Zephyr runtime for Kenning

This project provides a Zephyr library for the Kenning runtime API, along with an application for model evaluation. Its aim is to simplify adoption and switching between existing runtime implementations.

Overview

This repository provides:

kenning_inference_lib - a Zephyr library providing generic wrapper methods for loading models and running inference, regardless of their underlying implementation.
kenning-zephyr-runtime app - a Zephyr application used by Kenning for evaluating models and runtimes on devices.
demo application (demo_app) - a Zephyr application that uses kenning_inference_lib to run gesture recognition on sample data.

Quickstart

This is the minimal set of steps to build the runtime and run demo application or Kenning inference server.

The easiest way to obtain environment with all dependences is to use prepared Docker image

mkdir zephyr-workspace && cd zephyr-workspace
docker run --rm -it -v $(pwd):$(pwd) -w $(pwd) ghcr.io/antmicro/kenning-zephyr-runtime:latest /bin/bash

Now clone this repository and install the latest Zephyr SDK

git clone https://github.com/antmicro/kenning-zephyr-runtime
cd kenning-zephyr-runtime/
./scripts/prepare_zephyr_env.sh
./scripts/prepare_modules.sh
source .venv/bin/activate

Building and running demo app

At this point you should be able to build the demo app and run it

west build -p always -b stm32f746g_disco demo_app -- -DEXTRA_CONF_FILE=tvm.conf
west build -t board-repl
python ./scripts/run_renode.py

The output should look similar as in demo app section.

Building inference server app and benchmarking with Kenning

To build Kenning inference server app run

west build -p always -b stm32f746g_disco app -- -DEXTRA_CONF_FILE=tvm.conf

And then execute Kenning to compile the model, run benchmark and generate report

kenning optimize test report
    --json-cfg ./kenning-scenarios/renode-zephyr-tvm-magic-wand-inference.json
    --measurements ./results-tvm.json
    --report-path ./report-tvm.md
    --report-types performance classification renode_stats
    --to-html
    --verbosity INFO

The report will be saved as report-tvm/report-tvm.html.

Building the project

This section contains instructions for preparing Zephyr and building the runtime.

Using the Docker environment

The Docker environment with all the necessary components is available in Dockerfile. The built image can be pulled with:

docker pull ghcr.io/antmicro/kenning-zephyr-runtime:latest

or you can build the image with

docker build -t kenning-zephyr-runtime:local .

Installing the dependencies in the system

To be able to build and use the project, you need the folowing dependencies:

Zephyr dependencies
jq
curl
west
patch
CMake

On Debian-based Linux distributions, install the dependencies as follows:

sudo apt update

sudo apt install -y --no-install-recommends ccache curl device-tree-compiler dfu-util file \
  g++-multilib gcc gcc-multilib git jq libmagic1 libsdl2-dev make ninja-build \
  python3-dev python3-pip python3-setuptools python3-tk python3-wheel python3-venv \
  mono-complete wget xxd xz-utils patch

Cloning the project and preparing the environment

First off, create a workspace directory and clone the repository:

mkdir zephyr-workspace && cd zephyr-workspace
git clone https://github.com/antmicro/kenning-zephyr-runtime.git
cd kenning-zephyr-runtime

After entering the project's directory, initialize a Zephyr workspace with:

./scripts/prepare_zephyr_env.sh
source .venv/bin/activate

This will:

Download (if necessary) and set up the Zephyr SDK
Download necessary toolchains
Set up a Python virtual environment with necessary dependencies.

This can be reused to load the necessary environment before launching commands mentioned later in this README.

Now, prepare additional modules:

./scripts/prepare_modules.sh

Building the Kenning runtime tester application

To build the Kenning Zephyr runtime, select a supported machine learning runtime and a board.

west build --board <board> app -- -DEXTRA_CONF_FILE=<runtime>.conf

You can provide one of the following runtimes in <runtime>:

tvm
tflite

The project was tested on the following boards:

Check the Adding support for more boards section for information on whow to add a new target device.

The binary built after executing west build can be found in build/zephyr/zephyr.elf.

Installing Kenning with Renode

Use pip to install Kenning with Renode support enabled:

pip install --upgrade pip
pip install "kenning[tvm,tensorflow,reports,renode] @ git+https://github.com/antmicro/kenning.git"

The pyrenode3 module requires installing Renode to work. The easiest way is to use the latest Renode package and store its location in PYRENODE_PKG:

wget https://builds.renode.io/renode-latest.pkg.tar.xz
export PYRENODE_PKG=`pwd`/renode-latest.pkg.tar.xz

For other configuration options check pyrenode3 README.md.

Evaluating the model in Kenning

Kenning provides:

Model optimization and compilation
Evaluation of a model on target device:
- Sending the model to the device using UART communication (e.g. execution graph or TFLite Flatbuffer)
- Sending input data for running inference on the model
- Collecting output data from the model, and evaluating the quality and performance of the model on target device with selected runtime
Report rendering, including comparison reports that allow to compare various runtimes, boards, models and applied optimizations.

With Kenning, we can also evaluate the runtime by simulating the device in Renode. This allows us to:

Verify model behavior without the need for physical hardware
Check model and runtime performance and correctness in Continuous Integration pipelines without the actual device in the loop
Check model and runtime performance on platforms under development
Obtain more detailed metrics regarding device usage, e.g. histogram of instructions

The switch between Renode and actual hardware is seamless - both communicate with Kenning using UART.

Building the project and evaluating models in Renode

This section will demonstrate how to build the project and evaluate a model for recognizing gestures on stm32f746g_disco.

Using TFLite Micro runtime

First off, build the kenning-zephyr-runtime app for stm32f746g_disco and the TFLite Micro configuration:

west build -p always -b stm32f746g_disco app -- -DEXTRA_CONF_FILE=tflite.conf

Then, evaluate the model in Renode using a sample scenario located in kenning-scenarios/renode-zephyr-tflite-magic-wand-inference.json and generate a report with performance and quality metrics:

kenning optimize test report \
    --json-cfg kenning-scenarios/renode-zephyr-tflite-magic-wand-inference.json \
    --measurements results.json --verbosity INFO \
    --report-path reports/stm32-renode-tflite-magic-wand/report.md \
    --to-html \
    --verbosity INFO

The model performance report in Markdown will be available under reports/stm32-renode-tflite-magic-wand/report.md. The HTML version of the report will be accessible from reports/stm32-renode-tflite-magic-wand/report/report.html.

Using microTVM

To build the kenning-zephyr-runtime app to work with microTVM runtime, set -DEXTRA_CONF_FILE to tvm.conf, e.g. by executing:

west build -p always -b stm32f746g_disco app -- -DEXTRA_CONF_FILE=tvm.conf

Evaluate the model using the sample scenario located in kenning-scenarios/renode-zephyr-tvm-magic-wand-inference.json:

kenning optimize test report \
    --json-cfg kenning-scenarios/renode-zephyr-tvm-magic-wand-inference.json \
    --measurements results.json --verbosity INFO \
    --report-path reports/stm32-renode-tvm-magic-wand/report.md \
    --to-html \
    --verbosity INFO

Building runtime with microTVM backend using custom model

This step requires Kenning to be installed. Follow the steps in Installing Kenning with Renode to install it.

The microTVM backend requires having TVM ops used by model to be compiled with the runtime. By default, it is compiled with Magic Wand model ops, but it is possible to use ops from any model. To do so, provide additional config variable CONFIG_KENNING_MODEL_PATH which should contain path to the model. This path can be either path to the file or URL to any model hosted online, for example at https://dl.antmicro.com/kenning/ (i.e. https://dl.antmicro.com/kenning/models/classification/magic_wand.h5). The supported model formats are:

ONNX (.onnx),
Keras (.h5),
PyTorch (.pt, .pth),
TFLite (.tflite).

You can set this variable in prj.conf or add it to west build as follows (remember to wrap path in \"):

west build -p always -b stm32f746g_disco app -- \
    -DEXTRA_CONF_FILE=tvm.conf \
    -DCONFIG_KENNING_MODEL_PATH=\"https://dl.antmicro.com/kenning/models/classification/magic_wand.h5\"

Building the project with LLEXT runtime and evaluating models in Renode

Kenning Zephyr Runtime uses LLEXT to support hot-swapping ML runtimes. The runtime can be built separately from the project and loaded into an already running KZR.

Using LLEXT microTVM

Build kenning-zephyr-runtime with LLEXT support using:

west build -p always -b stm32f746g_disco app -- -DEXTRA_CONF_FILE=llext.conf

then build the TVM extension:

west build app -t llext-tvm -- -DEXTRA_CONF_FILE="llext.conf;llext_tvm.conf"

Evaluate the model using scenario located in kenning-scenarios/renode-zephyr-tvm-llext-magic-wand-inference.json:

kenning optimize test report \
    --json-cfg kenning-scenarios/renode-zephyr-tvm-llext-magic-wand-inference.json \
    --measurements results.json --verbosity INFO \
    --report-path reports/stm32-renode-tvm-llext-magic-wand/report.md \
    --to-html \
    --verbosity INFO

Alternatively, build and evaluation can be done in a single step:

kenning optimize test report \
    --json-cfg kenning-scenarios/renode-zephyr-auto-tvm-llext-magic-wand-inference.json \
    --measurements results.json --verbosity INFO \
    --report-path reports/stm32-renode-auto-tvm-llext-magic-wand/report.md \
    --to-html \
    --verbosity INFO

Evaluating a model in Kenning using actual hardware

Kenning can evaluate the runtime running on a physical device. To do so, we need to flash the device and replace RenodeRuntime in evaluation scenarios for Kenning with proper runtimes.

Running evaluation on NRF52840 dongle

Build the runtime for nrf52840dongle (let's use TFLite Micro in this example):

west build -p always -b nrf52840dongle app -- -DEXTRA_CONF_FILE=tflite.conf

Flash Kenning runtime on the device by following instructions in the Zephyr documenation.

Finally, evaluate the model and generate a report with performance and quality metrics:

kenning optimize test report \
    --json-cfg kenning-scenarios/zephyr-tflite-magic-wand-inference.json \
    --measurements results.json --verbosity INFO \
    --report-types performance classification \
    --report-path reports/nrf-tflite-magic-wand/report.md \
    --to-html \
    --verbosity INFO

Running evaluation on STM32F746

Build the runtime for stm32f746g_disco (let's use TFLite Micro in this example):

west build -p always -b stm32f746g_disco app -- -DEXTRA_CONF_FILE=tvm.conf

Flash the connected device with the kenning-zephyr-runtime app:

west flash

Evaluate the model and generate a report with performance and quality metrics:

kenning optimize test report \
    --json-cfg kenning-scenarios/zephyr-tvm-magic-wand-inference.json \
    --measurements results.json --verbosity INFO \
    --report-types performance classification \
    --report-path reports/stm32-tvm-magic-wand/report.md \
    --to-html \
    --verbosity INFO

Demo application using Kenning inference library

The Kenning inference library present in this repository can be also used in actual applications, not only in the evaluation process in Kenning.

The application present in demo_app demonstrates how to use Kenning Zephyr Runtime in actual, simple use case, where we take a model recognizing gestures (wing, ring, slope and negative, trained with Magic Wand dataset) and compile it with picked runtime. It goes through delivered inputs, runs inference and prints the output.

With the build environment configured as described in the Cloning the project and preparing the environment, you can build the demo_app as follows:

using the microTVM runtime:

west build -p always -b hifive_unleashed demo_app -- -DEXTRA_CONF_FILE=tvm.conf

using the TFLite Micro runtime:

west build -p always -b hifive_unleashed demo_app -- -DEXTRA_CONF_FILE=tflite.conf

After building the application with a board specified, we can either flash the hardware with it, or simulate it in Renode. To simulate it in Renode, generate the board's repl platform file using:

west build -t board-repl

The result can be found under ./build/<board_name>.repl.

Finally, run the demo with:

python ./scripts/run_renode.py

The output should look like this:

Starting Renode simulation. Press CTRL+C to exit.
*** Booting Zephyr OS build zephyr-v3.5.0-5385-g415cb65e3f48 ***
__nop function is not yet supported.I: model output: [wing: 1.000000, ring: 0.000000, slope: 0.000000, negative: 0.000000]
I: model output: [wing: 0.000000, ring: 0.000000, slope: 0.000000, negative: 1.000000]
I: model output: [wing: 0.000000, ring: 0.000000, slope: 1.000000, negative: 0.000000]
I: model output: [wing: 1.000000, ring: 0.000000, slope: 0.000000, negative: 0.000000]
I: model output: [wing: 0.000000, ring: 0.997457, slope: 0.000000, negative: 0.002543]
I: model output: [wing: 0.000000, ring: 0.000000, slope: 1.000000, negative: 0.000000]
I: model output: [wing: 1.000000, ring: 0.000000, slope: 0.000000, negative: 0.000000]
I: model output: [wing: 1.000000, ring: 0.000000, slope: 0.000000, negative: 0.000000]
I: model output: [wing: 1.000000, ring: 0.000000, slope: 0.000000, negative: 0.000000]
I: model output: [wing: 0.000000, ring: 0.000000, slope: 1.000000, negative: 0.000000]
I: model output: [wing: 0.000000, ring: 0.000000, slope: 0.000000, negative: 1.000000]
I: inference done

Building demo using different model

It is also possible to build demo_app using some custom model. To do it, you need to edit model_struct in demo_app/src/main.c to match the model IO specification. Then, provide model input in demo_app/src/input_data.h and model path using CONFIG_KENNING_MODEL_PATH config variable (similarly as in Building runtime with microTVM backend using custom model):

west build -p always -b stm32f746g_disco demo_app -- \
    -DEXTRA_CONF_FILE=tvm.conf \
    -DCONFIG_KENNING_MODEL_PATH=\"https://dl.antmicro.com/kenning/models/classification/magic_wand.h5\"

Adding support for more boards

Adapting kenning-zephyr-runtime for new boards is straightforward. As long as the underlying runtime implementation supports a given board without additional configuration, the process of adapting the application for new board boils down to picking an UART for communication with the Kenning application running on host. Such UART is expected to be aliased kcomms in the application.

The alias can be set in the overlay file under app/boards/<board_name>.overlay, where <board_name> is the name of the board in Zephyr, passed in --board flag in west build:

/ {
    aliases {
        kcomms = &uart0;
    };
};

It is crucial that the selected UART isn't used anywhere else (e.g. as zephyr,console).

Some boards may also require additional configuration. Those should be placed at app/boards/<board_name>.conf.

Name		Name	Last commit message	Last commit date
Latest commit History 218 Commits
.github/workflows		.github/workflows
app		app
demo_app		demo_app
environments		environments
img		img
include/kenning_inference_lib/core		include/kenning_inference_lib/core
kenning-scenarios		kenning-scenarios
lib		lib
modules		modules
renode/scripts		renode/scripts
scripts		scripts
tests		tests
zephyr		zephyr
.ci.yml		.ci.yml
.clang-format		.clang-format
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
CMakeLists.txt		CMakeLists.txt
Kconfig		Kconfig
README.md		README.md
requirements.txt		requirements.txt
west.yml		west.yml

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Zephyr runtime for Kenning

Overview

Quickstart

Building and running demo app

Building inference server app and benchmarking with Kenning

Building the project

Using the Docker environment

Installing the dependencies in the system

Cloning the project and preparing the environment

Building the Kenning runtime tester application

Installing Kenning with Renode

Evaluating the model in Kenning

Building the project and evaluating models in Renode

Using TFLite Micro runtime

Using microTVM

Building runtime with microTVM backend using custom model

Building the project with LLEXT runtime and evaluating models in Renode

Using LLEXT microTVM

Evaluating a model in Kenning using actual hardware

Running evaluation on NRF52840 dongle

Running evaluation on STM32F746

Demo application using Kenning inference library

Building demo using different model

Adding support for more boards

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Zephyr runtime for Kenning

Overview

Quickstart

Building and running demo app

Building inference server app and benchmarking with Kenning

Building the project

Using the Docker environment

Installing the dependencies in the system

Cloning the project and preparing the environment

Building the Kenning runtime tester application

Installing Kenning with Renode

Evaluating the model in Kenning

Building the project and evaluating models in Renode

Using TFLite Micro runtime

Using microTVM

Building runtime with microTVM backend using custom model

Building the project with LLEXT runtime and evaluating models in Renode

Using LLEXT microTVM

Evaluating a model in Kenning using actual hardware

Running evaluation on NRF52840 dongle

Running evaluation on STM32F746

Demo application using Kenning inference library

Building demo using different model

Adding support for more boards

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