$\text{AC}^2\text{AS}$: Activation Consistency Coupled ANN-SNN Framework for Fast and Memory-Efficient SNN Training

The demo code of the paper: $\text{AC}^2\text{AS}$: Activation Consistency Coupled ANN-SNN Framework for Fast and Memory-Efficient SNN Training

Training with ReSU:

VGG-13

python run.py --arch=VGG --time-step=4 --batch-size=512 --spike-unit=ReSU --dataset=CIFAR100 --class-nums=100 --data-path=dataset_path --pretrained=pretrained_model_path

ResNet-17

python run.py --arch=ResNet --time-step=5 --batch-size=512 --spike-unit=ReSU --dataset=CIFAR100 --class-nums=100 --kaiming-norm=True --data-path=dataset_path --pretrained=pretrained_model_path

Training with STSU:

VGG-13

python run.py --arch=VGG --time-step=4 --batch-size=512 --spike-unit=STSU --dataset=CIFAR100 --class-nums=100 --data-path=dataset_path --pretrained=pretrained_model_path

ResNet-17

python run.py --arch=ResNet --time-step=5 --batch-size=512 --spike-unit=STSU --dataset=CIFAR100 --class-nums=100 --kaiming-norm=True --data-path=dataset_path --pretrained=pretrained_model_path

Pre-trained Model

The pre-trained models are available at https://drive.google.com/drive/folders/1y6ZUT3WToowuCo72CVspurQrdSG4U8zi?usp=sharing

Citation

@article{ac2asnn,
  title={AC2AS: Activation Consistency Coupled ANN-SNN Framework for Fast and Memory-Efficient SNN Training},
  author={Jianxiong Tang and Jianhuang Lai and Xiaohua Xie and Lingxiao Yang and Wei-Shi Zheng},
  journal={Pattern Recognition},
  year={2023}
}