specialized form of speech recognition with its own set of challenges and requirements.\nHere a typical KWS Process using MFCC Feature Converter:\n\n\nApplications of KWS\n\nVoice Assistants: In devices like Amazon’s Alexa or Google Home, KWS is used to detect the wake word (“Alexa” or “Hey Google”) to activate the device.\nVoice-Activated Controls: In automotive or industrial settings, KWS can be used to initiate specific commands like “Start engine” or “Turn off lights.”\nSecurity Systems: Voice-activated security systems may use KWS to authenticate users based on a spoken passphrase.\nTelecommunication Services: Customer service lines may use KWS to route calls based on spoken keywords.\n\n\n\nDifferences from General Speech Recognition\n\nComputational Efficiency: KWS is usually designed to be less computationally intensive than full speech recognition, as it only needs to recognize a small set of phrases.\nReal-time Processing: KWS often operates in real-time and is optimized for low-latency detection of keywords.\nResource Constraints: KWS models are often designed to be lightweight, so they can run on devices with limited computational resources, like microcontrollers or mobile phones.\nFocused Task: While general speech recognition models are trained to handle a broad range of vocabulary and accents, KWS models are fine-tuned to recognize specific keywords, often in noisy environments accurately.", "crumbs": [ "Shared Labs", "KWS Feature Engineering" diff --git a/site_libs/bootstrap/bootstrap-dark.min 2.css b/site_libs/bootstrap/bootstrap-dark.min 2.css deleted file mode 100644 index 768d61fd..00000000 --- a/site_libs/bootstrap/bootstrap-dark.min 2.css +++ /dev/null @@ -1,12 +0,0 @@ -@import"https://fonts.googleapis.com/css2?family=Nunito:wght@400;800&display=swap";@import"https://fonts.googleapis.com/css2?family=Lato:ital,wght@0,400;0,700;1,400&display=swap";:root{--link-color: #A51C30}div.sidebar-item-container 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#141414;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: rgb(27, 27, 27);--bs-btn-disabled-border-color: rgb(27, 27, 27)}.btn-secondary{--bs-btn-color: #fff;--bs-btn-bg: #434343;--bs-btn-border-color: #434343;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #393939;--bs-btn-hover-border-color: #363636;--bs-btn-focus-shadow-rgb: 95, 95, 95;--bs-btn-active-color: #fff;--bs-btn-active-bg: #363636;--bs-btn-active-border-color: #323232;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #434343;--bs-btn-disabled-border-color: #434343}.btn-success{--bs-btn-color: #fff;--bs-btn-bg: #E09F9C;--bs-btn-border-color: #E09F9C;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #be8785;--bs-btn-hover-border-color: #b37f7d;--bs-btn-focus-shadow-rgb: 229, 173, 171;--bs-btn-active-color: #fff;--bs-btn-active-bg: #b37f7d;--bs-btn-active-border-color: #a87775;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #E09F9C;--bs-btn-disabled-border-color: #E09F9C}.btn-info{--bs-btn-color: #fff;--bs-btn-bg: #3498db;--bs-btn-border-color: #3498db;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2c81ba;--bs-btn-hover-border-color: #2a7aaf;--bs-btn-focus-shadow-rgb: 82, 167, 224;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2a7aaf;--bs-btn-active-border-color: #2772a4;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #3498db;--bs-btn-disabled-border-color: #3498db}.btn-warning{--bs-btn-color: #fff;--bs-btn-bg: #f39c12;--bs-btn-border-color: #f39c12;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #cf850f;--bs-btn-hover-border-color: #c27d0e;--bs-btn-focus-shadow-rgb: 245, 171, 54;--bs-btn-active-color: #fff;--bs-btn-active-bg: #c27d0e;--bs-btn-active-border-color: #b6750e;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #f39c12;--bs-btn-disabled-border-color: #f39c12}.btn-danger{--bs-btn-color: #fff;--bs-btn-bg: #e74c3c;--bs-btn-border-color: #e74c3c;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #c44133;--bs-btn-hover-border-color: #b93d30;--bs-btn-focus-shadow-rgb: 235, 103, 89;--bs-btn-active-color: #fff;--bs-btn-active-bg: #b93d30;--bs-btn-active-border-color: #ad392d;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #e74c3c;--bs-btn-disabled-border-color: #e74c3c}.btn-light{--bs-btn-color: #fff;--bs-btn-bg: #6f6f6f;--bs-btn-border-color: #6f6f6f;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #5e5e5e;--bs-btn-hover-border-color: #595959;--bs-btn-focus-shadow-rgb: 133, 133, 133;--bs-btn-active-color: #fff;--bs-btn-active-bg: #595959;--bs-btn-active-border-color: #535353;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #6f6f6f;--bs-btn-disabled-border-color: #6f6f6f}.btn-dark{--bs-btn-color: #fff;--bs-btn-bg: #2d2d2d;--bs-btn-border-color: #2d2d2d;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #4d4d4d;--bs-btn-hover-border-color: #424242;--bs-btn-focus-shadow-rgb: 77, 77, 77;--bs-btn-active-color: #fff;--bs-btn-active-bg: #575757;--bs-btn-active-border-color: #424242;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #fff;--bs-btn-disabled-bg: #2d2d2d;--bs-btn-disabled-border-color: #2d2d2d}.btn-outline-default{--bs-btn-color: #434343;--bs-btn-border-color: #434343;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #434343;--bs-btn-hover-border-color: #434343;--bs-btn-focus-shadow-rgb: 67, 67, 67;--bs-btn-active-color: #fff;--bs-btn-active-bg: #434343;--bs-btn-active-border-color: #434343;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #434343;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #434343;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-primary{--bs-btn-color: rgb(27, 27, 27);--bs-btn-border-color: rgb(27, 27, 27);--bs-btn-hover-color: #fff;--bs-btn-hover-bg: rgb(27, 27, 27);--bs-btn-hover-border-color: rgb(27, 27, 27);--bs-btn-focus-shadow-rgb: 27, 27, 27;--bs-btn-active-color: #fff;--bs-btn-active-bg: rgb(27, 27, 27);--bs-btn-active-border-color: rgb(27, 27, 27);--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: rgb(27, 27, 27);--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: rgb(27, 27, 27);--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #434343;--bs-btn-border-color: #434343;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #434343;--bs-btn-hover-border-color: #434343;--bs-btn-focus-shadow-rgb: 67, 67, 67;--bs-btn-active-color: #fff;--bs-btn-active-bg: #434343;--bs-btn-active-border-color: #434343;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: 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inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #e74c3c;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #e74c3c;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #6f6f6f;--bs-btn-border-color: #6f6f6f;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #6f6f6f;--bs-btn-hover-border-color: #6f6f6f;--bs-btn-focus-shadow-rgb: 111, 111, 111;--bs-btn-active-color: #fff;--bs-btn-active-bg: #6f6f6f;--bs-btn-active-border-color: #6f6f6f;--bs-btn-active-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);--bs-btn-disabled-color: #6f6f6f;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #6f6f6f;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #2d2d2d;--bs-btn-border-color: #2d2d2d;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #2d2d2d;--bs-btn-hover-border-color: #2d2d2d;--bs-btn-focus-shadow-rgb: 45, 45, 45;--bs-btn-active-color: #fff;--bs-btn-active-bg: #2d2d2d;--bs-btn-active-border-color: 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var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23767676'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23767676'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: #434343;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(255, 255, 255, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #E09F9C;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #ecc5c3;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #b37f7d;--bs-pagination-focus-bg: #ebebeb;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(27, 27, 27, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #ecc5c3;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #fff;--bs-pagination-disabled-bg: #ce6762;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #434343;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: rgb(27, 27, 27);--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #fff;--bs-list-group-bg: #2d2d2d;--bs-list-group-border-color: #434343;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(255, 255, 255, 0.75);--bs-list-group-action-hover-color: #fff;--bs-list-group-action-hover-bg: #434343;--bs-list-group-action-active-color: #fff;--bs-list-group-action-active-bg: #222;--bs-list-group-disabled-color: rgba(255, 255, 255, 0.75);--bs-list-group-disabled-bg: #2d2d2d;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: rgb(27, 27, 27);--bs-list-group-active-border-color: rgb(27, 27, 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". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(27, 27, 27, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: #434343;--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(255, 255, 255, 0.75);--bs-toast-header-bg: #2d2d2d;--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) 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1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #434343;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #434343;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable 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auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down 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.modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #222;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:Lato,-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI 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50%)}.bslib-value-box.showcase-top-right .value-box-grid .value-box-showcase{grid-area:right;margin-left:auto;align-self:start;align-items:end;padding-left:0;padding-bottom:0}.bslib-value-box.showcase-top-right .value-box-grid .value-box-area{grid-area:left;align-self:end}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid{grid-template-columns:auto var(---bslib-value-box-showcase-w-fs, 1fr)}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid>div{align-self:center}.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-showcase{margin-top:0}@container bslib-value-box (max-width: 300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card 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1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes bslib-full-screen-overlay-enter{0%{opacity:0}100%{opacity:1}}.bslib-grid{display:grid !important;gap:var(--bslib-spacer, 1rem);height:var(--bslib-grid-height)}.bslib-grid.grid{grid-template-columns:repeat(var(--bs-columns, 12), minmax(0, 1fr));grid-template-rows:unset;grid-auto-rows:var(--bslib-grid--row-heights);--bslib-grid--row-heights--xs: unset;--bslib-grid--row-heights--sm: unset;--bslib-grid--row-heights--md: unset;--bslib-grid--row-heights--lg: unset;--bslib-grid--row-heights--xl: unset;--bslib-grid--row-heights--xxl: unset}.bslib-grid.grid.bslib-grid--row-heights--xs{--bslib-grid--row-heights: 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.accordion-button:not(.collapsed):focus{box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.navbar+.container-fluid:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-sm:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-md:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-lg:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-xl:has(>.tab-content>.tab-pane.active.html-fill-container),.navbar+.container-xxl:has(>.tab-content>.tab-pane.active.html-fill-container){padding-left:0;padding-right:0}.navbar+.container-fluid>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-sm>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-md>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-lg>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-xl>.tab-content>.tab-pane.active.html-fill-container,.navbar+.container-xxl>.tab-content>.tab-pane.active.html-fill-container{padding:var(--bslib-spacer, 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var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:0.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: #434343;--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: #434343;--bs-dropdown-link-hover-bg: rgba(255, 255, 255, 0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: rgb(27, 27, 27);--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:.25rem}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(1px*-1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n+3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(1px*-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 2rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #E09F9C;--bs-nav-link-hover-color: #b37f7d;--bs-nav-link-disabled-color: #6f6f6f;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out}@media(prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem 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.nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: 1rem;--bs-navbar-color: #dee2e6;--bs-navbar-hover-color: rgba(255, 255, 255, 0.8);--bs-navbar-disabled-color: rgba(222, 226, 230, 0.75);--bs-navbar-active-color: #fff;--bs-navbar-brand-padding-y: 0.3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: #dee2e6;--bs-navbar-brand-hover-color: #fff;--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25;--bs-navbar-toggler-padding-x: 0;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(222, 226, 230, 0);--bs-navbar-toggler-border-radius: 0.25rem;--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23767676'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23767676'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: #434343;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(255, 255, 255, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #E09F9C;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #ecc5c3;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #b37f7d;--bs-pagination-focus-bg: #ebebeb;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(27, 27, 27, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #ecc5c3;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #fff;--bs-pagination-disabled-bg: #ce6762;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #434343;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: rgb(27, 27, 27);--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #fff;--bs-list-group-bg: #2d2d2d;--bs-list-group-border-color: #434343;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(255, 255, 255, 0.75);--bs-list-group-action-hover-color: #fff;--bs-list-group-action-hover-bg: #434343;--bs-list-group-action-active-color: #fff;--bs-list-group-action-active-bg: #222;--bs-list-group-disabled-color: rgba(255, 255, 255, 0.75);--bs-list-group-disabled-bg: #2d2d2d;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: rgb(27, 27, 27);--bs-list-group-active-border-color: rgb(27, 27, 27);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(27, 27, 27, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: #434343;--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(255, 255, 255, 0.75);--bs-toast-header-bg: #2d2d2d;--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #2d2d2d;--bs-modal-border-color: #434343;--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #434343;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #434343;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #222;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:Lato,-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI 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.tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg);border-radius:var(--bs-tooltip-border-radius)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #2d2d2d;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem 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var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #4c76d1;color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: #7c74bb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #7c74bb;color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: #7c7a9b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) #7c7a9b;color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: #848e8b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #848e8b;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #809a8b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) #809a8b;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #1fa6bb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #00bc8c var(--bg-gradient-end, 180%)) #1fa6bb;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #2cacc0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #2cacc0;color:#fff}:root{--bslib-value-box-shadow: none;--bslib-value-box-border-width-auto-yes: var(--bslib-value-box-border-width-baseline);--bslib-value-box-border-width-auto-no: 0;--bslib-value-box-border-width-baseline: 1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #222);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box .value-box-area{justify-content:center;padding:1.5rem 1rem;font-size:.9rem;font-weight:500}.bslib-value-box .value-box-area *{margin-bottom:0;margin-top:0}.bslib-value-box .value-box-title{font-size:1rem;margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}.bslib-value-box .value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 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var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:0.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: #434343;--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: #434343;--bs-dropdown-link-hover-bg: rgba(255, 255, 255, 0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: rgb(27, 27, 27);--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:.25rem}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(1px*-1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n+3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(1px*-1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 2rem;--bs-nav-link-padding-y: 0.5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: #E09F9C;--bs-nav-link-hover-color: #b37f7d;--bs-nav-link-disabled-color: #6f6f6f;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out}@media(prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem 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.nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline .show>.nav-link{font-weight:700;color:var(--bs-nav-underline-link-active-color);border-bottom-color:currentcolor}.nav-fill>.nav-link,.nav-fill .nav-item{flex:1 1 auto;-webkit-flex:1 1 auto;text-align:center}.nav-justified>.nav-link,.nav-justified .nav-item{flex-basis:0;-webkit-flex-basis:0;flex-grow:1;-webkit-flex-grow:1;text-align:center}.nav-fill .nav-item .nav-link,.nav-justified .nav-item .nav-link{width:100%}.tab-content>.tab-pane{display:none}.tab-content>.active{display:block}.navbar{--bs-navbar-padding-x: 0;--bs-navbar-padding-y: 1rem;--bs-navbar-color: #dee2e6;--bs-navbar-hover-color: rgba(255, 255, 255, 0.8);--bs-navbar-disabled-color: rgba(222, 226, 230, 0.75);--bs-navbar-active-color: #fff;--bs-navbar-brand-padding-y: 0.3125rem;--bs-navbar-brand-margin-end: 1rem;--bs-navbar-brand-font-size: 1.25rem;--bs-navbar-brand-color: #dee2e6;--bs-navbar-brand-hover-color: #fff;--bs-navbar-nav-link-padding-x: 0.5rem;--bs-navbar-toggler-padding-y: 0.25;--bs-navbar-toggler-padding-x: 0;--bs-navbar-toggler-font-size: 1.25rem;--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23dee2e6' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e");--bs-navbar-toggler-border-color: rgba(222, 226, 230, 0);--bs-navbar-toggler-border-radius: 0.25rem;--bs-navbar-toggler-focus-width: 0.25rem;--bs-navbar-toggler-transition: box-shadow 0.15s ease-in-out;position:relative;display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-navbar-padding-y) 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23767676'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23767676'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0.75rem;--bs-breadcrumb-padding-y: 0.375rem;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: #434343;--bs-breadcrumb-border-radius: 0.25rem;--bs-breadcrumb-divider-color: rgba(255, 255, 255, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(255, 255, 255, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #fff;--bs-pagination-bg: #E09F9C;--bs-pagination-border-width: 0;--bs-pagination-border-color: transparent;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #fff;--bs-pagination-hover-bg: #ecc5c3;--bs-pagination-hover-border-color: transparent;--bs-pagination-focus-color: #b37f7d;--bs-pagination-focus-bg: #ebebeb;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(27, 27, 27, 0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #ecc5c3;--bs-pagination-active-border-color: transparent;--bs-pagination-disabled-color: #fff;--bs-pagination-disabled-bg: #ce6762;--bs-pagination-disabled-border-color: transparent;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(0*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #434343;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: rgb(27, 27, 27);--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #fff;--bs-list-group-bg: #2d2d2d;--bs-list-group-border-color: #434343;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(255, 255, 255, 0.75);--bs-list-group-action-hover-color: #fff;--bs-list-group-action-hover-bg: #434343;--bs-list-group-action-active-color: #fff;--bs-list-group-action-active-bg: #222;--bs-list-group-disabled-color: rgba(255, 255, 255, 0.75);--bs-list-group-disabled-bg: #2d2d2d;--bs-list-group-active-color: #fff;--bs-list-group-active-bg: rgb(27, 27, 27);--bs-list-group-active-border-color: rgb(27, 27, 27);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #fff;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23fff'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.4;--bs-btn-close-hover-opacity: 1;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(27, 27, 27, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: #434343;--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(255, 255, 255, 0.75);--bs-toast-header-bg: #2d2d2d;--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #2d2d2d;--bs-modal-border-color: #434343;--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #434343;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #434343;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #222;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:Lato,-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI 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.tooltip-arrow{right:calc(-1*var(--bs-tooltip-arrow-height));width:var(--bs-tooltip-arrow-height);height:var(--bs-tooltip-arrow-width)}.bs-tooltip-start .tooltip-arrow::before,.bs-tooltip-auto[data-popper-placement^=left] .tooltip-arrow::before{left:-1px;border-width:calc(var(--bs-tooltip-arrow-width)*.5) 0 calc(var(--bs-tooltip-arrow-width)*.5) var(--bs-tooltip-arrow-height);border-left-color:var(--bs-tooltip-bg)}.tooltip-inner{max-width:var(--bs-tooltip-max-width);padding:var(--bs-tooltip-padding-y) var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg);border-radius:var(--bs-tooltip-border-radius)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #2d2d2d;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem 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var(--bg-gradient-start, 36%), #6f42c1 var(--bg-gradient-end, 180%)) #4c76d1;color:#fff}.bg-gradient-cyan-pink{--bslib-color-fg: #fff;--bslib-color-bg: #7c74bb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e83e8c var(--bg-gradient-end, 180%)) #7c74bb;color:#fff}.bg-gradient-cyan-red{--bslib-color-fg: #fff;--bslib-color-bg: #7c7a9b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #e74c3c var(--bg-gradient-end, 180%)) #7c7a9b;color:#fff}.bg-gradient-cyan-orange{--bslib-color-fg: #fff;--bslib-color-bg: #848e8b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #fd7e14 var(--bg-gradient-end, 180%)) #848e8b;color:#fff}.bg-gradient-cyan-yellow{--bslib-color-fg: #fff;--bslib-color-bg: #809a8b;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #f39c12 var(--bg-gradient-end, 180%)) #809a8b;color:#fff}.bg-gradient-cyan-green{--bslib-color-fg: #fff;--bslib-color-bg: #1fa6bb;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #00bc8c var(--bg-gradient-end, 180%)) #1fa6bb;color:#fff}.bg-gradient-cyan-teal{--bslib-color-fg: #fff;--bslib-color-bg: #2cacc0;background:linear-gradient(var(--bg-gradient-deg, 140deg), #3498db var(--bg-gradient-start, 36%), #20c997 var(--bg-gradient-end, 180%)) #2cacc0;color:#fff}:root{--bslib-value-box-shadow: none;--bslib-value-box-border-width-auto-yes: var(--bslib-value-box-border-width-baseline);--bslib-value-box-border-width-auto-no: 0;--bslib-value-box-border-width-baseline: 1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #222);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box .value-box-area{justify-content:center;padding:1.5rem 1rem;font-size:.9rem;font-weight:500}.bslib-value-box .value-box-area *{margin-bottom:0;margin-top:0}.bslib-value-box .value-box-title{font-size:1rem;margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}.bslib-value-box .value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid 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var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #ffffff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 1px;position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform .3s ease-out;transform:translate(0, -50px)}@media(prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin)*2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin)*2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: 0.5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid var(--bs-modal-header-border-color);border-top-left-radius:var(--bs-modal-inner-border-radius);border-top-right-radius:var(--bs-modal-inner-border-radius)}.modal-header .btn-close{padding:calc(var(--bs-modal-header-padding-y)*.5) calc(var(--bs-modal-header-padding-x)*.5);margin:calc(-0.5*var(--bs-modal-header-padding-y)) calc(-0.5*var(--bs-modal-header-padding-x)) calc(-0.5*var(--bs-modal-header-padding-y)) auto}.modal-title{margin-bottom:0;line-height:var(--bs-modal-title-line-height)}.modal-body{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-modal-padding)}.modal-footer{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;flex-wrap:wrap;-webkit-flex-wrap:wrap;align-items:center;-webkit-align-items:center;justify-content:flex-end;-webkit-justify-content:flex-end;padding:calc(var(--bs-modal-padding) - var(--bs-modal-footer-gap)*.5);background-color:var(--bs-modal-footer-bg);border-top:var(--bs-modal-footer-border-width) solid var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap)*.5)}@media(min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media(min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media(min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media(max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-sm-down .modal-header,.modal-fullscreen-sm-down .modal-footer{border-radius:0}.modal-fullscreen-sm-down .modal-body{overflow-y:auto}}@media(max-width: 767.98px){.modal-fullscreen-md-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-md-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-md-down .modal-header,.modal-fullscreen-md-down .modal-footer{border-radius:0}.modal-fullscreen-md-down .modal-body{overflow-y:auto}}@media(max-width: 991.98px){.modal-fullscreen-lg-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-lg-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-lg-down .modal-header,.modal-fullscreen-lg-down .modal-footer{border-radius:0}.modal-fullscreen-lg-down .modal-body{overflow-y:auto}}@media(max-width: 1199.98px){.modal-fullscreen-xl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xl-down .modal-header,.modal-fullscreen-xl-down .modal-footer{border-radius:0}.modal-fullscreen-xl-down .modal-body{overflow-y:auto}}@media(max-width: 1399.98px){.modal-fullscreen-xxl-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-xxl-down .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen-xxl-down .modal-header,.modal-fullscreen-xxl-down .modal-footer{border-radius:0}.modal-fullscreen-xxl-down .modal-body{overflow-y:auto}}.tooltip{--bs-tooltip-zindex: 1080;--bs-tooltip-max-width: 200px;--bs-tooltip-padding-x: 0.5rem;--bs-tooltip-padding-y: 0.25rem;--bs-tooltip-margin: ;--bs-tooltip-font-size:0.875rem;--bs-tooltip-color: #ffffff;--bs-tooltip-bg: #000;--bs-tooltip-border-radius: 0.25rem;--bs-tooltip-opacity: 0.9;--bs-tooltip-arrow-width: 0.8rem;--bs-tooltip-arrow-height: 0.4rem;z-index:var(--bs-tooltip-zindex);display:block;margin:var(--bs-tooltip-margin);font-family:system-ui,-apple-system,"Segoe UI",Roboto,"Helvetica Neue","Noto Sans","Liberation Sans",Arial,sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";font-style:normal;font-weight:400;line-height:1.5;text-align:left;text-align:start;text-decoration:none;text-shadow:none;text-transform:none;letter-spacing:normal;word-break:normal;white-space:normal;word-spacing:normal;line-break:auto;font-size:var(--bs-tooltip-font-size);word-wrap:break-word;opacity:0}.tooltip.show{opacity:var(--bs-tooltip-opacity)}.tooltip .tooltip-arrow{display:block;width:var(--bs-tooltip-arrow-width);height:var(--bs-tooltip-arrow-height)}.tooltip .tooltip-arrow::before{position:absolute;content:"";border-color:rgba(0,0,0,0);border-style:solid}.bs-tooltip-top .tooltip-arrow,.bs-tooltip-auto[data-popper-placement^=top] 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var(--bs-tooltip-padding-x);color:var(--bs-tooltip-color);text-align:center;background-color:var(--bs-tooltip-bg);border-radius:var(--bs-tooltip-border-radius)}.popover{--bs-popover-zindex: 1070;--bs-popover-max-width: 276px;--bs-popover-font-size:0.875rem;--bs-popover-bg: #ffffff;--bs-popover-border-width: 1px;--bs-popover-border-color: rgba(0, 0, 0, 0.175);--bs-popover-border-radius: 0.5rem;--bs-popover-inner-border-radius: calc(0.5rem - 1px);--bs-popover-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-popover-header-padding-x: 1rem;--bs-popover-header-padding-y: 0.5rem;--bs-popover-header-font-size:1rem;--bs-popover-header-color: inherit;--bs-popover-header-bg: #e9ecef;--bs-popover-body-padding-x: 1rem;--bs-popover-body-padding-y: 1rem;--bs-popover-body-color: #212529;--bs-popover-arrow-width: 1rem;--bs-popover-arrow-height: 0.5rem;--bs-popover-arrow-border: 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1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #ffffff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box .value-box-showcase,.bslib-value-box .value-box-showcase>.html-fill-item{width:100%}.bslib-value-box[data-full-screen=true] .value-box-showcase{max-height:var(---bslib-value-box-showcase-max-h-fs, 100%)}@media screen and (min-width: 575.98px){@container bslib-value-box (max-width: 300px){.bslib-value-box:not(.showcase-bottom) .value-box-grid{grid-template-columns:1fr !important;grid-template-rows:auto auto;grid-template-areas:"top" "bottom"}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-showcase{grid-area:top !important}.bslib-value-box:not(.showcase-bottom) .value-box-grid .value-box-area{grid-area:bottom !important;justify-content:end}}}.bslib-value-box .value-box-area{justify-content:center;padding:1.5rem 1rem;font-size:.9rem;font-weight:500}.bslib-value-box .value-box-area *{margin-bottom:0;margin-top:0}.bslib-value-box .value-box-title{font-size:1rem;margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}.bslib-value-box .value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 50%)}.bslib-value-box.showcase-top-right .value-box-grid .value-box-showcase{grid-area:right;margin-left:auto;align-self:start;align-items:end;padding-left:0;padding-bottom:0}.bslib-value-box.showcase-top-right .value-box-grid .value-box-area{grid-area:left;align-self:end}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid{grid-template-columns:auto var(---bslib-value-box-showcase-w-fs, 1fr)}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid>div{align-self:center}.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-showcase{margin-top:0}@container bslib-value-box (max-width: 300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 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var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 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1px}.bslib-value-box{border-width:var(--bslib-value-box-border-width-auto-no, var(--bslib-value-box-border-width-baseline));container-name:bslib-value-box;container-type:inline-size}.bslib-value-box.card{box-shadow:var(--bslib-value-box-shadow)}.bslib-value-box.border-auto{border-width:var(--bslib-value-box-border-width-auto-yes, var(--bslib-value-box-border-width-baseline))}.bslib-value-box.default{--bslib-value-box-bg-default: var(--bs-card-bg, #ffffff);--bslib-value-box-border-color-default: var(--bs-card-border-color, rgba(0, 0, 0, 0.175));color:var(--bslib-value-box-color);background-color:var(--bslib-value-box-bg, var(--bslib-value-box-bg-default));border-color:var(--bslib-value-box-border-color, var(--bslib-value-box-border-color-default))}.bslib-value-box .value-box-grid{display:grid;grid-template-areas:"left right";align-items:center;overflow:hidden}.bslib-value-box .value-box-showcase{height:100%;max-height:var(---bslib-value-box-showcase-max-h, 100%)}.bslib-value-box 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.value-box-title:empty::after{content:" "}.bslib-value-box .value-box-value{font-size:calc(1.29rem + 0.48vw);margin-top:0;margin-bottom:.5rem;font-weight:500;line-height:1.2}@media(min-width: 1200px){.bslib-value-box .value-box-value{font-size:1.65rem}}.bslib-value-box .value-box-value:empty::after{content:" "}.bslib-value-box .value-box-showcase{align-items:center;justify-content:center;margin-top:auto;margin-bottom:auto;padding:1rem}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{opacity:.85;min-width:50px;max-width:125%}.bslib-value-box .value-box-showcase .bi,.bslib-value-box .value-box-showcase .fa,.bslib-value-box .value-box-showcase .fab,.bslib-value-box .value-box-showcase .fas,.bslib-value-box .value-box-showcase .far{font-size:4rem}.bslib-value-box.showcase-top-right .value-box-grid{grid-template-columns:1fr var(---bslib-value-box-showcase-w, 50%)}.bslib-value-box.showcase-top-right .value-box-grid .value-box-showcase{grid-area:right;margin-left:auto;align-self:start;align-items:end;padding-left:0;padding-bottom:0}.bslib-value-box.showcase-top-right .value-box-grid .value-box-area{grid-area:left;align-self:end}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid{grid-template-columns:auto var(---bslib-value-box-showcase-w-fs, 1fr)}.bslib-value-box.showcase-top-right[data-full-screen=true] .value-box-grid>div{align-self:center}.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-showcase{margin-top:0}@container bslib-value-box (max-width: 300px){.bslib-value-box.showcase-top-right:not([data-full-screen=true]) .value-box-grid .value-box-showcase{padding-left:1rem}}.bslib-value-box.showcase-left-center .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w, 30%) auto}.bslib-value-box.showcase-left-center[data-full-screen=true] .value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card .card-header .form-group{margin-bottom:0}.bslib-card .card-header .selectize-control{margin-bottom:0}.bslib-card .card-header .selectize-control .item{margin-right:1.15rem}.bslib-card .card-footer{margin-top:auto}.bslib-card .bslib-navs-card-title{display:flex;flex-wrap:wrap;justify-content:space-between;align-items:center}.bslib-card .bslib-navs-card-title .nav{margin-left:auto}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border=true]){border:none}.bslib-card .bslib-sidebar-layout:not([data-bslib-sidebar-border-radius=true]){border-top-left-radius:0;border-top-right-radius:0}[data-full-screen=true]{position:fixed;inset:3.5rem 1rem 1rem;height:auto !important;max-height:none !important;width:auto !important;z-index:1070}.bslib-full-screen-enter{display:none;position:absolute;bottom:var(--bslib-full-screen-enter-bottom, 0.2rem);right:var(--bslib-full-screen-enter-right, 0);top:var(--bslib-full-screen-enter-top);left:var(--bslib-full-screen-enter-left);color:var(--bslib-color-fg, var(--bs-card-color));background-color:var(--bslib-color-bg, var(--bs-card-bg, var(--bs-body-bg)));border:var(--bs-card-border-width) solid var(--bslib-color-fg, var(--bs-card-border-color));box-shadow:0 2px 4px rgba(0,0,0,.15);margin:.2rem .4rem;padding:.55rem !important;font-size:.8rem;cursor:pointer;opacity:.7;z-index:1070}.bslib-full-screen-enter:hover{opacity:1}.card[data-full-screen=false]:hover>*>.bslib-full-screen-enter{display:block}.bslib-has-full-screen .card:hover>*>.bslib-full-screen-enter{display:none}@media(max-width: 575.98px){.bslib-full-screen-enter{display:none !important}}.bslib-full-screen-exit{position:relative;top:1.35rem;font-size:.9rem;cursor:pointer;text-decoration:none;display:flex;float:right;margin-right:2.15rem;align-items:center;color:rgba(var(--bs-body-bg-rgb), 0.8)}.bslib-full-screen-exit:hover{color:rgba(var(--bs-body-bg-rgb), 1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes bslib-full-screen-overlay-enter{0%{opacity:0}100%{opacity:1}}.bslib-grid{display:grid !important;gap:var(--bslib-spacer, 1rem);height:var(--bslib-grid-height)}.bslib-grid.grid{grid-template-columns:repeat(var(--bs-columns, 12), minmax(0, 1fr));grid-template-rows:unset;grid-auto-rows:var(--bslib-grid--row-heights);--bslib-grid--row-heights--xs: unset;--bslib-grid--row-heights--sm: unset;--bslib-grid--row-heights--md: unset;--bslib-grid--row-heights--lg: unset;--bslib-grid--row-heights--xl: unset;--bslib-grid--row-heights--xxl: unset}.bslib-grid.grid.bslib-grid--row-heights--xs{--bslib-grid--row-heights: 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex 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30'%3e%3cpath stroke='%23fdfefe' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23fdfefe' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.25rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.25rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(52, 58, 64, 0.25);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #ffffff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius)}.card-header-tabs{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-bottom:calc(-1*var(--bs-card-cap-padding-y));margin-left:calc(-0.5*var(--bs-card-cap-padding-x));border-bottom:0}.card-header-tabs .nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #212529;--bs-accordion-bg: #ffffff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s ease;--bs-accordion-border-color: #dee2e6;--bs-accordion-border-width: 1px;--bs-accordion-border-radius: 0.25rem;--bs-accordion-inner-border-radius: calc(0.25rem - 1px);--bs-accordion-btn-padding-x: 1.25rem;--bs-accordion-btn-padding-y: 1rem;--bs-accordion-btn-color: #212529;--bs-accordion-btn-bg: #ffffff;--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23212529'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-icon-width: 1.25rem;--bs-accordion-btn-icon-transform: rotate(-180deg);--bs-accordion-btn-icon-transition: transform 0.2s ease-in-out;--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23052c65'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #86b7fe;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #052c65;--bs-accordion-active-bg: #cfe2ff}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.25rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.2em}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.25rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.25rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.25rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.25rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: #0d6efd;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1*var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media(min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media(min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1*var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: 0.5;--bs-btn-close-hover-opacity: 0.75;--bs-btn-close-focus-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: 0.25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:rgba(0,0,0,0) var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.25rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme=dark] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: 0.75rem;--bs-toast-padding-y: 0.5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:0.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(255, 255, 255, 0.85);--bs-toast-border-width: 1px;--bs-toast-border-color: rgba(0, 0, 0, 0.175);--bs-toast-border-radius: 0.25rem;--bs-toast-box-shadow: 0 0.5rem 1rem rgba(0, 0, 0, 0.15);--bs-toast-header-color: rgba(33, 37, 41, 0.75);--bs-toast-header-bg: rgba(255, 255, 255, 0.85);--bs-toast-header-border-color: rgba(0, 0, 0, 0.175);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-0.5*var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: 0.5rem;--bs-modal-color: ;--bs-modal-bg: #ffffff;--bs-modal-border-color: rgba(0, 0, 0, 0.175);--bs-modal-border-width: 1px;--bs-modal-border-radius: 0.5rem;--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0, 0, 0, 0.075);--bs-modal-inner-border-radius: calc(0.5rem - 1px);--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: #dee2e6;--bs-modal-header-border-width: 1px;--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: 0.5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: #dee2e6;--bs-modal-footer-border-width: 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.value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card 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1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes bslib-full-screen-overlay-enter{0%{opacity:0}100%{opacity:1}}.bslib-grid{display:grid !important;gap:var(--bslib-spacer, 1rem);height:var(--bslib-grid-height)}.bslib-grid.grid{grid-template-columns:repeat(var(--bs-columns, 12), minmax(0, 1fr));grid-template-rows:unset;grid-auto-rows:var(--bslib-grid--row-heights);--bslib-grid--row-heights--xs: unset;--bslib-grid--row-heights--sm: unset;--bslib-grid--row-heights--md: unset;--bslib-grid--row-heights--lg: unset;--bslib-grid--row-heights--xl: unset;--bslib-grid--row-heights--xxl: unset}.bslib-grid.grid.bslib-grid--row-heights--xs{--bslib-grid--row-heights: 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document&&"visibilityState"in document&&this.off(document,"visibilitychange",this.toggleVisibility_),a.prototype.dispose.call(this)},e}(li);Bt.prototype.options_={children:["loadProgressBar","playProgressBar"],barName:"playProgressBar"},q||A||Bt.prototype.options_.children.splice(1,0,"mouseTimeDisplay"),pt.registerComponent("SeekBar",Bt);Ft=function(n){function e(e,t){var i=n.call(this,e,t)||this;return i.handleMouseMove=We(Ve(ft(i),i.handleMouseMove),30),i.throttledHandleMouseSeek=We(Ve(ft(i),i.handleMouseSeek),30),i.handleMouseUpHandler_=function(e){return i.handleMouseUp(e)},i.handleMouseDownHandler_=function(e){return i.handleMouseDown(e)},i.enable(),i}mt(e,n);var t=e.prototype;return t.createEl=function(){return n.prototype.createEl.call(this,"div",{className:"vjs-progress-control vjs-control"})},t.handleMouseMove=function(e){var t,i,n,r,a=this.getChild("seekBar");a&&(t=a.getChild("playProgressBar"),i=a.getChild("mouseTimeDisplay"),(t||i)&&(r=he(n=a.el()),e=pe(n,e).x,e=cn(e,0,1),i&&i.update(r,e),t&&t.update(r,a.getProgress())))},t.handleMouseSeek=function(e){var t=this.getChild("seekBar");t&&t.handleMouseMove(e)},t.enabled=function(){return this.enabled_},t.disable=function(){var e;this.children().forEach(function(e){return e.disable&&e.disable()}),this.enabled()&&(this.off(["mousedown","touchstart"],this.handleMouseDownHandler_),this.off(this.el_,"mousemove",this.handleMouseMove),this.removeListenersAddedOnMousedownAndTouchstart(),this.addClass("disabled"),this.enabled_=!1,this.player_.scrubbing()&&(e=this.getChild("seekBar"),this.player_.scrubbing(!1),e.videoWasPlaying&&Et(this.player_.play())))},t.enable=function(){this.children().forEach(function(e){return e.enable&&e.enable()}),this.enabled()||(this.on(["mousedown","touchstart"],this.handleMouseDownHandler_),this.on(this.el_,"mousemove",this.handleMouseMove),this.removeClass("disabled"),this.enabled_=!0)},t.removeListenersAddedOnMousedownAndTouchstart=function(){var e=this.el_.ownerDocument;this.off(e,"mousemove",this.throttledHandleMouseSeek),this.off(e,"touchmove",this.throttledHandleMouseSeek),this.off(e,"mouseup",this.handleMouseUpHandler_),this.off(e,"touchend",this.handleMouseUpHandler_)},t.handleMouseDown=function(e){var t=this.el_.ownerDocument,i=this.getChild("seekBar");i&&i.handleMouseDown(e),this.on(t,"mousemove",this.throttledHandleMouseSeek),this.on(t,"touchmove",this.throttledHandleMouseSeek),this.on(t,"mouseup",this.handleMouseUpHandler_),this.on(t,"touchend",this.handleMouseUpHandler_)},t.handleMouseUp=function(e){var t=this.getChild("seekBar");t&&t.handleMouseUp(e),this.removeListenersAddedOnMousedownAndTouchstart()},e}(pt);Ft.prototype.options_={children:["seekBar"]},pt.registerComponent("ProgressControl",Ft);jt=function(n){function e(e,t){var i=n.call(this,e,t)||this;return i.on(e,["enterpictureinpicture","leavepictureinpicture"],function(e){return i.handlePictureInPictureChange(e)}),i.on(e,["disablepictureinpicturechanged","loadedmetadata"],function(e){return i.handlePictureInPictureEnabledChange(e)}),i.on(e,["loadedmetadata","audioonlymodechange","audiopostermodechange"],function(){"audio"===e.currentType().substring(0,5)||e.audioPosterMode()||e.audioOnlyMode()?(e.isInPictureInPicture()&&e.exitPictureInPicture(),i.hide()):i.show()}),i.disable(),i}mt(e,n);var t=e.prototype;return t.buildCSSClass=function(){return"vjs-picture-in-picture-control "+n.prototype.buildCSSClass.call(this)},t.handlePictureInPictureEnabledChange=function(){document.pictureInPictureEnabled&&!1===this.player_.disablePictureInPicture()?this.enable():this.disable()},t.handlePictureInPictureChange=function(e){this.player_.isInPictureInPicture()?this.controlText("Exit Picture-in-Picture"):this.controlText("Picture-in-Picture"),this.handlePictureInPictureEnabledChange()},t.handleClick=function(e){this.player_.isInPictureInPicture()?this.player_.exitPictureInPicture():this.player_.requestPictureInPicture()},e}(sn);jt.prototype.controlText_="Picture-in-Picture",pt.registerComponent("PictureInPictureToggle",jt);j=function(n){function e(e,t){var i=n.call(this,e,t)||this;return i.on(e,"fullscreenchange",function(e){return i.handleFullscreenChange(e)}),!1===document[e.fsApi_.fullscreenEnabled]&&i.disable(),i}mt(e,n);var t=e.prototype;return t.buildCSSClass=function(){return"vjs-fullscreen-control "+n.prototype.buildCSSClass.call(this)},t.handleFullscreenChange=function(e){this.player_.isFullscreen()?this.controlText("Non-Fullscreen"):this.controlText("Fullscreen")},t.handleClick=function(e){this.player_.isFullscreen()?this.player_.exitFullscreen():this.player_.requestFullscreen()},e}(sn);j.prototype.controlText_="Fullscreen",pt.registerComponent("FullscreenToggle",j);pt.registerComponent("VolumeLevel",function(t){function e(){return t.apply(this,arguments)||this}return mt(e,t),e.prototype.createEl=function(){var e=t.prototype.createEl.call(this,"div",{className:"vjs-volume-level"});return e.appendChild(t.prototype.createEl.call(this,"span",{className:"vjs-control-text"})),e},e}(pt)),pt.registerComponent("VolumeLevelTooltip",function(i){function e(e,t){t=i.call(this,e,t)||this;return t.update=We(Ve(ft(t),t.update),30),t}mt(e,i);var t=e.prototype;return t.createEl=function(){return i.prototype.createEl.call(this,"div",{className:"vjs-volume-tooltip"},{"aria-hidden":"true"})},t.update=function(e,t,i,n){if(!i){var r=de(this.el_),a=de(this.player_.el()),i=e.width*t;if(!a||!r)return;t=e.left-a.left+i,a=e.width-i+(a.right-e.right),e=r.width/2;tr.width&&(e=r.width),this.el_.style.right="-"+e+"px"}this.write(n+"%")},t.write=function(e){J(this.el_,e)},t.updateVolume=function(e,t,i,n,r){var a=this;this.requestNamedAnimationFrame("VolumeLevelTooltip#updateVolume",function(){a.update(e,t,i,n.toFixed(0)),r&&r()})},e}(pt));k=function(i){function e(e,t){t=i.call(this,e,t)||this;return t.update=We(Ve(ft(t),t.update),30),t}mt(e,i);var t=e.prototype;return t.createEl=function(){return i.prototype.createEl.call(this,"div",{className:"vjs-mouse-display"})},t.update=function(e,t,i){var n=this,r=100*t;this.getChild("volumeLevelTooltip").updateVolume(e,t,i,r,function(){i?n.el_.style.bottom=e.height*t+"px":n.el_.style.left=e.width*t+"px"})},e}(pt);k.prototype.options_={children:["volumeLevelTooltip"]},pt.registerComponent("MouseVolumeLevelDisplay",k);f=function(n){function e(e,t){var i=n.call(this,e,t)||this;return i.on("slideractive",function(e){return i.updateLastVolume_(e)}),i.on(e,"volumechange",function(e){return i.updateARIAAttributes(e)}),e.ready(function(){return i.updateARIAAttributes()}),i}mt(e,n);var t=e.prototype;return t.createEl=function(){return n.prototype.createEl.call(this,"div",{className:"vjs-volume-bar vjs-slider-bar"},{"aria-label":this.localize("Volume Level"),"aria-live":"polite"})},t.handleMouseDown=function(e){_e(e)&&n.prototype.handleMouseDown.call(this,e)},t.handleMouseMove=function(e){var t,i,n,r=this.getChild("mouseVolumeLevelDisplay");r&&(t=de(n=this.el()),i=this.vertical(),n=pe(n,e),n=i?n.y:n.x,n=cn(n,0,1),r.update(t,n,i)),_e(e)&&(this.checkMuted(),this.player_.volume(this.calculateDistance(e)))},t.checkMuted=function(){this.player_.muted()&&this.player_.muted(!1)},t.getPercent=function(){return this.player_.muted()?0:this.player_.volume()},t.stepForward=function(){this.checkMuted(),this.player_.volume(this.player_.volume()+.1)},t.stepBack=function(){this.checkMuted(),this.player_.volume(this.player_.volume()-.1)},t.updateARIAAttributes=function(e){var t=this.player_.muted()?0:this.volumeAsPercentage_();this.el_.setAttribute("aria-valuenow",t),this.el_.setAttribute("aria-valuetext",t+"%")},t.volumeAsPercentage_=function(){return Math.round(100*this.player_.volume())},t.updateLastVolume_=function(){var e=this,t=this.player_.volume();this.one("sliderinactive",function(){0===e.player_.volume()&&e.player_.lastVolume_(t)})},e}(li);f.prototype.options_={children:["volumeLevel"],barName:"volumeLevel"},q||A||f.prototype.options_.children.splice(0,0,"mouseVolumeLevelDisplay"),f.prototype.playerEvent="volumechange",pt.registerComponent("VolumeBar",f);ui=function(a){function e(e,t){var i,n,r;return(t=void 0===t?{}:t).vertical=t.vertical||!1,"undefined"!=typeof t.volumeBar&&!S(t.volumeBar)||(t.volumeBar=t.volumeBar||{},t.volumeBar.vertical=t.vertical),i=a.call(this,e,t)||this,n=ft(i),(r=e).tech_&&!r.tech_.featuresVolumeControl&&n.addClass("vjs-hidden"),n.on(r,"loadstart",function(){r.tech_.featuresVolumeControl?n.removeClass("vjs-hidden"):n.addClass("vjs-hidden")}),i.throttledHandleMouseMove=We(Ve(ft(i),i.handleMouseMove),30),i.handleMouseUpHandler_=function(e){return i.handleMouseUp(e)},i.on("mousedown",function(e){return i.handleMouseDown(e)}),i.on("touchstart",function(e){return i.handleMouseDown(e)}),i.on("mousemove",function(e){return i.handleMouseMove(e)}),i.on(i.volumeBar,["focus","slideractive"],function(){i.volumeBar.addClass("vjs-slider-active"),i.addClass("vjs-slider-active"),i.trigger("slideractive")}),i.on(i.volumeBar,["blur","sliderinactive"],function(){i.volumeBar.removeClass("vjs-slider-active"),i.removeClass("vjs-slider-active"),i.trigger("sliderinactive")}),i}mt(e,a);var t=e.prototype;return t.createEl=function(){var e="vjs-volume-horizontal";return this.options_.vertical&&(e="vjs-volume-vertical"),a.prototype.createEl.call(this,"div",{className:"vjs-volume-control vjs-control "+e})},t.handleMouseDown=function(e){var t=this.el_.ownerDocument;this.on(t,"mousemove",this.throttledHandleMouseMove),this.on(t,"touchmove",this.throttledHandleMouseMove),this.on(t,"mouseup",this.handleMouseUpHandler_),this.on(t,"touchend",this.handleMouseUpHandler_)},t.handleMouseUp=function(e){var t=this.el_.ownerDocument;this.off(t,"mousemove",this.throttledHandleMouseMove),this.off(t,"touchmove",this.throttledHandleMouseMove),this.off(t,"mouseup",this.handleMouseUpHandler_),this.off(t,"touchend",this.handleMouseUpHandler_)},t.handleMouseMove=function(e){this.volumeBar.handleMouseMove(e)},e}(pt);ui.prototype.options_={children:["volumeBar"]},pt.registerComponent("VolumeControl",ui);Xt=function(a){function e(e,t){var i,n,r=a.call(this,e,t)||this;return i=ft(r),(n=e).tech_&&!n.tech_.featuresMuteControl&&i.addClass("vjs-hidden"),i.on(n,"loadstart",function(){n.tech_.featuresMuteControl?i.removeClass("vjs-hidden"):i.addClass("vjs-hidden")}),r.on(e,["loadstart","volumechange"],function(e){return r.update(e)}),r}mt(e,a);var t=e.prototype;return t.buildCSSClass=function(){return"vjs-mute-control "+a.prototype.buildCSSClass.call(this)},t.handleClick=function(e){var t=this.player_.volume(),i=this.player_.lastVolume_();0===t?(this.player_.volume(i<.1?.1:i),this.player_.muted(!1)):this.player_.muted(!this.player_.muted())},t.update=function(e){this.updateIcon_(),this.updateControlText_()},t.updateIcon_=function(){var e=this.player_.volume(),t=3;q&&this.player_.tech_&&this.player_.tech_.el_&&this.player_.muted(this.player_.tech_.el_.muted),0===e||this.player_.muted()?t=0:e<.33?t=1:e<.67&&(t=2);for(var i=0;i<4;i++)ie(this.el_,"vjs-vol-"+i);te(this.el_,"vjs-vol-"+t)},t.updateControlText_=function(){var e=this.player_.muted()||0===this.player_.volume()?"Unmute":"Mute";this.controlText()!==e&&this.controlText(e)},e}(sn);Xt.prototype.controlText_="Mute",pt.registerComponent("MuteToggle",Xt);I=function(n){function e(e,t){var i;return"undefined"!=typeof(t=void 0===t?{}:t).inline?t.inline=t.inline:t.inline=!0,"undefined"!=typeof t.volumeControl&&!S(t.volumeControl)||(t.volumeControl=t.volumeControl||{},t.volumeControl.vertical=!t.inline),(i=n.call(this,e,t)||this).handleKeyPressHandler_=function(e){return i.handleKeyPress(e)},i.on(e,["loadstart"],function(e){return i.volumePanelState_(e)}),i.on(i.muteToggle,"keyup",function(e){return i.handleKeyPress(e)}),i.on(i.volumeControl,"keyup",function(e){return i.handleVolumeControlKeyUp(e)}),i.on("keydown",function(e){return i.handleKeyPress(e)}),i.on("mouseover",function(e){return i.handleMouseOver(e)}),i.on("mouseout",function(e){return i.handleMouseOut(e)}),i.on(i.volumeControl,["slideractive"],i.sliderActive_),i.on(i.volumeControl,["sliderinactive"],i.sliderInactive_),i}mt(e,n);var t=e.prototype;return t.sliderActive_=function(){this.addClass("vjs-slider-active")},t.sliderInactive_=function(){this.removeClass("vjs-slider-active")},t.volumePanelState_=function(){this.volumeControl.hasClass("vjs-hidden")&&this.muteToggle.hasClass("vjs-hidden")&&this.addClass("vjs-hidden"),this.volumeControl.hasClass("vjs-hidden")&&!this.muteToggle.hasClass("vjs-hidden")&&this.addClass("vjs-mute-toggle-only")},t.createEl=function(){var e="vjs-volume-panel-horizontal";return this.options_.inline||(e="vjs-volume-panel-vertical"),n.prototype.createEl.call(this,"div",{className:"vjs-volume-panel vjs-control "+e})},t.dispose=function(){this.handleMouseOut(),n.prototype.dispose.call(this)},t.handleVolumeControlKeyUp=function(e){ht.isEventKey(e,"Esc")&&this.muteToggle.focus()},t.handleMouseOver=function(e){this.addClass("vjs-hover"),Be(document,"keyup",this.handleKeyPressHandler_)},t.handleMouseOut=function(e){this.removeClass("vjs-hover"),Fe(document,"keyup",this.handleKeyPressHandler_)},t.handleKeyPress=function(e){ht.isEventKey(e,"Esc")&&this.handleMouseOut()},e}(pt);I.prototype.options_={children:["muteToggle","volumeControl"]},pt.registerComponent("VolumePanel",I);var hn=function(n){function e(e,t){var i=n.call(this,e,t)||this;return t&&(i.menuButton_=t.menuButton),i.focusedChild_=-1,i.on("keydown",function(e){return i.handleKeyDown(e)}),i.boundHandleBlur_=function(e){return i.handleBlur(e)},i.boundHandleTapClick_=function(e){return i.handleTapClick(e)},i}mt(e,n);var t=e.prototype;return t.addEventListenerForItem=function(e){e instanceof pt&&(this.on(e,"blur",this.boundHandleBlur_),this.on(e,["tap","click"],this.boundHandleTapClick_))},t.removeEventListenerForItem=function(e){e instanceof pt&&(this.off(e,"blur",this.boundHandleBlur_),this.off(e,["tap","click"],this.boundHandleTapClick_))},t.removeChild=function(e){"string"==typeof e&&(e=this.getChild(e)),this.removeEventListenerForItem(e),n.prototype.removeChild.call(this,e)},t.addItem=function(e){e=this.addChild(e);e&&this.addEventListenerForItem(e)},t.createEl=function(){var e=this.options_.contentElType||"ul";this.contentEl_=$(e,{className:"vjs-menu-content"}),this.contentEl_.setAttribute("role","menu");e=n.prototype.createEl.call(this,"div",{append:this.contentEl_,className:"vjs-menu"});return e.appendChild(this.contentEl_),Be(e,"click",function(e){e.preventDefault(),e.stopImmediatePropagation()}),e},t.dispose=function(){this.contentEl_=null,this.boundHandleBlur_=null,this.boundHandleTapClick_=null,n.prototype.dispose.call(this)},t.handleBlur=function(e){var t=e.relatedTarget||document.activeElement;this.children().some(function(e){return e.el()===t})||(e=this.menuButton_)&&e.buttonPressed_&&t!==e.el().firstChild&&e.unpressButton()},t.handleTapClick=function(t){var e;this.menuButton_&&(this.menuButton_.unpressButton(),e=this.children(),!Array.isArray(e)||(e=e.filter(function(e){return e.el()===t.target})[0])&&"CaptionSettingsMenuItem"!==e.name()&&this.menuButton_.focus())},t.handleKeyDown=function(e){ht.isEventKey(e,"Left")||ht.isEventKey(e,"Down")?(e.preventDefault(),e.stopPropagation(),this.stepForward()):(ht.isEventKey(e,"Right")||ht.isEventKey(e,"Up"))&&(e.preventDefault(),e.stopPropagation(),this.stepBack())},t.stepForward=function(){var e=0;void 0!==this.focusedChild_&&(e=this.focusedChild_+1),this.focus(e)},t.stepBack=function(){var e=0;void 0!==this.focusedChild_&&(e=this.focusedChild_-1),this.focus(e)},t.focus=function(e){void 0===e&&(e=0);var t=this.children().slice();t.length&&t[0].hasClass("vjs-menu-title")&&t.shift(),0=t.length&&(e=t.length-1),t[this.focusedChild_=e].el_.focus())},e}(pt);pt.registerComponent("Menu",hn);Bt=function(n){function e(e,t){var i;(i=n.call(this,e,t=void 0===t?{}:t)||this).menuButton_=new sn(e,t),i.menuButton_.controlText(i.controlText_),i.menuButton_.el_.setAttribute("aria-haspopup","true");t=sn.prototype.buildCSSClass();i.menuButton_.el_.className=i.buildCSSClass()+" "+t,i.menuButton_.removeClass("vjs-control"),i.addChild(i.menuButton_),i.update(),i.enabled_=!0;t=function(e){return i.handleClick(e)};return i.handleMenuKeyUp_=function(e){return i.handleMenuKeyUp(e)},i.on(i.menuButton_,"tap",t),i.on(i.menuButton_,"click",t),i.on(i.menuButton_,"keydown",function(e){return i.handleKeyDown(e)}),i.on(i.menuButton_,"mouseenter",function(){i.addClass("vjs-hover"),i.menu.show(),Be(document,"keyup",i.handleMenuKeyUp_)}),i.on("mouseleave",function(e){return i.handleMouseLeave(e)}),i.on("keydown",function(e){return i.handleSubmenuKeyDown(e)}),i}mt(e,n);var t=e.prototype;return t.update=function(){var e=this.createMenu();this.menu&&(this.menu.dispose(),this.removeChild(this.menu)),this.menu=e,this.addChild(e),this.buttonPressed_=!1,this.menuButton_.el_.setAttribute("aria-expanded","false"),this.items&&this.items.length<=this.hideThreshold_?(this.hide(),this.menu.contentEl_.removeAttribute("role")):(this.show(),this.menu.contentEl_.setAttribute("role","menu"))},t.createMenu=function(){var e,t=new hn(this.player_,{menuButton:this});if(this.hideThreshold_=0,this.options_.title&&(e=$("li",{className:"vjs-menu-title",textContent:ut(this.options_.title),tabIndex:-1}),e=new pt(this.player_,{el:e}),t.addItem(e)),this.items=this.createItems(),this.items)for(var i=0;i select",id:"captions-background-color-%s",label:"Color",options:[ui,Bt,jt,Ft,j,C,I,Xt]},backgroundOpacity:{selector:".vjs-bg-opacity > select",id:"captions-background-opacity-%s",label:"Transparency",options:[k,li,f]},color:{selector:".vjs-fg-color > select",id:"captions-foreground-color-%s",label:"Color",options:[Bt,ui,jt,Ft,j,C,I,Xt]},edgeStyle:{selector:".vjs-edge-style > select",id:"%s",label:"Text Edge Style",options:[["none","None"],["raised","Raised"],["depressed","Depressed"],["uniform","Uniform"],["dropshadow","Dropshadow"]]},fontFamily:{selector:".vjs-font-family > select",id:"captions-font-family-%s",label:"Font Family",options:[["proportionalSansSerif","Proportional Sans-Serif"],["monospaceSansSerif","Monospace Sans-Serif"],["proportionalSerif","Proportional Serif"],["monospaceSerif","Monospace Serif"],["casual","Casual"],["script","Script"],["small-caps","Small Caps"]]},fontPercent:{selector:".vjs-font-percent > select",id:"captions-font-size-%s",label:"Font Size",options:[["0.50","50%"],["0.75","75%"],["1.00","100%"],["1.25","125%"],["1.50","150%"],["1.75","175%"],["2.00","200%"],["3.00","300%"],["4.00","400%"]],default:2,parser:function(e){return"1.00"===e?null:Number(e)}},textOpacity:{selector:".vjs-text-opacity > select",id:"captions-foreground-opacity-%s",label:"Transparency",options:[k,li]},windowColor:{selector:".vjs-window-color > select",id:"captions-window-color-%s",label:"Color"},windowOpacity:{selector:".vjs-window-opacity > select",id:"captions-window-opacity-%s",label:"Transparency",options:[f,li,k]}};function wn(e,t){if((e=t?t(e):e)&&"none"!==e)return e}Sn.windowColor.options=Sn.backgroundColor.options,pt.registerComponent("TextTrackSettings",function(n){function e(e,t){var i;return t.temporary=!1,(i=n.call(this,e,t)||this).updateDisplay=i.updateDisplay.bind(ft(i)),i.fill(),i.hasBeenOpened_=i.hasBeenFilled_=!0,i.endDialog=$("p",{className:"vjs-control-text",textContent:i.localize("End of dialog window.")}),i.el().appendChild(i.endDialog),i.setDefaults(),void 0===t.persistTextTrackSettings&&(i.options_.persistTextTrackSettings=i.options_.playerOptions.persistTextTrackSettings),i.on(i.$(".vjs-done-button"),"click",function(){i.saveSettings(),i.close()}),i.on(i.$(".vjs-default-button"),"click",function(){i.setDefaults(),i.updateDisplay()}),_(Sn,function(e){i.on(i.$(e.selector),"change",i.updateDisplay)}),i.options_.persistTextTrackSettings&&i.restoreSettings(),i}mt(e,n);var t=e.prototype;return t.dispose=function(){this.endDialog=null,n.prototype.dispose.call(this)},t.createElSelect_=function(e,t,i){var n=this;void 0===t&&(t=""),void 0===i&&(i="label");var e=Sn[e],r=e.id.replace("%s",this.id_),a=[t,r].join(" ").trim();return["<"+i+' id="'+r+'" class="'+("label"===i?"vjs-label":"")+'">',this.localize(e.label),"",'").join("")},t.createElFgColor_=function(){var e="captions-text-legend-"+this.id_;return['
    ','',this.localize("Text"),"",this.createElSelect_("color",e),'',this.createElSelect_("textOpacity",e),"","
    "].join("")},t.createElBgColor_=function(){var e="captions-background-"+this.id_;return['
    ','',this.localize("Background"),"",this.createElSelect_("backgroundColor",e),'',this.createElSelect_("backgroundOpacity",e),"","
    "].join("")},t.createElWinColor_=function(){var e="captions-window-"+this.id_;return['
    ','',this.localize("Window"),"",this.createElSelect_("windowColor",e),'',this.createElSelect_("windowOpacity",e),"","
    "].join("")},t.createElColors_=function(){return $("div",{className:"vjs-track-settings-colors",innerHTML:[this.createElFgColor_(),this.createElBgColor_(),this.createElWinColor_()].join("")})},t.createElFont_=function(){return $("div",{className:"vjs-track-settings-font",innerHTML:['
    ',this.createElSelect_("fontPercent","","legend"),"
    ",'
    ',this.createElSelect_("edgeStyle","","legend"),"
    ",'
    ',this.createElSelect_("fontFamily","","legend"),"
    "].join("")})},t.createElControls_=function(){var e=this.localize("restore all settings to the default values");return $("div",{className:"vjs-track-settings-controls",innerHTML:['",'"].join("")})},t.content=function(){return[this.createElColors_(),this.createElFont_(),this.createElControls_()]},t.label=function(){return this.localize("Caption Settings Dialog")},t.description=function(){return this.localize("Beginning of dialog window. 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u.apply(null,[T.moov,l(4294967295)].concat(i).concat(o(e)))},o=function(e){for(var t=e.length,i=[];t--;)i[t]=_(e[t]);return u.apply(null,[T.mvex].concat(i))},l=function(e){e=new Uint8Array([0,0,0,0,0,0,0,1,0,0,0,2,0,1,95,144,(4278190080&e)>>24,(16711680&e)>>16,(65280&e)>>8,255&e,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,255,255,255,255]);return u(T.mvhd,e)},m=function(e){for(var t,i=e.samples||[],n=new Uint8Array(4+i.length),r=0;r>>8),a.push(255&n[o].byteLength),a=a.concat(Array.prototype.slice.call(n[o]));for(o=0;o>>8),s.push(255&r[o].byteLength),s=s.concat(Array.prototype.slice.call(r[o]));return t=[T.avc1,new Uint8Array([0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,(65280&e.width)>>8,255&e.width,(65280&e.height)>>8,255&e.height,0,72,0,0,0,72,0,0,0,0,0,0,0,1,19,118,105,100,101,111,106,115,45,99,111,110,116,114,105,98,45,104,108,115,0,0,0,0,0,0,0,0,0,0,0,0,0,24,17,17]),u(T.avcC,new Uint8Array([1,e.profileIdc,e.profileCompatibility,e.levelIdc,255].concat([n.length],a,[r.length],s))),u(T.btrt,new Uint8Array([0,28,156,128,0,45,198,192,0,45,198,192]))],e.sarRatio&&(i=e.sarRatio[0],e=e.sarRatio[1],t.push(u(T.pasp,new Uint8Array([(4278190080&i)>>24,(16711680&i)>>16,(65280&i)>>8,255&i,(4278190080&e)>>24,(16711680&e)>>16,(65280&e)>>8,255&e])))),u.apply(null,t)},N=function(e){return u(T.mp4a,new Uint8Array([0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,(65280&e.channelcount)>>8,255&e.channelcount,(65280&e.samplesize)>>8,255&e.samplesize,0,0,0,0,(65280&e.samplerate)>>8,255&e.samplerate,0,0]),i(e))},d=function(e){e=new Uint8Array([0,0,0,7,0,0,0,0,0,0,0,0,(4278190080&e.id)>>24,(16711680&e.id)>>16,(65280&e.id)>>8,255&e.id,0,0,0,0,(4278190080&e.duration)>>24,(16711680&e.duration)>>16,(65280&e.duration)>>8,255&e.duration,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,64,0,0,0,(65280&e.width)>>8,255&e.width,0,0,(65280&e.height)>>8,255&e.height,0,0]);return u(T.tkhd,e)},v=function(e){var t,i=u(T.tfhd,new Uint8Array([0,0,0,58,(4278190080&e.id)>>24,(16711680&e.id)>>16,(65280&e.id)>>8,255&e.id,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0])),n=Math.floor(e.baseMediaDecodeTime/V),r=Math.floor(e.baseMediaDecodeTime%V),n=u(T.tfdt,new Uint8Array([1,0,0,0,n>>>24&255,n>>>16&255,n>>>8&255,255&n,r>>>24&255,r>>>16&255,r>>>8&255,255&r]));return"audio"===e.type?(t=b(e,92),u(T.traf,i,n,t)):(r=m(e),t=b(e,r.length+92),u(T.traf,i,n,t,r))},c=function(e){return e.duration=e.duration||4294967295,u(T.trak,d(e),h(e))},_=function(e){var t=new Uint8Array([0,0,0,0,(4278190080&e.id)>>24,(16711680&e.id)>>16,(65280&e.id)>>8,255&e.id,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,1]);return"video"!==e.type&&(t[t.length-1]=0),u(T.trex,t)},U=function(e,t){var i=0,n=0,r=0,a=0;return e.length&&(void 0!==e[0].duration&&(i=1),void 0!==e[0].size&&(n=2),void 0!==e[0].flags&&(r=4),void 0!==e[0].compositionTimeOffset&&(a=8)),[0,0,i|n|r|a,1,(4278190080&e.length)>>>24,(16711680&e.length)>>>16,(65280&e.length)>>>8,255&e.length,(4278190080&t)>>>24,(16711680&t)>>>16,(65280&t)>>>8,255&t]},B=function(e,t){var i,n,r,a,s=e.samples||[];for(t+=20+16*s.length,t=U(s,t),(n=new Uint8Array(t.length+16*s.length)).set(t),i=t.length,a=0;a>>24,n[i++]=(16711680&r.duration)>>>16,n[i++]=(65280&r.duration)>>>8,n[i++]=255&r.duration,n[i++]=(4278190080&r.size)>>>24,n[i++]=(16711680&r.size)>>>16,n[i++]=(65280&r.size)>>>8,n[i++]=255&r.size,n[i++]=r.flags.isLeading<<2|r.flags.dependsOn,n[i++]=r.flags.isDependedOn<<6|r.flags.hasRedundancy<<4|r.flags.paddingValue<<1|r.flags.isNonSyncSample,n[i++]=61440&r.flags.degradationPriority,n[i++]=15&r.flags.degradationPriority,n[i++]=(4278190080&r.compositionTimeOffset)>>>24,n[i++]=(16711680&r.compositionTimeOffset)>>>16,n[i++]=(65280&r.compositionTimeOffset)>>>8,n[i++]=255&r.compositionTimeOffset;return u(T.trun,n)},F=function(e,t){var i,n,r,a,s=e.samples||[];for(t+=20+8*s.length,t=U(s,t),(i=new Uint8Array(t.length+8*s.length)).set(t),n=t.length,a=0;a>>24,i[n++]=(16711680&r.duration)>>>16,i[n++]=(65280&r.duration)>>>8,i[n++]=255&r.duration,i[n++]=(4278190080&r.size)>>>24,i[n++]=(16711680&r.size)>>>16,i[n++]=(65280&r.size)>>>8,i[n++]=255&r.size;return u(T.trun,i)},b=function(e,t){return("audio"===e.type?F:B)(e,t)};n=function(){return u(T.ftyp,S,w,S,E)};function W(e,t){var i={size:0,flags:{isLeading:0,dependsOn:1,isDependedOn:0,hasRedundancy:0,degradationPriority:0,isNonSyncSample:1}};return i.dataOffset=t,i.compositionTimeOffset=e.pts-e.dts,i.duration=e.duration,i.size=4*e.length,i.size+=e.byteLength,e.keyFrame&&(i.flags.dependsOn=2,i.flags.isNonSyncSample=0),i}function G(e){for(var t=[];e--;)t.push(0);return t}function z(){var e,i;return X||(e={96e3:[ie,[227,64],G(154),[56]],88200:[ie,[231],G(170),[56]],64e3:[ie,[248,192],G(240),[56]],48e3:[ie,[255,192],G(268),[55,148,128],G(54),[112]],44100:[ie,[255,192],G(268),[55,163,128],G(84),[112]],32e3:[ie,[255,192],G(268),[55,234],G(226),[112]],24e3:[ie,[255,192],G(268),[55,255,128],G(268),[111,112],G(126),[224]],16e3:[ie,[255,192],G(268),[55,255,128],G(268),[111,255],G(269),[223,108],G(195),[1,192]],12e3:[ne,G(268),[3,127,248],G(268),[6,255,240],G(268),[13,255,224],G(268),[27,253,128],G(259),[56]],11025:[ne,G(268),[3,127,248],G(268),[6,255,240],G(268),[13,255,224],G(268),[27,255,192],G(268),[55,175,128],G(108),[112]],8e3:[ne,G(268),[3,121,16],G(47),[7]]},i=e,X=Object.keys(i).reduce(function(e,t){return e[t]=new Uint8Array(i[t].reduce(function(e,t){return e.concat(t)},[])),e},{})),X}var X,K=function(e){return u(T.mdat,e)},Y=We,Q=function(e){var t=n(),i=s(e),e=new Uint8Array(t.byteLength+i.byteLength);return e.set(t),e.set(i,t.byteLength),e},$=function(e){var t,i,n=[],r=[];for(r.byteLength=0,r.nalCount=0,r.duration=0,t=n.byteLength=0;t=i?e:(t.minSegmentDts=1/0,e.filter(function(e){return e.dts>=i&&(t.minSegmentDts=Math.min(t.minSegmentDts,e.dts),t.minSegmentPts=t.minSegmentDts,!0)}))},ge=function(e){for(var t,i=[],n=0;n=this.virtualRowCount&&"function"==typeof this.beforeRowOverflow&&this.beforeRowOverflow(e),0this.virtualRowCount;)this.rows.shift(),this.rowIdx--},Ae.prototype.isEmpty=function(){return 0===this.rows.length||1===this.rows.length&&""===this.rows[0]},Ae.prototype.addText=function(e){this.rows[this.rowIdx]+=e},Ae.prototype.backspace=function(){var e;this.isEmpty()||(e=this.rows[this.rowIdx],this.rows[this.rowIdx]=e.substr(0,e.length-1))};function Le(e,t,i){this.serviceNum=e,this.text="",this.currentWindow=new Ae(-1),this.windows=[],this.stream=i,"string"==typeof t&&this.createTextDecoder(t)}Le.prototype.init=function(e,t){this.startPts=e;for(var i=0;i<8;i++)this.windows[i]=new Ae(i),"function"==typeof t&&(this.windows[i].beforeRowOverflow=t)},Le.prototype.setCurrentWindow=function(e){this.currentWindow=this.windows[e]},Le.prototype.createTextDecoder=function(t){if("undefined"==typeof TextDecoder)this.stream.trigger("log",{level:"warn",message:"The `encoding` option is unsupported without TextDecoder support"});else try{this.textDecoder_=new TextDecoder(t)}catch(e){this.stream.trigger("log",{level:"warn",message:"TextDecoder could not be created with "+t+" encoding. "+e})}};var De=function e(t){t=t||{},e.prototype.init.call(this);var i,n=this,r=t.captionServices||{},a={};Object.keys(r).forEach(function(e){i=r[e],/^SERVICE/.test(e)&&(a[e]=i.encoding)}),this.serviceEncodings=a,this.current708Packet=null,this.services={},this.push=function(e){(3===e.type||null===n.current708Packet)&&n.new708Packet(),n.add708Bytes(e)}};De.prototype=new j,De.prototype.new708Packet=function(){null!==this.current708Packet&&this.push708Packet(),this.current708Packet={data:[],ptsVals:[]}},De.prototype.add708Bytes=function(e){var t=e.ccData,i=t>>>8,t=255&t;this.current708Packet.ptsVals.push(e.pts),this.current708Packet.data.push(i),this.current708Packet.data.push(t)},De.prototype.push708Packet=function(){var e,t=this.current708Packet,i=t.data,n=null,r=0,a=i[r++];for(t.seq=a>>6,t.sizeCode=63&a;r>5)&&0>5,t.rowLock=(16&n)>>4,t.columnLock=(8&n)>>3,t.priority=7&n,n=i[++e],t.relativePositioning=(128&n)>>7,t.anchorVertical=127&n,n=i[++e],t.anchorHorizontal=n,n=i[++e],t.anchorPoint=(240&n)>>4,t.rowCount=15&n,n=i[++e],t.columnCount=63&n,n=i[++e],t.windowStyle=(56&n)>>3,t.penStyle=7&n,t.virtualRowCount=t.rowCount+1,e},De.prototype.setWindowAttributes=function(e,t){var i=this.current708Packet.data,n=i[e],t=t.currentWindow.winAttr,n=i[++e];return t.fillOpacity=(192&n)>>6,t.fillRed=(48&n)>>4,t.fillGreen=(12&n)>>2,t.fillBlue=3&n,n=i[++e],t.borderType=(192&n)>>6,t.borderRed=(48&n)>>4,t.borderGreen=(12&n)>>2,t.borderBlue=3&n,n=i[++e],t.borderType+=(128&n)>>5,t.wordWrap=(64&n)>>6,t.printDirection=(48&n)>>4,t.scrollDirection=(12&n)>>2,t.justify=3&n,n=i[++e],t.effectSpeed=(240&n)>>4,t.effectDirection=(12&n)>>2,t.displayEffect=3&n,e},De.prototype.flushDisplayed=function(e,t){for(var i=[],n=0;n<8;n++)t.windows[n].visible&&!t.windows[n].isEmpty()&&i.push(t.windows[n].getText());t.endPts=e,t.text=i.join("\n\n"),this.pushCaption(t),t.startPts=e},De.prototype.pushCaption=function(e){""!==e.text&&(this.trigger("data",{startPts:e.startPts,endPts:e.endPts,text:e.text,stream:"cc708_"+e.serviceNum}),e.text="",e.startPts=e.endPts)},De.prototype.displayWindows=function(e,t){var i=this.current708Packet.data[++e],n=this.getPts(e);this.flushDisplayed(n,t);for(var r=0;r<8;r++)i&1<>4,t.offset=(12&n)>>2,t.penSize=3&n,n=i[++e],t.italics=(128&n)>>7,t.underline=(64&n)>>6,t.edgeType=(56&n)>>3,t.fontStyle=7&n,e},De.prototype.setPenColor=function(e,t){var i=this.current708Packet.data,n=i[e],t=t.currentWindow.penColor,n=i[++e];return t.fgOpacity=(192&n)>>6,t.fgRed=(48&n)>>4,t.fgGreen=(12&n)>>2,t.fgBlue=3&n,n=i[++e],t.bgOpacity=(192&n)>>6,t.bgRed=(48&n)>>4,t.bgGreen=(12&n)>>2,t.bgBlue=3&n,n=i[++e],t.edgeRed=(48&n)>>4,t.edgeGreen=(12&n)>>2,t.edgeBlue=3&n,e},De.prototype.setPenLocation=function(e,t){var i=this.current708Packet.data,n=i[e],r=t.currentWindow.penLoc;return t.currentWindow.pendingNewLine=!0,n=i[++e],r.row=15&n,n=i[++e],r.column=63&n,e},De.prototype.reset=function(e,t){var i=this.getPts(e);return this.flushDisplayed(i,t),this.initService(t.serviceNum,e)};function Oe(e){return null===e?"":(e=Re[e]||e,String.fromCharCode(e))}function Me(){for(var e=[],t=15;t--;)e.push("");return e}var Re={42:225,92:233,94:237,95:243,96:250,123:231,124:247,125:209,126:241,127:9608,304:174,305:176,306:189,307:191,308:8482,309:162,310:163,311:9834,312:224,313:160,314:232,315:226,316:234,317:238,318:244,319:251,544:193,545:201,546:211,547:218,548:220,549:252,550:8216,551:161,552:42,553:39,554:8212,555:169,556:8480,557:8226,558:8220,559:8221,560:192,561:194,562:199,563:200,564:202,565:203,566:235,567:206,568:207,569:239,570:212,571:217,572:249,573:219,574:171,575:187,800:195,801:227,802:205,803:204,804:236,805:210,806:242,807:213,808:245,809:123,810:125,811:92,812:94,813:95,814:124,815:126,816:196,817:228,818:214,819:246,820:223,821:165,822:164,823:9474,824:197,825:229,826:216,827:248,828:9484,829:9488,830:9492,831:9496},Ne=[4352,4384,4608,4640,5376,5408,5632,5664,5888,5920,4096,4864,4896,5120,5152],Ue=function e(t,i){e.prototype.init.call(this),this.field_=t||0,this.dataChannel_=i||0,this.name_="CC"+(1+(this.field_<<1|this.dataChannel_)),this.setConstants(),this.reset(),this.push=function(e){var t,i,n,r,a=32639&e.ccData;a!==this.lastControlCode_?(4096==(61440&a)?this.lastControlCode_=a:a!==this.PADDING_&&(this.lastControlCode_=null),t=a>>>8,i=255&a,a===this.PADDING_||(a===this.RESUME_CAPTION_LOADING_?this.mode_="popOn":a===this.END_OF_CAPTION_?(this.mode_="popOn",this.clearFormatting(e.pts),this.flushDisplayed(e.pts),r=this.displayed_,this.displayed_=this.nonDisplayed_,this.nonDisplayed_=r,this.startPts_=e.pts):a===this.ROLL_UP_2_ROWS_?(this.rollUpRows_=2,this.setRollUp(e.pts)):a===this.ROLL_UP_3_ROWS_?(this.rollUpRows_=3,this.setRollUp(e.pts)):a===this.ROLL_UP_4_ROWS_?(this.rollUpRows_=4,this.setRollUp(e.pts)):a===this.CARRIAGE_RETURN_?(this.clearFormatting(e.pts),this.flushDisplayed(e.pts),this.shiftRowsUp_(),this.startPts_=e.pts):a===this.BACKSPACE_?"popOn"===this.mode_?this.nonDisplayed_[this.row_]=this.nonDisplayed_[this.row_].slice(0,-1):this.displayed_[this.row_]=this.displayed_[this.row_].slice(0,-1):a===this.ERASE_DISPLAYED_MEMORY_?(this.flushDisplayed(e.pts),this.displayed_=Me()):a===this.ERASE_NON_DISPLAYED_MEMORY_?this.nonDisplayed_=Me():a===this.RESUME_DIRECT_CAPTIONING_?("paintOn"!==this.mode_&&(this.flushDisplayed(e.pts),this.displayed_=Me()),this.mode_="paintOn",this.startPts_=e.pts):this.isSpecialCharacter(t,i)?(n=Oe((t=(3&t)<<8)|i),this[this.mode_](e.pts,n),this.column_++):this.isExtCharacter(t,i)?("popOn"===this.mode_?this.nonDisplayed_[this.row_]=this.nonDisplayed_[this.row_].slice(0,-1):this.displayed_[this.row_]=this.displayed_[this.row_].slice(0,-1),n=Oe((t=(3&t)<<8)|i),this[this.mode_](e.pts,n),this.column_++):this.isMidRowCode(t,i)?(this.clearFormatting(e.pts),this[this.mode_](e.pts," "),this.column_++,14==(14&i)&&this.addFormatting(e.pts,["i"]),1==(1&i)&&this.addFormatting(e.pts,["u"])):this.isOffsetControlCode(t,i)?this.column_+=3&i:this.isPAC(t,i)?(r=Ne.indexOf(7968&a),"rollUp"===this.mode_&&(r-this.rollUpRows_+1<0&&(r=this.rollUpRows_-1),this.setRollUp(e.pts,r)),r!==this.row_&&(this.clearFormatting(e.pts),this.row_=r),1&i&&-1===this.formatting_.indexOf("u")&&this.addFormatting(e.pts,["u"]),16==(16&a)&&(this.column_=4*((14&a)>>1)),this.isColorPAC(i)&&14==(14&i)&&this.addFormatting(e.pts,["i"])):this.isNormalChar(t)&&(0===i&&(i=null),n=Oe(t),n+=Oe(i),this[this.mode_](e.pts,n),this.column_+=n.length))):this.lastControlCode_=null}};Ue.prototype=new j,Ue.prototype.flushDisplayed=function(e){var t=this.displayed_.map(function(e,t){try{return e.trim()}catch(e){return this.trigger("log",{level:"warn",message:"Skipping a malformed 608 caption at index "+t+"."}),""}},this).join("\n").replace(/^\n+|\n+$/g,"");t.length&&this.trigger("data",{startPts:this.startPts_,endPts:e,text:t,stream:this.name_})},Ue.prototype.reset=function(){this.mode_="popOn",this.topRow_=0,this.startPts_=0,this.displayed_=Me(),this.nonDisplayed_=Me(),this.lastControlCode_=null,this.column_=0,this.row_=14,this.rollUpRows_=2,this.formatting_=[]},Ue.prototype.setConstants=function(){0===this.dataChannel_?(this.BASE_=16,this.EXT_=17,this.CONTROL_=(20|this.field_)<<8,this.OFFSET_=23):1===this.dataChannel_&&(this.BASE_=24,this.EXT_=25,this.CONTROL_=(28|this.field_)<<8,this.OFFSET_=31),this.PADDING_=0,this.RESUME_CAPTION_LOADING_=32|this.CONTROL_,this.END_OF_CAPTION_=47|this.CONTROL_,this.ROLL_UP_2_ROWS_=37|this.CONTROL_,this.ROLL_UP_3_ROWS_=38|this.CONTROL_,this.ROLL_UP_4_ROWS_=39|this.CONTROL_,this.CARRIAGE_RETURN_=45|this.CONTROL_,this.RESUME_DIRECT_CAPTIONING_=41|this.CONTROL_,this.BACKSPACE_=33|this.CONTROL_,this.ERASE_DISPLAYED_MEMORY_=44|this.CONTROL_,this.ERASE_NON_DISPLAYED_MEMORY_=46|this.CONTROL_},Ue.prototype.isSpecialCharacter=function(e,t){return e===this.EXT_&&48<=t&&t<=63},Ue.prototype.isExtCharacter=function(e,t){return(e===this.EXT_+1||e===this.EXT_+2)&&32<=t&&t<=63},Ue.prototype.isMidRowCode=function(e,t){return e===this.EXT_&&32<=t&&t<=47},Ue.prototype.isOffsetControlCode=function(e,t){return e===this.OFFSET_&&33<=t&&t<=35},Ue.prototype.isPAC=function(e,t){return e>=this.BASE_&&e"},"");this[this.mode_](e,t)},Ue.prototype.clearFormatting=function(e){var t;this.formatting_.length&&(t=this.formatting_.reverse().reduce(function(e,t){return e+""},""),this.formatting_=[],this[this.mode_](e,t))},Ue.prototype.popOn=function(e,t){var i=this.nonDisplayed_[this.row_];this.nonDisplayed_[this.row_]=i+=t},Ue.prototype.rollUp=function(e,t){var i=this.displayed_[this.row_];this.displayed_[this.row_]=i+=t},Ue.prototype.shiftRowsUp_=function(){for(var e=0;e>>2,s*=4,s+=3&a[7],o.timeStamp=s,void 0===t.pts&&void 0===t.dts&&(t.pts=o.timeStamp,t.dts=o.timeStamp),this.trigger("timestamp",o))),t.frames.push(o),i+=10,(i+=n)>>4&&(i+=e[i]+1),0===t.pid)t.type="pat",n(e.subarray(i),t),this.trigger("data",t);else if(t.pid===this.pmtPid)for(t.type="pmt",n(e.subarray(i),t),this.trigger("data",t);this.packetsWaitingForPmt.length;)this.processPes_.apply(this,this.packetsWaitingForPmt.shift());else void 0===this.programMapTable?this.packetsWaitingForPmt.push([e,i,t]):this.processPes_(e,i,t)},this.processPes_=function(e,t,i){i.pid===this.programMapTable.video?i.streamType=je.H264_STREAM_TYPE:i.pid===this.programMapTable.audio?i.streamType=je.ADTS_STREAM_TYPE:i.streamType=this.programMapTable["timed-metadata"][i.pid],i.type="pes",i.data=e.subarray(t),this.trigger("data",i)}}).prototype=new j,Xe.STREAM_TYPES={h264:27,adts:15},(Ke=function(){function n(e,t,i){var n,r,a,s,o=new Uint8Array(e.size),u={type:t},l=0,c=0;if(e.data.length&&!(e.size<9)){for(u.trackId=e.data[0].pid,l=0;l>>3,a.pts*=4,a.pts+=(6&r[13])>>>1,a.dts=a.pts,64&s&&(a.dts=(14&r[14])<<27|(255&r[15])<<20|(254&r[16])<<12|(255&r[17])<<5|(254&r[18])>>>3,a.dts*=4,a.dts+=(6&r[18])>>>1)),a.data=r.subarray(9+r[8])),t="video"===t||u.packetLength<=e.size,(i||t)&&(e.size=0,e.data.length=0),t&&d.trigger("data",u)}}var t,d=this,r=!1,a={data:[],size:0},s={data:[],size:0},o={data:[],size:0};Ke.prototype.init.call(this),this.push=function(i){({pat:function(){},pes:function(){var e,t;switch(i.streamType){case je.H264_STREAM_TYPE:e=a,t="video";break;case je.ADTS_STREAM_TYPE:e=s,t="audio";break;case je.METADATA_STREAM_TYPE:e=o,t="timed-metadata";break;default:return}i.payloadUnitStartIndicator&&n(e,t,!0),e.data.push(i),e.size+=i.data.byteLength},pmt:function(){var e={type:"metadata",tracks:[]};null!==(t=i.programMapTable).video&&e.tracks.push({timelineStartInfo:{baseMediaDecodeTime:0},id:+t.video,codec:"avc",type:"video"}),null!==t.audio&&e.tracks.push({timelineStartInfo:{baseMediaDecodeTime:0},id:+t.audio,codec:"adts",type:"audio"}),r=!0,d.trigger("data",e)}})[i.type]()},this.reset=function(){a.size=0,a.data.length=0,s.size=0,s.data.length=0,this.trigger("reset")},this.flushStreams_=function(){n(a,"video"),n(s,"audio"),n(o,"timed-metadata")},this.flush=function(){var e;!r&&t&&(e={type:"metadata",tracks:[]},null!==t.video&&e.tracks.push({timelineStartInfo:{baseMediaDecodeTime:0},id:+t.video,codec:"avc",type:"video"}),null!==t.audio&&e.tracks.push({timelineStartInfo:{baseMediaDecodeTime:0},id:+t.audio,codec:"adts",type:"audio"}),d.trigger("data",e)),r=!1,this.flushStreams_(),this.trigger("done")}}).prototype=new j;var Qe,$e={PAT_PID:0,MP2T_PACKET_LENGTH:188,TransportPacketStream:Ye,TransportParseStream:Xe,ElementaryStream:Ke,TimestampRolloverStream:We,CaptionStream:Fe.CaptionStream,Cea608Stream:Fe.Cea608Stream,Cea708Stream:Fe.Cea708Stream,MetadataStream:e};for(Qe in je)je.hasOwnProperty(Qe)&&($e[Qe]=je[Qe]);var Je=$e,Ze=ue,et=[96e3,88200,64e3,48e3,44100,32e3,24e3,22050,16e3,12e3,11025,8e3,7350],tt=function(u){var l,c=0;tt.prototype.init.call(this),this.skipWarn_=function(e,t){this.trigger("log",{level:"warn",message:"adts skiping bytes "+e+" to "+t+" in frame "+c+" outside syncword"})},this.push=function(e){var t,i,n,r,a,s,o=0;if(u||(c=0),"audio"===e.type){for(l&&l.length?(n=l,(l=new Uint8Array(n.byteLength+e.data.byteLength)).set(n),l.set(e.data,n.byteLength)):l=e.data;o+7>5,a=(r=1024*(1+(3&l[o+6])))*Ze/et[(60&l[o+2])>>>2],l.byteLength-o>>6&3),channelcount:(1&l[o+2])<<2|(192&l[o+3])>>>6,samplerate:et[(60&l[o+2])>>>2],samplingfrequencyindex:(60&l[o+2])>>>2,samplesize:16,data:l.subarray(o+7+i,o+t)}),c++,o+=t}else"number"!=typeof s&&(s=o),o++;"number"==typeof s&&(this.skipWarn_(s,o),s=null),l=l.subarray(o)}},this.flush=function(){c=0,this.trigger("done")},this.reset=function(){l=void 0,this.trigger("reset")},this.endTimeline=function(){l=void 0,this.trigger("endedtimeline")}};tt.prototype=new j;var it,nt,rt=tt,at=function(n){var r=n.byteLength,a=0,s=0;this.length=function(){return 8*r},this.bitsAvailable=function(){return 8*r+s},this.loadWord=function(){var e=n.byteLength-r,t=new Uint8Array(4),i=Math.min(4,r);if(0===i)throw new Error("no bytes available");t.set(n.subarray(e,e+i)),a=new DataView(t.buffer).getUint32(0),s=8*i,r-=i},this.skipBits=function(e){var t;e>>32-t;return 0<(s-=t)?a<<=t:0>>e))return a<<=e,s-=e,e;return this.loadWord(),e+this.skipLeadingZeros()},this.skipUnsignedExpGolomb=function(){this.skipBits(1+this.skipLeadingZeros())},this.skipExpGolomb=function(){this.skipBits(1+this.skipLeadingZeros())},this.readUnsignedExpGolomb=function(){var e=this.skipLeadingZeros();return this.readBits(e+1)-1},this.readExpGolomb=function(){var e=this.readUnsignedExpGolomb();return 1&e?1+e>>>1:-1*(e>>>1)},this.readBoolean=function(){return 1===this.readBits(1)},this.readUnsignedByte=function(){return this.readBits(8)},this.loadWord()},st=function(){var n,r,a=0;st.prototype.init.call(this),this.push=function(e){for(var t,i=(r=r?((t=new Uint8Array(r.byteLength+e.data.byteLength)).set(r),t.set(e.data,r.byteLength),t):e.data).byteLength;a>4?i+20:i+10}function ut(e,t){return e.length-t<10||e[t]!=="I".charCodeAt(0)||e[t+1]!=="D".charCodeAt(0)||e[t+2]!=="3".charCodeAt(0)?t:ut(e,t+=ot(e,t))}function lt(e){return e[0]<<21|e[1]<<14|e[2]<<7|e[3]}var e={H264Stream:it,NalByteStream:st},ct=[96e3,88200,64e3,48e3,44100,32e3,24e3,22050,16e3,12e3,11025,8e3,7350],dt={isLikelyAacData:function(e){var t=ut(e,0);return e.length>=t+2&&255==(255&e[t])&&240==(240&e[t+1])&&16==(22&e[t+1])},parseId3TagSize:ot,parseAdtsSize:function(e,t){var i=(224&e[t+5])>>5,n=e[t+4]<<3;return 6144&e[t+3]|n|i},parseType:function(e,t){return e[t]==="I".charCodeAt(0)&&e[t+1]==="D".charCodeAt(0)&&e[t+2]==="3".charCodeAt(0)?"timed-metadata":!0&e[t]&&240==(240&e[t+1])?"audio":null},parseSampleRate:function(e){for(var t=0;t+5>>2];t++}return null},parseAacTimestamp:function(e){var t,i=10;64&e[5]&&(i+=4,i+=lt(e.subarray(10,14)));do{if((t=lt(e.subarray(i+4,i+8)))<1)return null;if("PRIV"===String.fromCharCode(e[i],e[i+1],e[i+2],e[i+3]))for(var n=e.subarray(i+10,i+t+10),r=0;r>>2;return s*=4,s+=3&a[7]}}while(i+=10,(i+=t)a.length)break;t={type:"audio",data:a.subarray(r,r+n),pts:s,dts:s},this.trigger("data",t),r+=n}else{if(a.length-r<10)break;if(r+(n=dt.parseId3TagSize(a,r))>a.length)break;t={type:"timed-metadata",data:a.subarray(r,r+n)},this.trigger("data",t),r+=n}e=a.length-r,a=0i.pts?u++:(t++,a-=n.byteLength,s-=n.nalCount,o-=n.duration);return 0===t?e:t===e.length?null:((r=e.slice(t)).byteLength=a,r.duration=o,r.nalCount=s,r.pts=r[0].pts,r.dts=r[0].dts,r)},this.alignGopsAtEnd_=function(e){for(var t,i,n=l.length-1,r=e.length-1,a=null,s=!1;0<=n&&0<=r;){if(t=l[n],i=e[r],t.pts===i.pts){s=!0;break}t.pts>i.pts?n--:(n===l.length-1&&(a=r),r--)}if(!s&&null===a)return null;if(0===(u=s?r:a))return e;var o=e.slice(u),u=o.reduce(function(e,t){return e.byteLength+=t.byteLength,e.duration+=t.duration,e.nalCount+=t.nalCount,e},{byteLength:0,duration:0,nalCount:0});return o.byteLength=u.byteLength,o.duration=u.duration,o.nalCount=u.nalCount,o.pts=o[0].pts,o.dts=o[0].dts,o},this.alignGopsWith=function(e){l=e}}).prototype=new j,(_t=function(e,t){this.numberOfTracks=0,this.metadataStream=t,"undefined"!=typeof(e=e||{}).remux?this.remuxTracks=!!e.remux:this.remuxTracks=!0,"boolean"==typeof e.keepOriginalTimestamps?this.keepOriginalTimestamps=e.keepOriginalTimestamps:this.keepOriginalTimestamps=!1,this.pendingTracks=[],this.videoTrack=null,this.pendingBoxes=[],this.pendingCaptions=[],this.pendingMetadata=[],this.pendingBytes=0,this.emittedTracks=0,_t.prototype.init.call(this),this.push=function(e){return e.text?this.pendingCaptions.push(e):e.frames?this.pendingMetadata.push(e):(this.pendingTracks.push(e.track),this.pendingBytes+=e.boxes.byteLength,"video"===e.track.type&&(this.videoTrack=e.track,this.pendingBoxes.push(e.boxes)),void("audio"===e.track.type&&(this.audioTrack=e.track,this.pendingBoxes.unshift(e.boxes))))}}).prototype=new j,_t.prototype.flush=function(e){var t,i,n,r=0,a={captions:[],captionStreams:{},metadata:[],info:{}},s=0;if(this.pendingTracks.length=this.numberOfTracks&&(this.trigger("done"),this.emittedTracks=0))}if(this.videoTrack?(s=this.videoTrack.timelineStartInfo.pts,St.forEach(function(e){a.info[e]=this.videoTrack[e]},this)):this.audioTrack&&(s=this.audioTrack.timelineStartInfo.pts,Tt.forEach(function(e){a.info[e]=this.audioTrack[e]},this)),this.videoTrack||this.audioTrack){for(1===this.pendingTracks.length?a.type=this.pendingTracks[0].type:a.type="combined",this.emittedTracks+=this.pendingTracks.length,e=Q(this.pendingTracks),a.initSegment=new Uint8Array(e.byteLength),a.initSegment.set(e),a.data=new Uint8Array(this.pendingBytes),n=0;n=this.numberOfTracks&&(this.trigger("done"),this.emittedTracks=0)},_t.prototype.setRemux=function(e){this.remuxTracks=e},(vt=function(n){var r,a,s=this,i=!0;vt.prototype.init.call(this),this.baseMediaDecodeTime=(n=n||{}).baseMediaDecodeTime||0,this.transmuxPipeline_={},this.setupAacPipeline=function(){var t={};(this.transmuxPipeline_=t).type="aac",t.metadataStream=new Je.MetadataStream,t.aacStream=new bt,t.audioTimestampRolloverStream=new Je.TimestampRolloverStream("audio"),t.timedMetadataTimestampRolloverStream=new Je.TimestampRolloverStream("timed-metadata"),t.adtsStream=new rt,t.coalesceStream=new _t(n,t.metadataStream),t.headOfPipeline=t.aacStream,t.aacStream.pipe(t.audioTimestampRolloverStream).pipe(t.adtsStream),t.aacStream.pipe(t.timedMetadataTimestampRolloverStream).pipe(t.metadataStream).pipe(t.coalesceStream),t.metadataStream.on("timestamp",function(e){t.aacStream.setTimestamp(e.timeStamp)}),t.aacStream.on("data",function(e){"timed-metadata"!==e.type&&"audio"!==e.type||t.audioSegmentStream||(a=a||{timelineStartInfo:{baseMediaDecodeTime:s.baseMediaDecodeTime},codec:"adts",type:"audio"},t.coalesceStream.numberOfTracks++,t.audioSegmentStream=new Ct(a,n),t.audioSegmentStream.on("log",s.getLogTrigger_("audioSegmentStream")),t.audioSegmentStream.on("timingInfo",s.trigger.bind(s,"audioTimingInfo")),t.adtsStream.pipe(t.audioSegmentStream).pipe(t.coalesceStream),s.trigger("trackinfo",{hasAudio:!!a,hasVideo:!!r}))}),t.coalesceStream.on("data",this.trigger.bind(this,"data")),t.coalesceStream.on("done",this.trigger.bind(this,"done")),ft(this,t)},this.setupTsPipeline=function(){var i={};(this.transmuxPipeline_=i).type="ts",i.metadataStream=new Je.MetadataStream,i.packetStream=new Je.TransportPacketStream,i.parseStream=new Je.TransportParseStream,i.elementaryStream=new Je.ElementaryStream,i.timestampRolloverStream=new Je.TimestampRolloverStream,i.adtsStream=new rt,i.h264Stream=new wt,i.captionStream=new Je.CaptionStream(n),i.coalesceStream=new _t(n,i.metadataStream),i.headOfPipeline=i.packetStream,i.packetStream.pipe(i.parseStream).pipe(i.elementaryStream).pipe(i.timestampRolloverStream),i.timestampRolloverStream.pipe(i.h264Stream),i.timestampRolloverStream.pipe(i.adtsStream),i.timestampRolloverStream.pipe(i.metadataStream).pipe(i.coalesceStream),i.h264Stream.pipe(i.captionStream).pipe(i.coalesceStream),i.elementaryStream.on("data",function(e){var t;if("metadata"===e.type){for(t=e.tracks.length;t--;)r||"video"!==e.tracks[t].type?a||"audio"!==e.tracks[t].type||((a=e.tracks[t]).timelineStartInfo.baseMediaDecodeTime=s.baseMediaDecodeTime):(r=e.tracks[t]).timelineStartInfo.baseMediaDecodeTime=s.baseMediaDecodeTime;r&&!i.videoSegmentStream&&(i.coalesceStream.numberOfTracks++,i.videoSegmentStream=new yt(r,n),i.videoSegmentStream.on("log",s.getLogTrigger_("videoSegmentStream")),i.videoSegmentStream.on("timelineStartInfo",function(e){a&&!n.keepOriginalTimestamps&&(a.timelineStartInfo=e,i.audioSegmentStream.setEarliestDts(e.dts-s.baseMediaDecodeTime))}),i.videoSegmentStream.on("processedGopsInfo",s.trigger.bind(s,"gopInfo")),i.videoSegmentStream.on("segmentTimingInfo",s.trigger.bind(s,"videoSegmentTimingInfo")),i.videoSegmentStream.on("baseMediaDecodeTime",function(e){a&&i.audioSegmentStream.setVideoBaseMediaDecodeTime(e)}),i.videoSegmentStream.on("timingInfo",s.trigger.bind(s,"videoTimingInfo")),i.h264Stream.pipe(i.videoSegmentStream).pipe(i.coalesceStream)),a&&!i.audioSegmentStream&&(i.coalesceStream.numberOfTracks++,i.audioSegmentStream=new Ct(a,n),i.audioSegmentStream.on("log",s.getLogTrigger_("audioSegmentStream")),i.audioSegmentStream.on("timingInfo",s.trigger.bind(s,"audioTimingInfo")),i.audioSegmentStream.on("segmentTimingInfo",s.trigger.bind(s,"audioSegmentTimingInfo")),i.adtsStream.pipe(i.audioSegmentStream).pipe(i.coalesceStream)),s.trigger("trackinfo",{hasAudio:!!a,hasVideo:!!r})}}),i.coalesceStream.on("data",this.trigger.bind(this,"data")),i.coalesceStream.on("id3Frame",function(e){e.dispatchType=i.metadataStream.dispatchType,s.trigger("id3Frame",e)}),i.coalesceStream.on("caption",this.trigger.bind(this,"caption")),i.coalesceStream.on("done",this.trigger.bind(this,"done")),ft(this,i)},this.setBaseMediaDecodeTime=function(e){var t=this.transmuxPipeline_;n.keepOriginalTimestamps||(this.baseMediaDecodeTime=e),a&&(a.timelineStartInfo.dts=void 0,a.timelineStartInfo.pts=void 0,_e(a),t.audioTimestampRolloverStream&&t.audioTimestampRolloverStream.discontinuity()),r&&(t.videoSegmentStream&&(t.videoSegmentStream.gopCache_=[]),r.timelineStartInfo.dts=void 0,r.timelineStartInfo.pts=void 0,_e(r),t.captionStream.reset()),t.timestampRolloverStream&&t.timestampRolloverStream.discontinuity()},this.setAudioAppendStart=function(e){a&&this.transmuxPipeline_.audioSegmentStream.setAudioAppendStart(e)},this.setRemux=function(e){var t=this.transmuxPipeline_;n.remux=e,t&&t.coalesceStream&&t.coalesceStream.setRemux(e)},this.alignGopsWith=function(e){r&&this.transmuxPipeline_.videoSegmentStream&&this.transmuxPipeline_.videoSegmentStream.alignGopsWith(e)},this.getLogTrigger_=function(t){var i=this;return function(e){e.stream=t,i.trigger("log",e)}},this.push=function(e){var t;i&&((t=Et(e))&&"aac"!==this.transmuxPipeline_.type?this.setupAacPipeline():t||"ts"===this.transmuxPipeline_.type||this.setupTsPipeline(),i=!1),this.transmuxPipeline_.headOfPipeline.push(e)},this.flush=function(){i=!0,this.transmuxPipeline_.headOfPipeline.flush()},this.endTimeline=function(){this.transmuxPipeline_.headOfPipeline.endTimeline()},this.reset=function(){this.transmuxPipeline_.headOfPipeline&&this.transmuxPipeline_.headOfPipeline.reset()},this.resetCaptions=function(){this.transmuxPipeline_.captionStream&&this.transmuxPipeline_.captionStream.reset()}}).prototype=new j;function It(e,c){var i=Rt(e,["moof","traf"]),e=Rt(e,["mdat"]),d={},n=[];return e.forEach(function(e,t){t=i[t];n.push({mdat:e,traf:t})}),n.forEach(function(e){var t,i,n,r,a,s=e.mdat,o=e.traf,u=Rt(o,["tfhd"]),l=Ht(u[0]),e=l.trackId,u=Rt(o,["tfdt"]),u=0>>4&&(t+=e[4]+1),t}function Lt(e){switch(e){case 5:return"slice_layer_without_partitioning_rbsp_idr";case 6:return"sei_rbsp";case 7:return"seq_parameter_set_rbsp";case 8:return"pic_parameter_set_rbsp";case 9:return"access_unit_delimiter_rbsp";default:return null}}var Dt={Transmuxer:vt,VideoSegmentStream:yt,AudioSegmentStream:Ct,AUDIO_PROPERTIES:Tt,VIDEO_PROPERTIES:St,generateSegmentTimingInfo:gt},e=function(e){return e>>>0},Ot=function(e){var t="";return t+=String.fromCharCode(e[0]),t+=String.fromCharCode(e[1]),t+=String.fromCharCode(e[2]),t+=String.fromCharCode(e[3])},Mt=e,Rt=function e(t,i){var n,r,a,s=[];if(!i.length)return null;for(n=0;n>>2,dependsOn:3&e[0],isDependedOn:(192&e[1])>>>6,hasRedundancy:(48&e[1])>>>4,paddingValue:(14&e[1])>>>1,isNonSyncSample:1&e[1],degradationPriority:e[2]<<8|e[3]}},jt=function(e){var t,i={version:e[0],flags:new Uint8Array(e.subarray(1,4)),samples:[]},n=new DataView(e.buffer,e.byteOffset,e.byteLength),r=1&i.flags[2],a=4&i.flags[2],s=1&i.flags[1],o=2&i.flags[1],u=4&i.flags[1],l=8&i.flags[1],c=n.getUint32(4),d=8;for(r&&(i.dataOffset=n.getInt32(d),d+=4),a&&c&&(t={flags:Ft(e.subarray(d,d+4))},d+=4,s&&(t.duration=n.getUint32(d),d+=4),o&&(t.size=n.getUint32(d),d+=4),l&&(1===i.version?t.compositionTimeOffset=n.getInt32(d):t.compositionTimeOffset=n.getUint32(d),d+=4),i.samples.push(t),c--);c--;)t={},s&&(t.duration=n.getUint32(d),d+=4),o&&(t.size=n.getUint32(d),d+=4),u&&(t.flags=Ft(e.subarray(d,d+4)),d+=4),l&&(1===i.version?t.compositionTimeOffset=n.getInt32(d):t.compositionTimeOffset=n.getUint32(d),d+=4),i.samples.push(t);return i},Ht=function(e){var t=new DataView(e.buffer,e.byteOffset,e.byteLength),i={version:e[0],flags:new Uint8Array(e.subarray(1,4)),trackId:t.getUint32(4)},n=1&i.flags[2],r=2&i.flags[2],a=8&i.flags[2],s=16&i.flags[2],o=32&i.flags[2],u=65536&i.flags[0],l=131072&i.flags[0],e=8;return n&&(e+=4,i.baseDataOffset=t.getUint32(12),e+=4),r&&(i.sampleDescriptionIndex=t.getUint32(e),e+=4),a&&(i.defaultSampleDuration=t.getUint32(e),e+=4),s&&(i.defaultSampleSize=t.getUint32(e),e+=4),o&&(i.defaultSampleFlags=t.getUint32(e)),u&&(i.durationIsEmpty=!0),!n&&l&&(i.baseDataOffsetIsMoof=!0),i},j="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{},j="undefined"!=typeof window?window:"undefined"!=typeof j?j:"undefined"!=typeof self?self:{},qt=j,Vt=ke,Wt=Fe.CaptionStream,Gt=function(){var t,r,a,s,o,i,n=!1;this.isInitialized=function(){return n},this.init=function(e){t=new Wt,n=!0,i=!!e&&e.isPartial,t.on("data",function(e){e.startTime=e.startPts/s,e.endTime=e.endPts/s,o.captions.push(e),o.captionStreams[e.stream]=!0}),t.on("log",function(e){o.logs.push(e)})},this.isNewInit=function(e,t){return!(e&&0===e.length||t&&"object"==typeof t&&0===Object.keys(t).length)&&(a!==e[0]||s!==t[a])},this.parse=function(e,t,i){if(!this.isInitialized())return null;if(!t||!i)return null;if(this.isNewInit(t,i))a=t[0],s=i[a];else if(null===a||!s)return r.push(e),null;for(;0>>2&63).replace(/^0/,"")):t.codec="mp4a.40.2"):t.codec=t.codec.toLowerCase()));e=Rt(e,["mdia","mdhd"])[0];e&&(t.timescale=Yt(e)),s.push(t)}),s},Qt=ke,$t=q,Jt=Ie,Zt={};Zt.ts={parseType:function(e,t){e=xt(e);return 0===e?"pat":e===t?"pmt":t?"pes":null},parsePat:function(e){var t=At(e),i=4+Pt(e);return t&&(i+=e[i]+1),(31&e[i+10])<<8|e[i+11]},parsePmt:function(e){var t={},i=At(e),n=4+Pt(e);if(i&&(n+=e[n]+1),1&e[n+5]){for(var r=3+((15&e[n+1])<<8|e[n+2])-4,a=12+((15&e[n+10])<<8|e[n+11]);a=e.byteLength)return null;var i=null,n=e[t+7];return 192&n&&((i={}).pts=(14&e[t+9])<<27|(255&e[t+10])<<20|(254&e[t+11])<<12|(255&e[t+12])<<5|(254&e[t+13])>>>3,i.pts*=4,i.pts+=(6&e[t+13])>>>1,i.dts=i.pts,64&n&&(i.dts=(14&e[t+14])<<27|(255&e[t+15])<<20|(254&e[t+16])<<12|(255&e[t+17])<<5|(254&e[t+18])>>>3,i.dts*=4,i.dts+=(6&e[t+18])>>>1)),i},videoPacketContainsKeyFrame:function(e){for(var t=4+Pt(e),i=e.subarray(t),n=0,r=0,a=!1;re.length){i=!0;break}null===a&&(t=e.subarray(o,o+s),a=Zt.aac.parseAacTimestamp(t)),o+=s;break;case"audio":if(e.length-o<7){i=!0;break}if((s=Zt.aac.parseAdtsSize(e,o))>e.length){i=!0;break}null===r&&(t=e.subarray(o,o+s),r=Zt.aac.parseSampleRate(t)),n++,o+=s;break;default:o++}if(i)return null}if(null===r||null===a)return null;var u=ii/r;return{audio:[{type:"audio",dts:a,pts:a},{type:"audio",dts:a+1024*n*u,pts:a+1024*n*u}]}}:ti)(e);return r&&(r.audio||r.video)?(e=t,(t=r).audio&&t.audio.length&&("undefined"!=typeof(i=e)&&!isNaN(i)||(i=t.audio[0].dts),t.audio.forEach(function(e){e.dts=Jt(e.dts,i),e.pts=Jt(e.pts,i),e.dtsTime=e.dts/ii,e.ptsTime=e.pts/ii})),t.video&&t.video.length&&("undefined"!=typeof(n=e)&&!isNaN(n)||(n=t.video[0].dts),t.video.forEach(function(e){e.dts=Jt(e.dts,n),e.pts=Jt(e.pts,n),e.dtsTime=e.dts/ii,e.ptsTime=e.pts/ii}),t.firstKeyFrame&&((t=t.firstKeyFrame).dts=Jt(t.dts,n),t.pts=Jt(t.pts,n),t.dtsTime=t.dts/ii,t.ptsTime=t.pts/ii)),r):null},ri=function(){function e(e,t){this.options=t||{},this.self=e,this.init()}var t=e.prototype;return t.init=function(){var i,e;this.transmuxer&&this.transmuxer.dispose(),this.transmuxer=new Dt.Transmuxer(this.options),i=this.self,(e=this.transmuxer).on("data",function(e){var t=e.initSegment;e.initSegment={data:t.buffer,byteOffset:t.byteOffset,byteLength:t.byteLength};t=e.data;e.data=t.buffer,i.postMessage({action:"data",segment:e,byteOffset:t.byteOffset,byteLength:t.byteLength},[e.data])}),e.on("done",function(e){i.postMessage({action:"done"})}),e.on("gopInfo",function(e){i.postMessage({action:"gopInfo",gopInfo:e})}),e.on("videoSegmentTimingInfo",function(e){var t={start:{decode:ce(e.start.dts),presentation:ce(e.start.pts)},end:{decode:ce(e.end.dts),presentation:ce(e.end.pts)},baseMediaDecodeTime:ce(e.baseMediaDecodeTime)};e.prependedContentDuration&&(t.prependedContentDuration=ce(e.prependedContentDuration)),i.postMessage({action:"videoSegmentTimingInfo",videoSegmentTimingInfo:t})}),e.on("audioSegmentTimingInfo",function(e){var t={start:{decode:ce(e.start.dts),presentation:ce(e.start.pts)},end:{decode:ce(e.end.dts),presentation:ce(e.end.pts)},baseMediaDecodeTime:ce(e.baseMediaDecodeTime)};e.prependedContentDuration&&(t.prependedContentDuration=ce(e.prependedContentDuration)),i.postMessage({action:"audioSegmentTimingInfo",audioSegmentTimingInfo:t})}),e.on("id3Frame",function(e){i.postMessage({action:"id3Frame",id3Frame:e})}),e.on("caption",function(e){i.postMessage({action:"caption",caption:e})}),e.on("trackinfo",function(e){i.postMessage({action:"trackinfo",trackInfo:e})}),e.on("audioTimingInfo",function(e){i.postMessage({action:"audioTimingInfo",audioTimingInfo:{start:ce(e.start),end:ce(e.end)}})}),e.on("videoTimingInfo",function(e){i.postMessage({action:"videoTimingInfo",videoTimingInfo:{start:ce(e.start),end:ce(e.end)}})}),e.on("log",function(e){i.postMessage({action:"log",log:e})})},t.pushMp4Captions=function(e){this.captionParser||(this.captionParser=new Gt,this.captionParser.init());var t=new Uint8Array(e.data,e.byteOffset,e.byteLength),e=this.captionParser.parse(t,e.trackIds,e.timescales);this.self.postMessage({action:"mp4Captions",captions:e&&e.captions||[],logs:e&&e.logs||[],data:t.buffer},[t.buffer])},t.probeMp4StartTime=function(e){var t=e.timescales,e=e.data,t=Qt(t,e);this.self.postMessage({action:"probeMp4StartTime",startTime:t,data:e},[e.buffer])},t.probeMp4Tracks=function(e){var t=e.data,e=$t(t);this.self.postMessage({action:"probeMp4Tracks",tracks:e,data:t},[t.buffer])},t.probeTs=function(e){var t=e.data,i=e.baseStartTime,e="number"!=typeof i||isNaN(i)?void 0:i*ue,i=ni(t,e),e=null;i&&((e={hasVideo:i.video&&2===i.video.length||!1,hasAudio:i.audio&&2===i.audio.length||!1}).hasVideo&&(e.videoStart=i.video[0].ptsTime),e.hasAudio&&(e.audioStart=i.audio[0].ptsTime)),this.self.postMessage({action:"probeTs",result:e,data:t},[t.buffer])},t.clearAllMp4Captions=function(){this.captionParser&&this.captionParser.clearAllCaptions()},t.clearParsedMp4Captions=function(){this.captionParser&&this.captionParser.clearParsedCaptions()},t.push=function(e){e=new Uint8Array(e.data,e.byteOffset,e.byteLength);this.transmuxer.push(e)},t.reset=function(){this.transmuxer.reset()},t.setTimestampOffset=function(e){e=e.timestampOffset||0;this.transmuxer.setBaseMediaDecodeTime(Math.round(le(e)))},t.setAudioAppendStart=function(e){this.transmuxer.setAudioAppendStart(Math.ceil(le(e.appendStart)))},t.setRemux=function(e){this.transmuxer.setRemux(e.remux)},t.flush=function(e){this.transmuxer.flush(),self.postMessage({action:"done",type:"transmuxed"})},t.endTimeline=function(){this.transmuxer.endTimeline(),self.postMessage({action:"endedtimeline",type:"transmuxed"})},t.alignGopsWith=function(e){this.transmuxer.alignGopsWith(e.gopsToAlignWith.slice())},e}();self.onmessage=function(e){"init"===e.data.action&&e.data.options?this.messageHandlers=new ri(self,e.data.options):(this.messageHandlers||(this.messageHandlers=new ri(self)),e.data&&e.data.action&&"init"!==e.data.action&&this.messageHandlers[e.data.action]&&this.messageHandlers[e.data.action](e.data))}}))),Cl=function(e){e.currentTransmux=null,e.transmuxQueue.length&&(e.currentTransmux=e.transmuxQueue.shift(),"function"==typeof e.currentTransmux?e.currentTransmux():Du(e.currentTransmux))},Il=function(e){Mu("reset",e)},xl=function(e){var t=new kl;t.currentTransmux=null,t.transmuxQueue=[];var i=t.terminate;return t.terminate=function(){return t.currentTransmux=null,t.transmuxQueue.length=0,i.call(t)},t.postMessage({action:"init",options:e}),t},Al=2,Pl=-101,Ll=-102,Dl=Ro("CodecUtils"),Ol=Ro("PlaylistSelector"),ar=function(){var e=this.useDevicePixelRatio&&window.devicePixelRatio||1;return il(this.playlists.master,this.systemBandwidth,parseInt(Zu(this.tech_.el(),"width"),10)*e,parseInt(Zu(this.tech_.el(),"height"),10)*e,this.limitRenditionByPlayerDimensions,this.masterPlaylistController_)},Ml=function(n){function e(e,t){var i=n.call(this)||this;if(!e)throw new TypeError("Initialization settings are required");if("function"!=typeof e.currentTime)throw new TypeError("No currentTime getter specified");if(!e.mediaSource)throw new TypeError("No MediaSource specified");return i.bandwidth=e.bandwidth,i.throughput={rate:0,count:0},i.roundTrip=NaN,i.resetStats_(),i.mediaIndex=null,i.partIndex=null,i.hasPlayed_=e.hasPlayed,i.currentTime_=e.currentTime,i.seekable_=e.seekable,i.seeking_=e.seeking,i.duration_=e.duration,i.mediaSource_=e.mediaSource,i.vhs_=e.vhs,i.loaderType_=e.loaderType,i.currentMediaInfo_=void 0,i.startingMediaInfo_=void 0,i.segmentMetadataTrack_=e.segmentMetadataTrack,i.goalBufferLength_=e.goalBufferLength,i.sourceType_=e.sourceType,i.sourceUpdater_=e.sourceUpdater,i.inbandTextTracks_=e.inbandTextTracks,i.state_="INIT",i.timelineChangeController_=e.timelineChangeController,i.shouldSaveSegmentTimingInfo_=!0,i.parse708captions_=e.parse708captions,i.useDtsForTimestampOffset_=e.useDtsForTimestampOffset,i.captionServices_=e.captionServices,i.experimentalExactManifestTimings=e.experimentalExactManifestTimings,i.checkBufferTimeout_=null,i.error_=void 0,i.currentTimeline_=-1,i.pendingSegment_=null,i.xhrOptions_=null,i.pendingSegments_=[],i.audioDisabled_=!1,i.isPendingTimestampOffset_=!1,i.gopBuffer_=[],i.timeMapping_=0,i.safeAppend_=11<=tr.browser.IE_VERSION,i.appendInitSegment_={audio:!0,video:!0},i.playlistOfLastInitSegment_={audio:null,video:null},i.callQueue_=[],i.loadQueue_=[],i.metadataQueue_={id3:[],caption:[]},i.waitingOnRemove_=!1,i.quotaExceededErrorRetryTimeout_=null,i.activeInitSegmentId_=null,i.initSegments_={},i.cacheEncryptionKeys_=e.cacheEncryptionKeys,i.keyCache_={},i.decrypter_=e.decrypter,i.syncController_=e.syncController,i.syncPoint_={segmentIndex:0,time:0},i.transmuxer_=i.createTransmuxer_(),i.triggerSyncInfoUpdate_=function(){return i.trigger("syncinfoupdate")},i.syncController_.on("syncinfoupdate",i.triggerSyncInfoUpdate_),i.mediaSource_.addEventListener("sourceopen",function(){i.isEndOfStream_()||(i.ended_=!1)}),i.fetchAtBuffer_=!1,i.logger_=Ro("SegmentLoader["+i.loaderType_+"]"),Object.defineProperty(ft(i),"state",{get:function(){return this.state_},set:function(e){e!==this.state_&&(this.logger_(this.state_+" -> "+e),this.state_=e,this.trigger("statechange"))}}),i.sourceUpdater_.on("ready",function(){i.hasEnoughInfoToAppend_()&&i.processCallQueue_()}),"main"===i.loaderType_&&i.timelineChangeController_.on("pendingtimelinechange",function(){i.hasEnoughInfoToAppend_()&&i.processCallQueue_()}),"audio"===i.loaderType_&&i.timelineChangeController_.on("timelinechange",function(){i.hasEnoughInfoToLoad_()&&i.processLoadQueue_(),i.hasEnoughInfoToAppend_()&&i.processCallQueue_()}),i}mt(e,n);var t=e.prototype;return t.createTransmuxer_=function(){return xl({remux:!1,alignGopsAtEnd:this.safeAppend_,keepOriginalTimestamps:!0,parse708captions:this.parse708captions_,captionServices:this.captionServices_})},t.resetStats_=function(){this.mediaBytesTransferred=0,this.mediaRequests=0,this.mediaRequestsAborted=0,this.mediaRequestsTimedout=0,this.mediaRequestsErrored=0,this.mediaTransferDuration=0,this.mediaSecondsLoaded=0,this.mediaAppends=0},t.dispose=function(){this.trigger("dispose"),this.state="DISPOSED",this.pause(),this.abort_(),this.transmuxer_&&this.transmuxer_.terminate(),this.resetStats_(),this.checkBufferTimeout_&&window.clearTimeout(this.checkBufferTimeout_),this.syncController_&&this.triggerSyncInfoUpdate_&&this.syncController_.off("syncinfoupdate",this.triggerSyncInfoUpdate_),this.off()},t.setAudio=function(e){this.audioDisabled_=!e,e?this.appendInitSegment_.audio=!0:this.sourceUpdater_.removeAudio(0,this.duration_())},t.abort=function(){"WAITING"===this.state?(this.abort_(),this.state="READY",this.paused()||this.monitorBuffer_()):this.pendingSegment_&&(this.pendingSegment_=null)},t.abort_=function(){this.pendingSegment_&&this.pendingSegment_.abortRequests&&this.pendingSegment_.abortRequests(),this.pendingSegment_=null,this.callQueue_=[],this.loadQueue_=[],this.metadataQueue_.id3=[],this.metadataQueue_.caption=[],this.timelineChangeController_.clearPendingTimelineChange(this.loaderType_),this.waitingOnRemove_=!1,window.clearTimeout(this.quotaExceededErrorRetryTimeout_),this.quotaExceededErrorRetryTimeout_=null},t.checkForAbort_=function(e){return"APPENDING"!==this.state||this.pendingSegment_?!this.pendingSegment_||this.pendingSegment_.requestId!==e:(this.state="READY",!0)},t.error=function(e){return"undefined"!=typeof e&&(this.logger_("error occurred:",e),this.error_=e),this.pendingSegment_=null,this.error_},t.endOfStream=function(){this.ended_=!0,this.transmuxer_&&Il(this.transmuxer_),this.gopBuffer_.length=0,this.pause(),this.trigger("ended")},t.buffered_=function(){var e=this.getMediaInfo_();if(!this.sourceUpdater_||!e)return tr.createTimeRanges();if("main"===this.loaderType_){var t=e.hasAudio,i=e.hasVideo,e=e.isMuxed;if(i&&t&&!this.audioDisabled_&&!e)return this.sourceUpdater_.buffered();if(i)return this.sourceUpdater_.videoBuffered()}return this.sourceUpdater_.audioBuffered()},t.initSegmentForMap=function(e,t){if(void 0===t&&(t=!1),!e)return null;var i=Su(e),n=this.initSegments_[i];return t&&!n&&e.bytes&&(this.initSegments_[i]=n={resolvedUri:e.resolvedUri,byterange:e.byterange,bytes:e.bytes,tracks:e.tracks,timescales:e.timescales}),n||e},t.segmentKey=function(e,t){if(void 0===t&&(t=!1),!e)return null;var i=wu(e),n=this.keyCache_[i];this.cacheEncryptionKeys_&&t&&!n&&e.bytes&&(this.keyCache_[i]=n={resolvedUri:e.resolvedUri,bytes:e.bytes});e={resolvedUri:(n||e).resolvedUri};return n&&(e.bytes=n.bytes),e},t.couldBeginLoading_=function(){return this.playlist_&&!this.paused()},t.load=function(){if(this.monitorBuffer_(),this.playlist_)return"INIT"===this.state&&this.couldBeginLoading_()?this.init_():void(!this.couldBeginLoading_()||"READY"!==this.state&&"INIT"!==this.state||(this.state="READY"))},t.init_=function(){return this.state="READY",this.resetEverything(),this.monitorBuffer_()},t.playlist=function(e,t){if(void 0===t&&(t={}),e){var i=this.playlist_,n=this.pendingSegment_;this.playlist_=e,this.xhrOptions_=t,"INIT"===this.state&&(e.syncInfo={mediaSequence:e.mediaSequence,time:0},"main"===this.loaderType_&&this.syncController_.setDateTimeMappingForStart(e));var r=null;if(i&&(i.id?r=i.id:i.uri&&(r=i.uri)),this.logger_("playlist update ["+r+" => "+(e.id||e.uri)+"]"),this.trigger("syncinfoupdate"),"INIT"===this.state&&this.couldBeginLoading_())return this.init_();if(!i||i.uri!==e.uri)return null!==this.mediaIndex&&(e.endList?this.resyncLoader():this.resetLoader()),this.currentMediaInfo_=void 0,void this.trigger("playlistupdate");t=e.mediaSequence-i.mediaSequence;this.logger_("live window shift ["+t+"]"),null!==this.mediaIndex&&(this.mediaIndex-=t,this.mediaIndex<0?(this.mediaIndex=null,this.partIndex=null):(r=this.playlist_.segments[this.mediaIndex],!this.partIndex||r.parts&&r.parts.length&&r.parts[this.partIndex]||(r=this.mediaIndex,this.logger_("currently processing part (index "+this.partIndex+") no longer exists."),this.resetLoader(),this.mediaIndex=r))),n&&(n.mediaIndex-=t,n.mediaIndex<0?(n.mediaIndex=null,n.partIndex=null):(0<=n.mediaIndex&&(n.segment=e.segments[n.mediaIndex]),0<=n.partIndex&&n.segment.parts&&(n.part=n.segment.parts[n.partIndex]))),this.syncController_.saveExpiredSegmentInfo(i,e)}},t.pause=function(){this.checkBufferTimeout_&&(window.clearTimeout(this.checkBufferTimeout_),this.checkBufferTimeout_=null)},t.paused=function(){return null===this.checkBufferTimeout_},t.resetEverything=function(e){this.ended_=!1,this.appendInitSegment_={audio:!0,video:!0},this.resetLoader(),this.remove(0,1/0,e),this.transmuxer_&&(this.transmuxer_.postMessage({action:"clearAllMp4Captions"}),this.transmuxer_.postMessage({action:"reset"}))},t.resetLoader=function(){this.fetchAtBuffer_=!1,this.resyncLoader()},t.resyncLoader=function(){this.transmuxer_&&Il(this.transmuxer_),this.mediaIndex=null,this.partIndex=null,this.syncPoint_=null,this.isPendingTimestampOffset_=!1,this.callQueue_=[],this.loadQueue_=[],this.metadataQueue_.id3=[],this.metadataQueue_.caption=[],this.abort(),this.transmuxer_&&this.transmuxer_.postMessage({action:"clearParsedMp4Captions"})},t.remove=function(e,t,i,n){if(void 0===i&&(i=function(){}),void 0===n&&(n=!1),(t=t===1/0?this.duration_():t)<=e)this.logger_("skipping remove because end ${end} is <= start ${start}");else if(this.sourceUpdater_&&this.getMediaInfo_()){var r,a=1,s=function(){0===--a&&i()};for(r in!n&&this.audioDisabled_||(a++,this.sourceUpdater_.removeAudio(e,t,s)),!n&&"main"!==this.loaderType_||(this.gopBuffer_=function(e,t,i,n){for(var r=Math.ceil((t-n)*hl),a=Math.ceil((i-n)*hl),n=e.slice(),s=e.length;s--&&!(e[s].pts<=a););if(-1===s)return n;for(var o=s+1;o--&&!(e[o].pts<=r););return o=Math.max(o,0),n.splice(o,s-o+1),n}(this.gopBuffer_,e,t,this.timeMapping_),a++,this.sourceUpdater_.removeVideo(e,t,s)),this.inbandTextTracks_)rl(e,t,this.inbandTextTracks_[r]);rl(e,t,this.segmentMetadataTrack_),s()}else this.logger_("skipping remove because no source updater or starting media info")},t.monitorBuffer_=function(){this.checkBufferTimeout_&&window.clearTimeout(this.checkBufferTimeout_),this.checkBufferTimeout_=window.setTimeout(this.monitorBufferTick_.bind(this),1)},t.monitorBufferTick_=function(){"READY"===this.state&&this.fillBuffer_(),this.checkBufferTimeout_&&window.clearTimeout(this.checkBufferTimeout_),this.checkBufferTimeout_=window.setTimeout(this.monitorBufferTick_.bind(this),500)},t.fillBuffer_=function(){var e;this.sourceUpdater_.updating()||(e=this.chooseNextRequest_())&&("number"==typeof e.timestampOffset&&(this.isPendingTimestampOffset_=!1,this.timelineChangeController_.pendingTimelineChange({type:this.loaderType_,from:this.currentTimeline_,to:e.timeline})),this.loadSegment_(e))},t.isEndOfStream_=function(e,t,i){if(void 0===e&&(e=this.mediaIndex),void 0===t&&(t=this.playlist_),void 0===i&&(i=this.partIndex),!t||!this.mediaSource_)return!1;var n="number"==typeof e&&t.segments[e],e=e+1===t.segments.length,n=!n||!n.parts||i+1===n.parts.length;return t.endList&&"open"===this.mediaSource_.readyState&&e&&n},t.chooseNextRequest_=function(){var e=this.buffered_(),t=Ho(e)||0,i=qo(e,this.currentTime_()),n=!this.hasPlayed_()&&1<=i,r=i>=this.goalBufferLength_(),e=this.playlist_.segments;if(!e.length||n||r)return null;this.syncPoint_=this.syncPoint_||this.syncController_.getSyncPoint(this.playlist_,this.duration_(),this.currentTimeline_,this.currentTime_());var a,n={partIndex:null,mediaIndex:null,startOfSegment:null,playlist:this.playlist_,isSyncRequest:Boolean(!this.syncPoint_)};n.isSyncRequest?n.mediaIndex=function(e,t,i){t=t||[];for(var n=[],r=0,a=0;a=e.length-1&&s&&!this.seeking_()?null:this.generateSegmentInfo_(n)},t.generateSegmentInfo_=function(e){var t=e.independent,i=e.playlist,n=e.mediaIndex,r=e.startOfSegment,a=e.isSyncRequest,s=e.partIndex,o=e.forceTimestampOffset,u=e.getMediaInfoForTime,l=i.segments[n],e="number"==typeof s&&l.parts[s],t={requestId:"segment-loader-"+Math.random(),uri:e&&e.resolvedUri||l.resolvedUri,mediaIndex:n,partIndex:e?s:null,isSyncRequest:a,startOfSegment:r,playlist:i,bytes:null,encryptedBytes:null,timestampOffset:null,timeline:l.timeline,duration:e&&e.duration||l.duration,segment:l,part:e,byteLength:0,transmuxer:this.transmuxer_,getMediaInfoForTime:u,independent:t},o="undefined"!=typeof o?o:this.isPendingTimestampOffset_;t.timestampOffset=this.timestampOffsetForSegment_({segmentTimeline:l.timeline,currentTimeline:this.currentTimeline_,startOfSegment:r,buffered:this.buffered_(),overrideCheck:o});o=Ho(this.sourceUpdater_.audioBuffered());return"number"==typeof o&&(t.audioAppendStart=o-this.sourceUpdater_.audioTimestampOffset()),this.sourceUpdater_.videoBuffered().length&&(t.gopsToAlignWith=function(e,t,i){if("undefined"==typeof t||null===t||!e.length)return[];for(var n=Math.ceil((t-i+3)*hl),r=0;rn);r++);return e.slice(r)}(this.gopBuffer_,this.currentTime_()-this.sourceUpdater_.videoTimestampOffset(),this.timeMapping_)),t},t.timestampOffsetForSegment_=function(e){return i=(t=e).segmentTimeline,n=t.currentTimeline,r=t.startOfSegment,e=t.buffered,t.overrideCheck||i!==n?!(i "+p+" for "+e),t=m,i=v.vhs_.tech_,t[n=e]||(i.trigger({type:"usage",name:"vhs-608"}),i.trigger({type:"usage",name:"hls-608"}),/^cc708_/.test(r=n)&&(r="SERVICE"+n.split("_")[1]),(o=i.textTracks().getTrackById(r))?t[n]=o:(s=a=n,d=!1,(o=(i.options_.vhs&&i.options_.vhs.captionServices||{})[r])&&(a=o.label,s=o.language,d=o.default),t[n]=i.addRemoteTextTrack({kind:"captions",id:r,default:d,label:a,language:s},!1).track)),rl(h,p,m[e]),l=(f={captionArray:f,inbandTextTracks:m,timestampOffset:g}).inbandTextTracks,m=f.captionArray,c=f.timestampOffset,m&&(u=window.WebKitDataCue||window.VTTCue,m.forEach(function(e){var t=e.stream;l[t].addCue(new u(e.startTime+c,e.endTime+c,e.text))}))}),this.transmuxer_&&this.transmuxer_.postMessage({action:"clearParsedMp4Captions"})):this.metadataQueue_.caption.push(this.handleCaptions_.bind(this,e,t)):this.logger_("SegmentLoader received no captions from a caption event"))},t.handleId3_=function(e,t,i){var n,r,a,s;this.earlyAbortWhenNeeded_(e.stats),this.checkForAbort_(e.requestId)||(this.pendingSegment_.hasAppendedData_?(n=null===this.sourceUpdater_.videoTimestampOffset()?this.sourceUpdater_.audioTimestampOffset():this.sourceUpdater_.videoTimestampOffset(),r=this.inbandTextTracks_,a=i,s=this.vhs_.tech_,r.metadataTrack_||(r.metadataTrack_=s.addRemoteTextTrack({kind:"metadata",label:"Timed Metadata"},!1).track,r.metadataTrack_.inBandMetadataTrackDispatchType=a),nl({inbandTextTracks:this.inbandTextTracks_,metadataArray:t,timestampOffset:n,videoDuration:this.duration_()})):this.metadataQueue_.id3.push(this.handleId3_.bind(this,e,t,i)))},t.processMetadataQueue_=function(){this.metadataQueue_.id3.forEach(function(e){return e()}),this.metadataQueue_.caption.forEach(function(e){return e()}),this.metadataQueue_.id3=[],this.metadataQueue_.caption=[]},t.processCallQueue_=function(){var e=this.callQueue_;this.callQueue_=[],e.forEach(function(e){return e()})},t.processLoadQueue_=function(){var e=this.loadQueue_;this.loadQueue_=[],e.forEach(function(e){return e()})},t.hasEnoughInfoToLoad_=function(){if("audio"!==this.loaderType_)return!0;var e=this.pendingSegment_;return!!e&&(!this.getCurrentMediaInfo_()||!ul({timelineChangeController:this.timelineChangeController_,currentTimeline:this.currentTimeline_,segmentTimeline:e.timeline,loaderType:this.loaderType_,audioDisabled:this.audioDisabled_}))},t.getCurrentMediaInfo_=function(e){return(e=void 0===e?this.pendingSegment_:e)&&e.trackInfo||this.currentMediaInfo_},t.getMediaInfo_=function(e){return void 0===e&&(e=this.pendingSegment_),this.getCurrentMediaInfo_(e)||this.startingMediaInfo_},t.hasEnoughInfoToAppend_=function(){if(!this.sourceUpdater_.ready())return!1;if(this.waitingOnRemove_||this.quotaExceededErrorRetryTimeout_)return!1;var e=this.pendingSegment_,t=this.getCurrentMediaInfo_();if(!e||!t)return!1;var i=t.hasAudio,n=t.hasVideo,t=t.isMuxed;return!(n&&!e.videoTimingInfo)&&(!(i&&!this.audioDisabled_&&!t&&!e.audioTimingInfo)&&!ul({timelineChangeController:this.timelineChangeController_,currentTimeline:this.currentTimeline_,segmentTimeline:e.timeline,loaderType:this.loaderType_,audioDisabled:this.audioDisabled_}))},t.handleData_=function(e,t){if(this.earlyAbortWhenNeeded_(e.stats),!this.checkForAbort_(e.requestId))if(!this.callQueue_.length&&this.hasEnoughInfoToAppend_()){var i,n=this.pendingSegment_;if(this.setTimeMapping_(n.timeline),this.updateMediaSecondsLoaded_(n.part||n.segment),"closed"!==this.mediaSource_.readyState){if(e.map&&(e.map=this.initSegmentForMap(e.map,!0),n.segment.map=e.map),e.key&&this.segmentKey(e.key,!0),n.isFmp4=e.isFmp4,n.timingInfo=n.timingInfo||{},n.isFmp4?(this.trigger("fmp4"),n.timingInfo.start=n[ol(t.type)].start):(i=this.getCurrentMediaInfo_(),(i="main"===this.loaderType_&&i&&i.hasVideo)&&(r=n.videoTimingInfo.start),n.timingInfo.start=this.trueSegmentStart_({currentStart:n.timingInfo.start,playlist:n.playlist,mediaIndex:n.mediaIndex,currentVideoTimestampOffset:this.sourceUpdater_.videoTimestampOffset(),useVideoTimingInfo:i,firstVideoFrameTimeForData:r,videoTimingInfo:n.videoTimingInfo,audioTimingInfo:n.audioTimingInfo})),this.updateAppendInitSegmentStatus(n,t.type),this.updateSourceBufferTimestampOffset_(n),n.isSyncRequest){this.updateTimingInfoEnd_(n),this.syncController_.saveSegmentTimingInfo({segmentInfo:n,shouldSaveTimelineMapping:"main"===this.loaderType_});var r=this.chooseNextRequest_();if(r.mediaIndex!==n.mediaIndex||r.partIndex!==n.partIndex)return void this.logger_("sync segment was incorrect, not appending");this.logger_("sync segment was correct, appending")}n.hasAppendedData_=!0,this.processMetadataQueue_(),this.appendData_(n,t)}}else this.callQueue_.push(this.handleData_.bind(this,e,t))},t.updateAppendInitSegmentStatus=function(e,t){"main"!==this.loaderType_||"number"!=typeof e.timestampOffset||e.changedTimestampOffset||(this.appendInitSegment_={audio:!0,video:!0}),this.playlistOfLastInitSegment_[t]!==e.playlist&&(this.appendInitSegment_[t]=!0)},t.getInitSegmentAndUpdateState_=function(e){var t=e.type,i=e.initSegment,n=e.map,r=e.playlist;if(n){e=Su(n);if(this.activeInitSegmentId_===e)return null;i=this.initSegmentForMap(n,!0).bytes,this.activeInitSegmentId_=e}return i&&this.appendInitSegment_[t]?(this.playlistOfLastInitSegment_[t]=r,this.appendInitSegment_[t]=!1,this.activeInitSegmentId_=null,i):null},t.handleQuotaExceededError_=function(e,t){var i=this,n=e.segmentInfo,r=e.type,a=e.bytes,s=this.sourceUpdater_.audioBuffered(),o=this.sourceUpdater_.videoBuffered();1=n);r++);return e.slice(0,r).concat(t)}(this.gopBuffer_,i.gopInfo,this.safeAppend_)),this.state="APPENDING",this.trigger("appending"),this.waitForAppendsToComplete_(e)}},t.setTimeMapping_=function(e){e=this.syncController_.mappingForTimeline(e);null!==e&&(this.timeMapping_=e)},t.updateMediaSecondsLoaded_=function(e){"number"==typeof e.start&&"number"==typeof e.end?this.mediaSecondsLoaded+=e.end-e.start:this.mediaSecondsLoaded+=e.duration},t.shouldUpdateTransmuxerTimestampOffset_=function(e){return null!==e&&("main"===this.loaderType_&&e!==this.sourceUpdater_.videoTimestampOffset()||!this.audioDisabled_&&e!==this.sourceUpdater_.audioTimestampOffset())},t.trueSegmentStart_=function(e){var t=e.currentStart,i=e.playlist,n=e.mediaIndex,r=e.firstVideoFrameTimeForData,a=e.currentVideoTimestampOffset,s=e.useVideoTimingInfo,o=e.videoTimingInfo,e=e.audioTimingInfo;if("undefined"!=typeof t)return t;if(!s)return e.start;i=i.segments[n-1];return 0!==n&&i&&"undefined"!=typeof i.start&&i.end===r+a?o.start:r},t.waitForAppendsToComplete_=function(e){var t=this.getCurrentMediaInfo_(e);if(!t)return this.error({message:"No starting media returned, likely due to an unsupported media format.",blacklistDuration:1/0}),void this.trigger("error");var i=t.hasAudio,n=t.hasVideo,t=t.isMuxed,n="main"===this.loaderType_&&n,t=!this.audioDisabled_&&i&&!t;if(e.waitingOnAppends=0,!e.hasAppendedData_)return e.timingInfo||"number"!=typeof e.timestampOffset||(this.isPendingTimestampOffset_=!0),e.timingInfo={start:0},e.waitingOnAppends++,this.isPendingTimestampOffset_||(this.updateSourceBufferTimestampOffset_(e),this.processMetadataQueue_()),void this.checkAppendsDone_(e);n&&e.waitingOnAppends++,t&&e.waitingOnAppends++,n&&this.sourceUpdater_.videoQueueCallback(this.checkAppendsDone_.bind(this,e)),t&&this.sourceUpdater_.audioQueueCallback(this.checkAppendsDone_.bind(this,e))},t.checkAppendsDone_=function(e){this.checkForAbort_(e.requestId)||(e.waitingOnAppends--,0===e.waitingOnAppends&&this.handleAppendsDone_())},t.checkForIllegalMediaSwitch=function(e){var t,i,e=(t=this.loaderType_,i=this.getCurrentMediaInfo_(),e=e,"main"===t&&i&&e?e.hasAudio||e.hasVideo?i.hasVideo&&!e.hasVideo?"Only audio found in segment when we expected video. We can't switch to audio only from a stream that had video. To get rid of this message, please add codec information to the manifest.":!i.hasVideo&&e.hasVideo?"Video found in segment when we expected only audio. We can't switch to a stream with video from an audio only stream. To get rid of this message, please add codec information to the manifest.":null:"Neither audio nor video found in segment.":null);return!!e&&(this.error({message:e,blacklistDuration:1/0}),this.trigger("error"),!0)},t.updateSourceBufferTimestampOffset_=function(e){var t;null===e.timestampOffset||"number"!=typeof e.timingInfo.start||e.changedTimestampOffset||"main"!==this.loaderType_||(t=!1,e.timestampOffset-=this.getSegmentStartTimeForTimestampOffsetCalculation_({videoTimingInfo:e.segment.videoTimingInfo,audioTimingInfo:e.segment.audioTimingInfo,timingInfo:e.timingInfo}),e.changedTimestampOffset=!0,e.timestampOffset!==this.sourceUpdater_.videoTimestampOffset()&&(this.sourceUpdater_.videoTimestampOffset(e.timestampOffset),t=!0),e.timestampOffset!==this.sourceUpdater_.audioTimestampOffset()&&(this.sourceUpdater_.audioTimestampOffset(e.timestampOffset),t=!0),t&&this.trigger("timestampoffset"))},t.getSegmentStartTimeForTimestampOffsetCalculation_=function(e){var t=e.videoTimingInfo,i=e.audioTimingInfo,e=e.timingInfo;return this.useDtsForTimestampOffset_?t&&"number"==typeof t.transmuxedDecodeStart?t.transmuxedDecodeStart:i&&"number"==typeof i.transmuxedDecodeStart?i.transmuxedDecodeStart:e.start:e.start},t.updateTimingInfoEnd_=function(e){e.timingInfo=e.timingInfo||{};var t=this.getMediaInfo_(),t="main"===this.loaderType_&&t&&t.hasVideo&&e.videoTimingInfo?e.videoTimingInfo:e.audioTimingInfo;t&&(e.timingInfo.end="number"==typeof t.end?t.end:t.start+e.duration)},t.handleAppendsDone_=function(){if(this.pendingSegment_&&this.trigger("appendsdone"),!this.pendingSegment_)return this.state="READY",void(this.paused()||this.monitorBuffer_());var e=this.pendingSegment_;this.updateTimingInfoEnd_(e),this.shouldSaveSegmentTimingInfo_&&this.syncController_.saveSegmentTimingInfo({segmentInfo:e,shouldSaveTimelineMapping:"main"===this.loaderType_});var t=cl(e,this.sourceType_);if(t&&("warn"===t.severity?tr.log.warn(t.message):this.logger_(t.message)),this.recordThroughput_(e),this.pendingSegment_=null,this.state="READY",!e.isSyncRequest||(this.trigger("syncinfoupdate"),e.hasAppendedData_)){this.logger_("Appended "+sl(e)),this.addSegmentMetadataCue_(e),this.fetchAtBuffer_=!0,this.currentTimeline_!==e.timeline&&(this.timelineChangeController_.lastTimelineChange({type:this.loaderType_,from:this.currentTimeline_,to:e.timeline}),"main"!==this.loaderType_||this.audioDisabled_||this.timelineChangeController_.lastTimelineChange({type:"audio",from:this.currentTimeline_,to:e.timeline})),this.currentTimeline_=e.timeline,this.trigger("syncinfoupdate");var i=e.segment,t=e.part,i=i.end&&this.currentTime_()-i.end>3*e.playlist.targetDuration,t=t&&t.end&&this.currentTime_()-t.end>3*e.playlist.partTargetDuration;if(i||t)return this.logger_("bad "+(i?"segment":"part")+" "+sl(e)),void this.resetEverything();null!==this.mediaIndex&&this.trigger("bandwidthupdate"),this.trigger("progress"),this.mediaIndex=e.mediaIndex,this.partIndex=e.partIndex,this.isEndOfStream_(e.mediaIndex,e.playlist,e.partIndex)&&this.endOfStream(),this.trigger("appended"),e.hasAppendedData_&&this.mediaAppends++,this.paused()||this.monitorBuffer_()}else this.logger_("Throwing away un-appended sync request "+sl(e))},t.recordThroughput_=function(e){var t,i;e.duration<1/60?this.logger_("Ignoring segment's throughput because its duration of "+e.duration+" is less than the min to record "+1/60):(t=this.throughput.rate,i=Date.now()-e.endOfAllRequests+1,i=Math.floor(e.byteLength/i*8*1e3),this.throughput.rate+=(i-t)/++this.throughput.count)},t.addSegmentMetadataCue_=function(e){var t,i,n,r;this.segmentMetadataTrack_&&(i=(t=e.segment).start,r=t.end,al(i)&&al(r)&&(rl(i,r,this.segmentMetadataTrack_),n=window.WebKitDataCue||window.VTTCue,e={custom:t.custom,dateTimeObject:t.dateTimeObject,dateTimeString:t.dateTimeString,bandwidth:e.playlist.attributes.BANDWIDTH,resolution:e.playlist.attributes.RESOLUTION,codecs:e.playlist.attributes.CODECS,byteLength:e.byteLength,uri:e.uri,timeline:e.timeline,playlist:e.playlist.id,start:i,end:r},(r=new n(i,r,JSON.stringify(e))).value=e,this.segmentMetadataTrack_.addCue(r)))},e}(tr.EventTarget);function Rl(){}function Nl(e){return"string"!=typeof e?e:e.replace(/./,function(e){return e.toUpperCase()})}function Ul(e,t){var i=t[e+"Buffer"];return i&&i.updating||t.queuePending[e]}function Bl(e,t){if(0!==t.queue.length){var i=0,n=t.queue[i];if("mediaSource"!==n.type){if("mediaSource"!==e&&t.ready()&&"closed"!==t.mediaSource.readyState&&!Ul(e,t)){if(n.type!==e){if(null===(i=function(e,t){for(var i=0;i=e.playlist.segments.length){e=null;break}e=this.generateSegmentInfo_({playlist:e.playlist,mediaIndex:e.mediaIndex+1,startOfSegment:e.startOfSegment+e.duration,isSyncRequest:e.isSyncRequest})}return e},t.stopForError=function(e){this.error(e),this.state="READY",this.pause(),this.trigger("error")},t.segmentRequestFinished_=function(e,t,i){var n=this;if(this.subtitlesTrack_){if(this.saveTransferStats_(t.stats),!this.pendingSegment_)return this.state="READY",void(this.mediaRequestsAborted+=1);if(e)return e.code===Pl&&this.handleTimeout_(),e.code===Ll?this.mediaRequestsAborted+=1:this.mediaRequestsErrored+=1,void this.stopForError(e);var r=this.pendingSegment_;this.saveBandwidthRelatedStats_(r.duration,t.stats),this.state="APPENDING",this.trigger("appending");var a=r.segment;if(a.map&&(a.map.bytes=t.map.bytes),r.bytes=t.bytes,"function"!=typeof window.WebVTT&&this.subtitlesTrack_&&this.subtitlesTrack_.tech_){var s=function(){n.subtitlesTrack_.tech_.off("vttjsloaded",o),n.stopForError({message:"Error loading vtt.js"})},o=function(){n.subtitlesTrack_.tech_.off("vttjserror",s),n.segmentRequestFinished_(e,t,i)};return this.state="WAITING_ON_VTTJS",this.subtitlesTrack_.tech_.one("vttjsloaded",o),void this.subtitlesTrack_.tech_.one("vttjserror",s)}a.requested=!0;try{this.parseVTTCues_(r)}catch(e){return void this.stopForError({message:e.message})}if(this.updateTimeMapping_(r,this.syncController_.timelines[r.timeline],this.playlist_),r.cues.length?r.timingInfo={start:r.cues[0].startTime,end:r.cues[r.cues.length-1].endTime}:r.timingInfo={start:r.startOfSegment,end:r.startOfSegment+r.duration},r.isSyncRequest)return this.trigger("syncinfoupdate"),this.pendingSegment_=null,void(this.state="READY");r.byteLength=r.bytes.byteLength,this.mediaSecondsLoaded+=a.duration,r.cues.forEach(function(e){n.subtitlesTrack_.addCue(n.featuresNativeTextTracks_?new window.VTTCue(e.startTime,e.endTime,e.text):e)}),function(t){var e=t.cues;if(e)for(var i=0;iu)&&(r=void 0,r=o<0?i.start-Qo({defaultDuration:t.targetDuration,durationList:t.segments,startIndex:e.mediaIndex,endIndex:a}):i.end+Qo({defaultDuration:t.targetDuration,durationList:t.segments,startIndex:e.mediaIndex+1,endIndex:a}),this.discontinuities[s]={time:r,accuracy:u})}},t.dispose=function(){this.trigger("dispose"),this.off()},e}(tr.EventTarget),pc=function(t){function e(){var e=t.call(this)||this;return e.pendingTimelineChanges_={},e.lastTimelineChanges_={},e}mt(e,t);var i=e.prototype;return i.clearPendingTimelineChange=function(e){this.pendingTimelineChanges_[e]=null,this.trigger("pendingtimelinechange")},i.pendingTimelineChange=function(e){var t=e.type,i=e.from,e=e.to;return"number"==typeof i&&"number"==typeof e&&(this.pendingTimelineChanges_[t]={type:t,from:i,to:e},this.trigger("pendingtimelinechange")),this.pendingTimelineChanges_[t]},i.lastTimelineChange=function(e){var t=e.type,i=e.from,e=e.to;return"number"==typeof i&&"number"==typeof e&&(this.lastTimelineChanges_[t]={type:t,from:i,to:e},delete this.pendingTimelineChanges_[t],this.trigger("timelinechange")),this.lastTimelineChanges_[t]},i.dispose=function(){this.trigger("dispose"),this.pendingTimelineChanges_={},this.lastTimelineChanges_={},this.off()},e}(tr.EventTarget),fc=x(U(W(function(){var e="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function t(e,t,i){return e(i={path:t,exports:{},require:function(e,t){return function(){throw new Error("Dynamic requires are not currently supported by @rollup/plugin-commonjs")}(null==t&&i.path)}},i.exports),i.exports}var i=t(function(e){function n(e,t){for(var i=0;i>7))^f]=f;for(e=t=0;!c[e];e^=i||1,t=p[t]||1)for(s=16843009*h[n=h[i=h[d[c[e]=r=(r=t^t<<1^t<<2^t<<3^t<<4)>>8^255&r^99]=e]]]^65537*n^257*i^16843008*e,a=257*h[r]^16843008*r,f=0;f<4;f++)u[f][e]=a=a<<24^a>>>8,l[f][r]=s=s<<24^s>>>8;for(f=0;f<5;f++)u[f]=u[f].slice(0),l[f]=l[f].slice(0);return o}(),this._tables=[[c[0][0].slice(),c[0][1].slice(),c[0][2].slice(),c[0][3].slice(),c[0][4].slice()],[c[1][0].slice(),c[1][1].slice(),c[1][2].slice(),c[1][3].slice(),c[1][4].slice()]];var r=this._tables[0][4],a=this._tables[1],s=e.length,o=1;if(4!==s&&6!==s&&8!==s)throw new Error("Invalid aes key size");var u=e.slice(0),l=[];for(this._key=[u,l],t=s;t<4*s+28;t++)n=u[t-1],(t%s==0||8===s&&t%s==4)&&(n=r[n>>>24]<<24^r[n>>16&255]<<16^r[n>>8&255]<<8^r[255&n],t%s==0&&(n=n<<8^n>>>24^o<<24,o=o<<1^283*(o>>7))),u[t]=u[t-s]^n;for(i=0;t;i++,t--)n=u[3&i?t:t-4],l[i]=t<=4||i<4?n:a[0][r[n>>>24]]^a[1][r[n>>16&255]]^a[2][r[n>>8&255]]^a[3][r[255&n]]}return e.prototype.decrypt=function(e,t,i,n,r,a){for(var s,o,u,l=this._key[1],c=e^l[0],d=n^l[1],h=i^l[2],p=t^l[3],f=l.length/4-2,m=4,t=this._tables[1],g=t[0],y=t[1],v=t[2],_=t[3],b=t[4],T=0;T>>24]^y[d>>16&255]^v[h>>8&255]^_[255&p]^l[m],o=g[d>>>24]^y[h>>16&255]^v[p>>8&255]^_[255&c]^l[m+1],u=g[h>>>24]^y[p>>16&255]^v[c>>8&255]^_[255&d]^l[m+2],p=g[p>>>24]^y[c>>16&255]^v[d>>8&255]^_[255&h]^l[m+3],m+=4,c=s,d=o,h=u;for(T=0;T<4;T++)r[(3&-T)+a]=b[c>>>24]<<24^b[d>>16&255]<<16^b[h>>8&255]<<8^b[255&p]^l[m++],s=c,c=d,d=h,h=p,p=s},e}(),l=function(t){function e(){var e=t.call(this,a)||this;return e.jobs=[],e.delay=1,e.timeout_=null,e}r(e,t);var i=e.prototype;return i.processJob_=function(){this.jobs.shift()(),this.jobs.length?this.timeout_=setTimeout(this.processJob_.bind(this),this.delay):this.timeout_=null},i.push=function(e){this.jobs.push(e),this.timeout_||(this.timeout_=setTimeout(this.processJob_.bind(this),this.delay))},e}(a),g=function(e){return e<<24|(65280&e)<<8|(16711680&e)>>8|e>>>24},s=function(){function u(e,t,i,n){var r=u.STEP,a=new Int32Array(e.buffer),s=new Uint8Array(e.byteLength),o=0;for(this.asyncStream_=new l,this.asyncStream_.push(this.decryptChunk_(a.subarray(o,o+r),t,i,s)),o=r;o>2),u=new m(Array.prototype.slice.call(t)),e=new Uint8Array(e.byteLength),l=new Int32Array(e.buffer),c=i[0],d=i[1],h=i[2],p=i[3],f=0;f "+n+" from "+t),this.tech_.trigger({type:"usage",name:"vhs-rendition-change-"+t})),this.masterPlaylistLoader_.media(e,i)},t.startABRTimer_=function(){var e=this;this.stopABRTimer_(),this.abrTimer_=window.setInterval(function(){return e.checkABR_()},250)},t.stopABRTimer_=function(){this.tech_.scrubbing&&this.tech_.scrubbing()||(window.clearInterval(this.abrTimer_),this.abrTimer_=null)},t.getAudioTrackPlaylists_=function(){var e=this.master(),t=e&&e.playlists||[];if(!e||!e.mediaGroups||!e.mediaGroups.AUDIO)return t;var i,n=e.mediaGroups.AUDIO,r=Object.keys(n);if(Object.keys(this.mediaTypes_.AUDIO.groups).length)i=this.mediaTypes_.AUDIO.activeTrack();else{var a,s=n.main||r.length&&n[r[0]];for(a in s)if(s[a].default){i={label:a};break}}if(!i)return t;var o,u=[];for(o in n)if(n[o][i.label]){var l=n[o][i.label];if(l.playlists&&l.playlists.length)u.push.apply(u,l.playlists);else if(l.uri)u.push(l);else if(e.playlists.length)for(var c=0;c "+r.id;if(!t)return l(c+" as current playlist is not set"),!0;if(r.id===t.id)return!1;e=Boolean(Uo(i,n).length);if(!t.endList)return e||"number"!=typeof t.partTargetDuration?(l(c+" as current playlist is live"),!0):(l("not "+c+" as current playlist is live llhls, but currentTime isn't in buffered."),!1);i=qo(i,n),n=u?El.EXPERIMENTAL_MAX_BUFFER_LOW_WATER_LINE:El.MAX_BUFFER_LOW_WATER_LINE;if(o= bufferLowWaterLine ("+i+" >= "+a+")";return u&&(a+=" and next bandwidth > current bandwidth ("+n+" > "+r+")"),l(a),!0}return l("not "+c+" as no switching criteria met"),!1}({buffered:this.tech_.buffered(),currentTime:i,currentPlaylist:t,nextPlaylist:e,bufferLowWaterLine:n,bufferHighWaterLine:r,duration:this.duration(),experimentalBufferBasedABR:this.experimentalBufferBasedABR,log:this.logger_})},t.setupSegmentLoaderListeners_=function(){var t=this;this.experimentalBufferBasedABR||(this.mainSegmentLoader_.on("bandwidthupdate",function(){var e=t.selectPlaylist();t.shouldSwitchToMedia_(e)&&t.switchMedia_(e,"bandwidthupdate"),t.tech_.trigger("bandwidthupdate")}),this.mainSegmentLoader_.on("progress",function(){t.trigger("progress")})),this.mainSegmentLoader_.on("error",function(){t.blacklistCurrentPlaylist(t.mainSegmentLoader_.error())}),this.mainSegmentLoader_.on("appenderror",function(){t.error=t.mainSegmentLoader_.error_,t.trigger("error")}),this.mainSegmentLoader_.on("syncinfoupdate",function(){t.onSyncInfoUpdate_()}),this.mainSegmentLoader_.on("timestampoffset",function(){t.tech_.trigger({type:"usage",name:"vhs-timestamp-offset"}),t.tech_.trigger({type:"usage",name:"hls-timestamp-offset"})}),this.audioSegmentLoader_.on("syncinfoupdate",function(){t.onSyncInfoUpdate_()}),this.audioSegmentLoader_.on("appenderror",function(){t.error=t.audioSegmentLoader_.error_,t.trigger("error")}),this.mainSegmentLoader_.on("ended",function(){t.logger_("main segment loader ended"),t.onEndOfStream()}),this.mainSegmentLoader_.on("earlyabort",function(e){t.experimentalBufferBasedABR||(t.delegateLoaders_("all",["abort"]),t.blacklistCurrentPlaylist({message:"Aborted early because there isn't enough bandwidth to complete the request without rebuffering."},120))});function e(){if(!t.sourceUpdater_.hasCreatedSourceBuffers())return t.tryToCreateSourceBuffers_();var e=t.getCodecsOrExclude_();e&&t.sourceUpdater_.addOrChangeSourceBuffers(e)}this.mainSegmentLoader_.on("trackinfo",e),this.audioSegmentLoader_.on("trackinfo",e),this.mainSegmentLoader_.on("fmp4",function(){t.triggeredFmp4Usage||(t.tech_.trigger({type:"usage",name:"vhs-fmp4"}),t.tech_.trigger({type:"usage",name:"hls-fmp4"}),t.triggeredFmp4Usage=!0)}),this.audioSegmentLoader_.on("fmp4",function(){t.triggeredFmp4Usage||(t.tech_.trigger({type:"usage",name:"vhs-fmp4"}),t.tech_.trigger({type:"usage",name:"hls-fmp4"}),t.triggeredFmp4Usage=!0)}),this.audioSegmentLoader_.on("ended",function(){t.logger_("audioSegmentLoader ended"),t.onEndOfStream()})},t.mediaSecondsLoaded_=function(){return Math.max(this.audioSegmentLoader_.mediaSecondsLoaded+this.mainSegmentLoader_.mediaSecondsLoaded)},t.load=function(){this.mainSegmentLoader_.load(),this.mediaTypes_.AUDIO.activePlaylistLoader&&this.audioSegmentLoader_.load(),this.mediaTypes_.SUBTITLES.activePlaylistLoader&&this.subtitleSegmentLoader_.load()},t.smoothQualityChange_=function(e){void 0===e&&(e=this.selectPlaylist()),this.fastQualityChange_(e)},t.fastQualityChange_=function(e){var t=this;(e=void 0===e?this.selectPlaylist():e)!==this.masterPlaylistLoader_.media()?(this.switchMedia_(e,"fast-quality"),this.mainSegmentLoader_.resetEverything(function(){tr.browser.IE_VERSION||tr.browser.IS_EDGE?t.tech_.setCurrentTime(t.tech_.currentTime()+.04):t.tech_.setCurrentTime(t.tech_.currentTime())})):this.logger_("skipping fastQualityChange because new media is same as old")},t.play=function(){if(!this.setupFirstPlay()){this.tech_.ended()&&this.tech_.setCurrentTime(0),this.hasPlayed_&&this.load();var e=this.tech_.seekable();return this.tech_.duration()===1/0&&this.tech_.currentTime()this.maxPlaylistRetries?1/0:Date.now()+1e3*t,i.excludeUntil=a,e.reason&&(i.lastExcludeReason_=e.reason),this.tech_.trigger("blacklistplaylist"),this.tech_.trigger({type:"usage",name:"vhs-rendition-blacklisted"}),this.tech_.trigger({type:"usage",name:"hls-rendition-blacklisted"});r=this.selectPlaylist();if(!r)return this.error="Playback cannot continue. No available working or supported playlists.",void this.trigger("error");t=e.internal?this.logger_:tr.log.warn,a=e.message?" "+e.message:"";t((e.internal?"Internal problem":"Problem")+" encountered with playlist "+i.id+"."+a+" Switching to playlist "+r.id+"."),r.attributes.AUDIO!==i.attributes.AUDIO&&this.delegateLoaders_("audio",["abort","pause"]),r.attributes.SUBTITLES!==i.attributes.SUBTITLES&&this.delegateLoaders_("subtitle",["abort","pause"]),this.delegateLoaders_("main",["abort","pause"]);a=r.targetDuration/2*1e3||5e3,a="number"==typeof r.lastRequest&&Date.now()-r.lastRequest<=a;return this.switchMedia_(r,"exclude",s||a)},t.pauseLoading=function(){this.delegateLoaders_("all",["abort","pause"]),this.stopABRTimer_()},t.delegateLoaders_=function(i,e){var n=this,r=[],t="all"===i;!t&&"main"!==i||r.push(this.masterPlaylistLoader_);var a=[];!t&&"audio"!==i||a.push("AUDIO"),!t&&"subtitle"!==i||(a.push("CLOSED-CAPTIONS"),a.push("SUBTITLES")),a.forEach(function(e){e=n.mediaTypes_[e]&&n.mediaTypes_[e].activePlaylistLoader;e&&r.push(e)}),["main","audio","subtitle"].forEach(function(e){var t=n[e+"SegmentLoader_"];!t||i!==e&&"all"!==i||r.push(t)}),r.forEach(function(t){return e.forEach(function(e){"function"==typeof t[e]&&t[e]()})})},t.setCurrentTime=function(e){var t=Uo(this.tech_.buffered(),e);return this.masterPlaylistLoader_&&this.masterPlaylistLoader_.media()&&this.masterPlaylistLoader_.media().segments?t&&t.length?e:(this.mainSegmentLoader_.resetEverything(),this.mainSegmentLoader_.abort(),this.mediaTypes_.AUDIO.activePlaylistLoader&&(this.audioSegmentLoader_.resetEverything(),this.audioSegmentLoader_.abort()),this.mediaTypes_.SUBTITLES.activePlaylistLoader&&(this.subtitleSegmentLoader_.resetEverything(),this.subtitleSegmentLoader_.abort()),void this.load()):0},t.duration=function(){if(!this.masterPlaylistLoader_)return 0;var e=this.masterPlaylistLoader_.media();return e?e.endList?this.mediaSource?this.mediaSource.duration:Ql.Playlist.duration(e):1/0:0},t.seekable=function(){return this.seekable_},t.onSyncInfoUpdate_=function(){var e;if(this.masterPlaylistLoader_){var t=this.masterPlaylistLoader_.media();if(t){var i=this.syncController_.getExpiredTime(t,this.duration());if(null!==i){var n,r,a=this.masterPlaylistLoader_.master,s=Ql.Playlist.seekable(t,i,Ql.Playlist.liveEdgeDelay(a,t));if(0!==s.length){if(this.mediaTypes_.AUDIO.activePlaylistLoader){if(t=this.mediaTypes_.AUDIO.activePlaylistLoader.media(),null===(i=this.syncController_.getExpiredTime(t,this.duration())))return;if(0===(e=Ql.Playlist.seekable(t,i,Ql.Playlist.liveEdgeDelay(a,t))).length)return}this.seekable_&&this.seekable_.length&&(n=this.seekable_.end(0),r=this.seekable_.start(0)),!e||e.start(0)>s.end(0)||s.start(0)>e.end(0)?this.seekable_=s:this.seekable_=tr.createTimeRanges([[(e.start(0)>s.start(0)?e:s).start(0),(e.end(0) "'+a[e]+'"')}),u.length)return void this.blacklistCurrentPlaylist({playlist:this.media(),message:"Codec switching not supported: "+u.join(", ")+".",blacklistDuration:1/0,internal:!0})}return a}t=Object.keys(o).reduce(function(e,t){return e&&(e+=", "),e+=t+' does not support codec(s): "'+o[t].join(",")+'"'},"")+".";this.blacklistCurrentPlaylist({playlist:this.media(),internal:!0,message:t,blacklistDuration:1/0})}else this.blacklistCurrentPlaylist({playlist:this.media(),message:"Could not determine codecs for playlist.",blacklistDuration:1/0})},t.tryToCreateSourceBuffers_=function(){var e;"open"!==this.mediaSource.readyState||this.sourceUpdater_.hasCreatedSourceBuffers()||!this.areMediaTypesKnown_()||(e=this.getCodecsOrExclude_())&&(this.sourceUpdater_.createSourceBuffers(e),e=[e.video,e.audio].filter(Boolean).join(","),this.excludeIncompatibleVariants_(e))},t.excludeUnsupportedVariants_=function(){var n=this,r=this.master().playlists,a=[];Object.keys(r).forEach(function(e){var t,i=r[e];-1===a.indexOf(i.id)&&(a.push(i.id),t=[],!(e=$u(n.master,i)).audio||yr(e.audio)||gr(e.audio)||t.push("audio codec "+e.audio),!e.video||yr(e.video)||gr(e.video)||t.push("video codec "+e.video),e.text&&"stpp.ttml.im1t"===e.text&&t.push("text codec "+e.text),t.length&&(i.excludeUntil=1/0,n.logger_("excluding "+i.id+" for unsupported: "+t.join(", "))))})},t.excludeIncompatibleVariants_=function(e){var r=this,a=[],s=this.master().playlists,e=Yu(pr(e)),o=Qu(e),u=e.video&&pr(e.video)[0]||null,l=e.audio&&pr(e.audio)[0]||null;Object.keys(s).forEach(function(e){var t,i,n=s[e];-1===a.indexOf(n.id)&&n.excludeUntil!==1/0&&(a.push(n.id),t=[],i=$u(r.masterPlaylistLoader_.master,n),e=Qu(i),(i.audio||i.video)&&(e!==o&&t.push('codec count "'+e+'" !== "'+o+'"'),r.sourceUpdater_.canChangeType()||(e=i.video&&pr(i.video)[0]||null,i=i.audio&&pr(i.audio)[0]||null,e&&u&&e.type.toLowerCase()!==u.type.toLowerCase()&&t.push('video codec "'+e.type+'" !== "'+u.type+'"'),i&&l&&i.type.toLowerCase()!==l.type.toLowerCase()&&t.push('audio codec "'+i.type+'" !== "'+l.type+'"')),t.length&&(n.excludeUntil=1/0,r.logger_("blacklisting "+n.id+": "+t.join(" && ")))))})},t.updateAdCues_=function(e){var t=0,i=this.seekable();i.length&&(t=i.start(0)),function(e,t,i){if(void 0===i&&(i=0),e.segments)for(var n=i,r=0;r=r.adStartTime&&t<=r.adEndTime)return r}return null}(t,n+u.duration/2)){if("cueIn"in u){o.endTime=n,o.adEndTime=n,n+=u.duration,o=null;continue}if(n=t.end(t.length-1)))return this.techWaiting_();5<=this.consecutiveUpdates&&e===this.lastRecordedTime?(this.consecutiveUpdates++,this.waiting_()):e===this.lastRecordedTime?this.consecutiveUpdates++:(this.consecutiveUpdates=0,this.lastRecordedTime=e)}},t.cancelTimer_=function(){this.consecutiveUpdates=0,this.timer_&&(this.logger_("cancelTimer_"),clearTimeout(this.timer_)),this.timer_=null},t.fixesBadSeeks_=function(){if(!this.tech_.seeking())return!1;var e,t=this.seekable(),i=this.tech_.currentTime();if(this.afterSeekableWindow_(t,i,this.media(),this.allowSeeksWithinUnsafeLiveWindow)&&(e=t.end(t.length-1)),"undefined"!=typeof(e=this.beforeSeekableWindow_(t,i)?(a=t.start(0))+(a===t.end(0)?0:.1):e))return this.logger_("Trying to seek outside of seekable at time "+i+" with seekable range "+Fo(t)+". Seeking to "+e+"."),this.tech_.setCurrentTime(e),!0;for(var n=this.masterPlaylistController_.sourceUpdater_,r=this.tech_.buffered(),a=n.audioBuffer?n.audioBuffered():null,t=n.videoBuffer?n.videoBuffered():null,n=this.media(),s=n.partTargetDuration||2*(n.targetDuration-fl),o=[a,t],u=0;u "+t.end(0)+"]. Attempting to resume playback by seeking to the current time."),this.tech_.trigger({type:"usage",name:"vhs-unknown-waiting"}),this.tech_.trigger({type:"usage",name:"hls-unknown-waiting"})))},t.techWaiting_=function(){var e=this.seekable(),t=this.tech_.currentTime();if(this.tech_.seeking()||null!==this.timer_)return!0;if(this.beforeSeekableWindow_(e,t)){var i=e.end(e.length-1);return this.logger_("Fell out of live window at time "+t+". Seeking to live point (seekable end) "+i),this.cancelTimer_(),this.tech_.setCurrentTime(i),this.tech_.trigger({type:"usage",name:"vhs-live-resync"}),this.tech_.trigger({type:"usage",name:"hls-live-resync"}),!0}e=this.tech_.vhs.masterPlaylistController_.sourceUpdater_,i=this.tech_.buffered();if(this.videoUnderflow_({audioBuffered:e.audioBuffered(),videoBuffered:e.videoBuffered(),currentTime:t}))return this.cancelTimer_(),this.tech_.setCurrentTime(t),this.tech_.trigger({type:"usage",name:"vhs-video-underflow"}),this.tech_.trigger({type:"usage",name:"hls-video-underflow"}),!0;e=Bo(i,t);if(0