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https://github.com/AmazingDD/EC-SNN
Official code for "EC-SNN: Splitting Deep Spiking Neural Networks on Edge Devices" (IJCAI2024)
https://github.com/AmazingDD/EC-SNN
distributed-computing edge edge-computing pruning-algorithms spiking-neural-networks split-learning
Last synced: 2 months ago
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Official code for "EC-SNN: Splitting Deep Spiking Neural Networks on Edge Devices" (IJCAI2024)
- Host: GitHub
- URL: https://github.com/AmazingDD/EC-SNN
- Owner: AmazingDD
- License: apache-2.0
- Created: 2023-12-13T08:03:43.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-04T07:56:23.000Z (5 months ago)
- Last Synced: 2024-08-04T08:52:39.807Z (5 months ago)
- Topics: distributed-computing, edge, edge-computing, pruning-algorithms, spiking-neural-networks, split-learning
- Language: Python
- Homepage:
- Size: 1.23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Spiking-Neural-Networks - [code
README
# EC-SNN
This is the repository of our article published in IJCAI 2024 "EC-SNN: Splitting Deep Spiking Neural Networks on Edge Devices".
## Overview
## Requirements
```
torch==2.0.1
torchvision==0.15.2
librosa==0.10.1
spikingjelly==0.0.0.0.14
numpy==1.23.5
pandas==1.5.3
scikit-learn==1.2.1
opencv-python==4.8.1.78
```
## How to run
Examples of running commands for different purposes are listed below, please modify the corresponding parts to implement your expected task. (All running commands in shell scripts will be attached to Github Pages later.)
This repository is a simulation toolkit for researchers to learn the logistics of EC-SNN. To get the results listed in our paper, please deploy the corresponding models to edge devices like Raspberry PI!
### model training
```
python ecsnn.py -train -arch=vgg9 -act=snn -device=cuda -data_dir=. -dataset=cifar10 -b=128
```
### model pruning for one edge device with all classes selected
make sure the class tokens are integers starting from 0.
```
python ecsnn.py -prune -arch=vgg9 -act=snn -data_dir=. -dataset=cifar10 -b=128 -split_dir=./splitted/ -device=cuda -apoz=95 -c 0 1 2 3 4 5 6 7 8 9
```
### energy consumption
```
python ecsnn.py -split -energy -arch=vgg9 -act=snn -device=cuda -split_dir=./splitted/ -data_dir=. -dataset=cifar10 -b=128
```
### latency
make sure `./infer_data/` contains the specific frame you want before making an inference
```
python ecsnn.py -split -infer -arch=vgg9 -act=snn -device=cpu -split_dir=./splitted/ -dataset=cifar10
```
_For more details about each argument, try reaching our Github pages later._
### cifarnet quick start
Implement the following commands step by step to get quick results. We provide this part for quick access to the whole workflow of EC-SNN.
```
# training
python ecsnn.py -arch=cifarnet -act=snn -device=cuda -train
python ecsnn.py -arch=cifarnet -act=ann -device=cuda -train
python ecsnn.py -arch=cifarnet -act=snn -prune -b=128 -split_dir=./splitted/ -device=cuda -apoz=95 -c 0 1 2 3 4 5 6 7 8 9
python ecsnn.py -arch=cifarnet -act=ann -prune -b=128 -split_dir=./splitted/ -device=cuda -apoz=56 -c 0 1 2 3 4 5 6 7 8 9
python ecsnn.py -arch=cifarnet -act=snn -fusion -split_dir=./splitted/ -device=cuda -b=128
python ecsnn.py -arch=cifarnet -act=ann -fusion -split_dir=./splitted/ -device=cuda -b=128
# latency
python ecsnn.py -arch=cifarnet -act=snn -device=cuda -infer
python ecsnn.py -arch=cifarnet -act=ann -device=cuda -infer
python ecsnn.py -arch=cifarnet -act=snn -device=cuda -infer -split -split_dir=./splitted/
python ecsnn.py -arch=cifarnet -act=ann -device=cuda -infer -split -split_dir=./splitted/
# energy consumption
python ecsnn.py -arch=cifarnet -act=snn -device=cuda -energy -b=128
python ecsnn.py -arch=cifarnet -act=ann -device=cuda -energy -b=128
python ecsnn.py -arch=cifarnet -act=snn -device=cuda -energy -split -split_dir=./splitted/ -b=128
python ecsnn.py -arch=cifarnet -act=ann -device=cuda -energy -split -split_dir=./splitted/ -b=128
```
## Datasets
You can download experiment data and put them into the data folder. All data are available in the links below:
- [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html)
- [Caltech 101](https://data.caltech.edu/records/mzrjq-6wc02)
- [CIFAR10-DVS](https://figshare.com/articles/dataset/CIFAR10-DVS_New/4724671)
- [N-Caltech101](https://www.garrickorchard.com/datasets/n-caltech101) (Recommend One Drive)
- [GTZAN](https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification)
- [UrbanSound8K](https://www.kaggle.com/datasets/chrisfilo/urbansound8k)
## Cite
Please cite the following paper if you find our work contributes to yours in any way:
```
@inproceedings{ijcai2024p596,
title = {EC-SNN: Splitting Deep Spiking Neural Networks for Edge Devices},
author = {Yu, Di and Du, Xin and Jiang, Linshan and Tong, Wentao and Deng, Shuiguang},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
pages = {5389--5397},
year = {2024}
}
```