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surrogate_module_learning

Code for surrogate module learning (SML) Create a new backward path for more accurate SNN gradients.

Prerequisites

The Following Setup is tested and it is working:

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

Description

• We use Dspike surrogate gradient to realize the backward of step function.
• LIF model is build in LIFSpike in models/layer.py.
• If you want to adjust the hyperparameters, number, position, and structure of the surrogate module, the relevant code is on lines 512-537 of Train_distribute_pallel.py.
• If you have two GPUs, you can use the following code to run this demo (ResNet-18 structure with T=4 on CIFAR100), the default two surrogate modules are located after 3-rd and 6-th basicblocks.

python -m torch.distributed.launch --nproc_per_node=2 --use_env Train_distribute_pallel.py \
   --batch-size 128 --cos_lr_T 300 --epochs 300 \
   --model ResNet_SB18 \
   --num_classes 100 --dataset cifar100 --T 2 \
   --sync-bn --optimizer adamw --lr 0.01 --weight-decay 0.02

Pre-trained models

• The ResNet-34 models we used on ImageNet are avilable here

Citation

Reference paper.