MLP simlator (+NeuroSim) V1.0

The MLP+NeuroSim framework was developed by Prof. Shimeng Yu's group (Arizona State University). The model is made publicly available on a non-commercial basis. Copyright of the model is maintained by the developers, and the model is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International Public License

This is the released version 1.0 (July 1st, 2017) for the tool, and this version only supports analog eNVM devices (RRAM or PCM). If you use the tool or adapt the tool in your work or publication, you are required to cite the following reference:

P.-Y. Chen, X. Peng, S. Yu, ※NeuroSim+: An integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures,§ IEEE International Electron Devices Meeting (IEDM), 2017, San Francisco, USA.

For more devices such as SRAM, digital eNVM (RRAM, PCM or STT-MRAM) and FeFET, please go to released version 2.0 (May 1st, 2018) MLP simulator (+NeuroSim) V2.0

If you have logistic questions or comments on the model, please contact Prof. Shimeng Yu, and if you have technical questions or comments, please contact Pai-Yu Chen and Xiaochen Peng.

File lists

1. MATLAB fitting script: nonlinear_fit.m
2. Nonlinearity-to-A table: Documents/Nonlinearity-NormA.htm
3. MNIST data: MNIST_data.zip
4. Manual: Documents/Manual.pdf
5. MLP Simulator (+NeuroSim): the rest of the files

Installation steps (Linux)

1. Get the tool from GitHub

git clone https://github.com/neurosim/MLP_NeuroSim_V1.0.git

2. Extract MNIST_data.zip to it’s current directory

unzip MNIST_data.zip

3. Compile the codes

make

For the usage of this tool, please refer to the manual.

References related to this tool

1. P.-Y. Chen, X. Peng, S. Yu, ※NeuroSim+: An Integrated Device-to-Algorithm Framework for Benchmarking Synaptic Devices and Array Architectures,*§ IEEE International Electron Devices Meeting (IEDM), 2017, San Francisco, USA.
2. P.-Y. Chen, X. Peng, S. Yu, ※System-level benchmark of synaptic device characteristics for neuro-inspired computing,§ IEEE SOI-3D-Subthreshold Microelectronics Technology Unified Conference (S3S) 2017, San Francisco, USA.
3. P.-Y. Chen, S. Yu, ※Partition SRAM and RRAM based synaptic arrays for neuro-inspired computing,§ IEEE International Symposium on Circuits and Systems (ISCAS), 2016, Montreal, Canada.
4. P.-Y. Chen, B. Lin, I.-T. Wang, T.-H. Hou, J. Ye, S. Vrudhula, J.-S. Seo, Y. Cao, and S. Yu, ※Mitigating effects of non-ideal synaptic device characteristics for on-chip learning,§ IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2015, Austin, TX, USA.
5. P.-Y. Chen, D. Kadetotad, Z. Xu, A. Mohanty, B. Lin, J. Ye, S. Vrudhula, J.-S. Seo, Y. Cao, S. Yu, ※Technology-design co-optimization of resistive cross-point array for accelerating learning algorithms on chip,§ IEEE Design, Automation & Test in Europe (DATE), 2015, Grenoble, France.