Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse

This repository contains the code producing the figures of the paper "Low Power In-Memory Implementation of Ternary Neural Networks with Resistive RAM-Based Synapse".

Setting the environment

To set the environment run in your conda main environment:

conda config --add channels conda-forge  
conda create --name environment_name --file requirements.txt  
conda activate environment_name  
conda install pytorch==1.1.0 torchvision==0.3.0 -c pytorch  

The code for BNN modules was adapted from this repo.
The code for VGG architecture was adapted from this repo (Copyright (c) 2017 liukuang).

Training Quantized VGGs

python main.py --filt 128 --weight-quant T --act-quant F --ber 0.0 --optim adamw --decay 2.0 --lr 0.01 --mbs 128 --epochs 700 --device 0 --save True