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NeuroPack

Paper available here: NeuroPack: An Algorithm-level Python-based Simulator for Memristor-empowered Neuro-inspired Computing

experimentalResults.npz gives experimental results shown in the paper.

NeuroPack is an algorithm-level simulator for memrsitor-based neuromorphic designs. It incorporates an empirical memristor model here to simulate how memristor states change during an online-training process. To get memristor fitting parameters, please use ArC One instrument module 'ParameterFit'.

Components

  • NeuroPack.py implements the main panel and the built-in analysis tool for NeuroPack
  • NeuroCores/core_X.py implements the neuron model and the learning rule

Input files

  • Configuration file: NeuroData/XX.json is an input file that users need to provide for setting configuration
  • Matrix Connectivity file: NeuroData/XX.txt is an input file that defines how neurons are connected and which memristor is mapped to the connectivity.
  • Stimuli file for training: NeuroData/XX.txt is an input file that provides input spikes and output labels (for supervised learning only) for training.
  • Stimuli file for testing (optional): NeuroData/XX.txt is an input file that provide input spikes and output labels (for supervised learning only) for test.

Install and run

  • Please install the updated Arc One interface with all required packages.
  • Download NeuroPack and put it in the following directory: C:/Users/AppData/Roaming/arc1pyqt\ProgPanels
  • If you use Windows, open command prompt and go to the path where you install arc1_pyqt.
  • Use the following commands to run Arc One interface:

SET NNDBG=1 if you want to print out debug information in command prompt.

python setup.py build

python run.py

  • Now ArC One interface pops up. Select 'VirtualArC' from the portal dropdown list, and click 'connect'.
  • Click 'Read All'. Now the state indicator changes to 'Ready'.
  • Select 'NeuroPack' from module dropdown list, and click 'Add'.
  • Now NeuroPack main panel should pop up. Add all necessary input files and click 'Training network' to run.

Example usage

If you want to reproduce the results showcased in the paper 'NeuroPack: An Algorithm-level Python-based Simulator for Memristor-empowered Neuro-inspired Computing', please do the following:

  • Click 'NeuroBase.json' and select 'MNIST_LIF.json'.
  • Click 'Load Conn. Matrix' and select 'MNIST_LIF_connmat.txt'.
  • Click 'Load Stim. File' and select 'MNIST_LIF_stim.txt'.
  • Tick 'Load test file', click and select 'MNIST_LIF_test_stim.txt'.
  • Tick 'Save to', click 'Load test file', and create a file to store inference results.
  • Select 'LIF_supervisedLearning_wta' for 'Network core'.
  • Press 'Train Network'

Citation

@ARTICLE{NeuroPack,
AUTHOR={Huang, Jinqi and Stathopoulos, Spyros and Serb, Alexantrou and Prodromakis, Themis},   
TITLE={NeuroPack: An Algorithm-Level Python-Based Simulator for Memristor-Empowered Neuro-Inspired Computing},      	
JOURNAL={Frontiers in Nanotechnology},      
VOLUME={4},      	
YEAR={2022},       
URL={https://www.frontiersin.org/article/10.3389/fnano.2022.851856},       	
DOI={10.3389/fnano.2022.851856},      	
ISSN={2673-3013}
}