This is the official code of our paper, AutoSNN: Towards Energy-Efficient Spiking Neural Networks, accepted in ICML 2022.
Python >= 3.6.10, PyTorch == 1.4.0, torchvision == 0.5.0
For training and evaluating SNNs, we used one of old versions of spikingjelly, which can be installed as follows:
git clone https://github.com/fangwei123456/spikingjelly.git
cd spikingjelly
git reset --hard 73f94ab983d0167623015537f7d4460b064cfca1
python setup.py install
CIFAR-10, CIFAR-100, and SVHN can be automatically downloaded by torchvision, but Tiny-ImageNet needs to be manually downloaded.
Neuromorphic datasets (CIFAR10-DVS, DVS128Gesture) can be downloaded by using this link
Our method has two-step search processes: training a super-network and searching for SNNs, which can be executed with 1_script_train_supernet.sh and 2_script_search.sh, respectively.
After the search process, SNN architectures searched by our method will be automatically saved in search_arch/arch.py.
We provide a script 3_script_retrain.sh to train the searched SNN architectures.