Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021

Paper Version 1: [PMLR], [arXiv].

Paper Version 2: [IJCV].

When converting ANNs to SNNs, conventional methods ensure the minimization in parameter space, while we focus on the minimization in network output space:

Feature Comparison of SNN calibration:

Pytorch 1.8

For ImageNet experiments, please be sure that you can initialize distributed environments

For CIFAR experiments, one GPU would suffice.

We released a new version of the paper, and will update the code to match the experiments with that paper.

For imagenet experiments, please first download the checkpoints from Google Drive.

We recommend initializing distributed learning environments, and utlizing multi-GPU calibration.

For reproducibility, 8 GPUs are per run and distributed environments are highly encouraged.

For example:

python main_cal_imagenet.py --arch res34 --T 32 --usebn --calib light --dpath PATH/TO/DATA

Train an ANN model with main_train_cifar.py

python main_train_cifar.py --dataset CIFAR10 --arch VGG16 --usebn 

Pre-trained results:

Calibrate an SNN with main_cal_cifar.py.

python main_cal_cifar.py --dataset CIFAR10 --arch VGG16 --T 16 --usebn --calib advanced --dpath PATH/TO/DATA

--T is the time step, --calib is the calibration method, please use none, light, advanced for experiments.

The calibration will run 5 times, and return the mean accuracy as well as the standard deviation.

Example results:

If you feel this repo helps you, please consider citing the following articles:

@article{li2022converting,
  title={Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration},
  author={Li, Yuhang and Deng, Shikuang and Dong, Xin and Gu, Shi},
  journal={arXiv preprint arXiv:2205.10121},
  year={2022}
}

@article{li2024error,
  title={Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration},
  author={Li, Yuhang and Deng, Shikuang and Dong, Xin and Gu, Shi},
  journal={International Journal of Computer Vision},
  pages={1--24},
  year={2024},
  publisher={Springer}
}