Awesome

Rate-based Backpropagation

This repository hosts the code implementation of the rate-based backpropagation detailed in paper, "Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation", accepted at NeurIPS 2024. [arXiv][OpenReview]

Dependencies

# Name                 Version
python                  3.9.19 
torch                   2.3.1
torchvision             0.18.1
tensorboard             2.17.0
spikingjelly            0.0.0.0.14

Directory Tree

.
├── experiment
│   ├── cifar
│   │   ├── config
│   │   └── main.py
│   └── dvs
│       ├── config
│       └── main.py
├── model
│   ├── layer.py
│   ├── resnet.py
│   └── vgg.py
└── util
    ├── data.py
    ├── image_augment.py
    ├── misc.py
    └── util.py

The experiment code for datasets are located on corresponding directories in experiment (CIFAR-10/CIFAR-100 in experiment/cifar, CIFAR10-DVS in experiment/dvs). Code related to neurons is defined in model/layer.py; code related to batch normalization is defined in util/util.py. The computational graph using rate-based gradients is implemented via model hooks, which are encapsulated in util/util.py.

Usage

1. Reproducing BPTT Results. To reproduce the results using backpropagation-through-time (BPTT) within the current project framework, use the following commands:

   ##### BPTT with multi-step #####
   # for CIFAR-10/100
   python experiment/cifar/main.py --dataset CIFAR10 --data_path [data_path] --arch resnet18 --T 4 --step_mode m
   # for CIFAR10-DVS
   python experiment/dvs/main.py --dataset CIFAR10_DVS_Aug --data_path [data_path] --arch vggsnn_dvs --step_mode m
   
   ##### BPTT with single-step #####
   # for CIFAR-10/100
   python experiment/cifar/main.py --dataset CIFAR10 --data_path [data_path] --arch resnet18 --T 4 --step_mode s
   # for CIFAR10-DVS
   python experiment/dvs/main.py --dataset CIFAR10_DVS_Aug --data_path [data_path] --arch vggsnn_dvs --step_mode s
   

2. Using Rate-Based Backpropagation. To enable rate-based backpropagation as a replacement for BPTT, use the following commands:

   ##### Rate-BP with multi-step #####
   # for CIFAR-10/100
   python experiment/cifar/main.py --dataset CIFAR10 --data_path [data_path] --arch resnet18 --T 4 --step_mode m --rate_flag
   # for CIFAR10-DVS
   python experiment/dvs/main.py --dataset CIFAR10_DVS_Aug --data_path [data_path] --arch vggsnn_dvs --step_mode m --rate_flag
   
   ##### Rate-BP with single-step #####
   # for CIFAR-10/100
   python experiment/cifar/main.py --dataset CIFAR10 --data_path [data_path] --arch resnet18 --T 4 --step_mode s --rate_flag
   # for CIFAR10-DVS
   python experiment/dvs/main.py --dataset CIFAR10_DVS_Aug --data_path [data_path] --arch vggsnn_dvs --step_mode s --rate_flag
   

3. Options for Hyper-Parameters:

   • --arch: corresponding SNN models, supporting: resnet18, resnet19, vggsnn_cifar, vggsnn_dvs
   • --T: Specifies the number of timesteps for the SNN model. Fixed as 10 on CIFAR10-DVS.
   • --step_mode: Specifies the training mode.
     • m: Multi-step training mode, where T loops are embedded within layers.
     • s: Single-step training mode, where T loops are executed outside the layers.
   • --rate_flag: Indicates whether to use the rate-based backpropagation (instead of BPTT).
     • Include this flag to enable Rate-BP.
     • Omit this flag to use standard backpropagation (BPTT).

Citation

If you find this work useful for your research, please consider citing it as follows:

@inproceedings{yu2024advancing,
  title={Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation},
  author={Yu, Chengting and Liu, Lei and Wang, Gaoang and Li, Erping and Wang, Aili},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}
}

Acknowledgement

Some of our code implementations for the SNN models and data preprocessing are based on references and adaptations from repositories: SpikingJelly, OTTT, SLTT, ASGL.