Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Strong AI.
STBP is a way to train SNN with datasets by Backward propagation.Using this Repositories allows you to train SNNS with STBP and quantize SNNS with QAT to deploy to neuromorphological chips like Loihi and Tianjic.
git clone https://github.com/ZLkanyo009/STBP-train-and-compression.git
Convert layer to spatiotemporal layer:
conv = nn.Conv2d(...)
conv_s = tdLayer(conv)
Define LIF activation function just like Relu:
spike = LIFSpike()
In the forward function, replace the activation function of each layer with LIF activation function, and replace the calls such as conv() with conv_ s(), then SNN_layers definition is completed.Finally, we use Frequency Coding to decode SNN's output like out = torch.sum(x, dim=2) / steps
def forward(self, x):
x = self.conv1_s(x)
x = self.spike(x)
x = self.pool1_s(x)
x = self.spike(x)
x = x.view(x.shape[0], -1, x.shape[4])
x = self.fc1_s(x)
x = self.spike(x)
out = torch.sum(x, dim=2) / steps
return out
If BN layer is required:
bn = nn.BatchNorm2d(...)
bn = tdBatchNorm(...)
conv_s = tdLayer(conv, bn)
# Start training fp32 model with:
# model_name can be ResNet18, CifarNet, ...
python main.py ResNet18 --dataset CIFAR10
# training with DDP:
python -m torch.distributed.launch main.py ResNet18 --local_rank 0 --dataset CIFAR10 --p DDP
# You can manually config the training with:
python main.py ResNet18 --resume --lr 0.01
# Start training quantize model with:
# model_name can be ResNet18, CifarNet, ...
python main.py ResNet18 --dataset CIFAR10 -q
# training with DDP:
python -m torch.distributed.launch main.py ResNet18 --local_rank 0 --dataset CIFAR10 -q --p DDP
# You can manually config the training with:
python main.py ResNet18 -q --resume --bit 4 --lr 0.01
All SNN run in timesteps = 2.
| Model | Acc.(fp32) | Acc.(8 bit quantize) | Acc.(4 bit quantize) |
|---|---|---|---|
| MNISTNet | 97.96% | 97.57% | 97.56% |
| ResNet18 | 84.40% | 84.23% | 83.61% |
- Zheng, H., Wu, Y., Deng, L., Hu, Y., & Li, G. (2020). Going Deeper With Directly-Trained Larger Spiking Neural Networks. arXiv preprint arXiv:2011.05280.
- Wu, Y., Deng, L., Li, G., Zhu, J., Xie, Y., & Shi, L. (2019, July). Direct training for spiking neural networks: Faster, larger, better. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 1311-1318).
- Wu, Y., Deng, L., Li, G., Zhu, J., & Shi, L. (2018). Spatio-temporal backpropagation for training high-performance spiking neural networks. Frontiers in neuroscience, 12, 331.