CQ+ Training: Minimizing Accuracy Loss in Conversion from Convolutional Neural Networks to Spiking Neural Networks
The repository includes C++ and CUDA code that has to be compiled and installed before it can be used from Python, download the repository and run the following command to do so:
python setup.py install
Z. Yan, J. Zhou, and W.F. Wong, " CQ+ Training: Minimizing Accuracy Loss in Conversion from Convolutional Neural Networks to Spiking Neural Networks." Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
Z. Yan, J. Zhou, and W.F. Wong, "Near Lossless Transfer Learning for Spiking Neural Networks." Accepted by Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21). Virtual conference. Feb 2021.
https://drive.google.com/file/d/1b1GB2wfab0Ezi7FvqnK4QdZUyj4qyA5N/view?usp=sharing
This simulator is inspired by and makes use of the code from slayerPytorch
Copyright 2020 Jun Zhou, ShenjingCat is free software: you can redistribute it and/or modoify it under the terms of GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
ShenjingCat is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details http://www.gnu.org/licenses/.