temporal_efficient_training

Code for temporal efficient training

Prerequisites

The Following Setup is tested and it is working:

• Python>=3.5
• Pytorch>=1.9.0
• Cuda>=10.2

Preprocess of DVS-CIFAR

• Download CIFAR10-DVS dataset
• transform .aedat to .mat by test_dvs.m with matlab.
• prepare the train and test data set by dvscifar_dataloader.py 1
• you can obtain processed data in this link.

Description

• use a triangle-like surrogate gradient ZIF in models/layer.py for step function forward and backward.
• It's very easy to build snn convolution layer by Layer in models/layer.py. \ self.conv = nn.Sequential(Layer(2,64,3,1,1),Layer(64,128,3,1,1),)
• The 0-th and 1-th dimension of snn layer's input and output are batch-dimension and time-dimension.

Citation

Reference paper.

@inproceedings{
deng2022temporal,
title={Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting},
author={Shikuang Deng and Yuhang Li and Shanghang Zhang and Shi Gu},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=_XNtisL32jv}
}