Awesome
snn_optimal_conversion_pipeline
Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks
Training and simulation
We suggest using file 'main_train.py' for training and file 'main_simulation.py' for simulation.
- The training and simulation parameters are collected in 'models/settings.py'.
Files
- 'main_train.py' : main training file.
- 'main_simulation.py' : main simulation file.
- 'models/settings.py' : collection of the parameters.
- 'models/spiking_layer.py' : SPIKE_layer to replace ANN's convolution layer and linear layer.
- 'models/new_relu.py' : threshold ReLU file
Pre-trained models
- All the pre-trained models we used are avilable here
Issues
- Consider the generalization, when T is large, the loss and accuracy of SNN may both decrease.
Citation
If our code is helpful to you, please cite the following paper.
@inproceedings{
deng2021optimal,
title={Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks},
author={Shikuang Deng and Shi Gu},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=FZ1oTwcXchK}
}