no code implementations • 9 Nov 2023 • Yudong Li, Yunlin Lei, Xu Yang
Spiking Neural Network (SNN) is known as the most famous brain-inspired model, but the non-differentiable spiking mechanism makes it hard to train large-scale SNNs.
1 code implementation • 19 Nov 2022 • Yudong Li, Yunlin Lei, Xu Yang
Spiking neural networks (SNNs) have made great progress on both performance and efficiency over the last few years, but their unique working pattern makes it hard to train a high-performance low-latency SNN. Thus the development of SNNs still lags behind traditional artificial neural networks (ANNs). To compensate this gap, many extraordinary works have been proposed. Nevertheless, these works are mainly based on the same kind of network structure (i. e. CNN) and their performance is worse than their ANN counterparts, which limits the applications of SNNs. To this end, we propose a novel Transformer-based SNN, termed "Spikeformer", which outperforms its ANN counterpart on both static dataset and neuromorphic dataset and may be an alternative architecture to CNN for training high-performance SNNs. First, to deal with the problem of "data hungry" and the unstable training period exhibited in the vanilla model, we design the Convolutional Tokenizer (CT) module, which improves the accuracy of the original model on DVS-Gesture by more than 16%. Besides, in order to better incorporate the attention mechanism inside Transformer and the spatio-temporal information inherent to SNN, we adopt spatio-temporal attention (STA) instead of spatial-wise or temporal-wise attention. With our proposed method, we achieve competitive or state-of-the-art (SOTA) SNN performance on DVS-CIFAR10, DVS-Gesture, and ImageNet datasets with the least simulation time steps (i. e. low latency). Remarkably, our Spikeformer outperforms other SNNs on ImageNet by a large margin (i. e. more than 5%) and even outperforms its ANN counterpart by 3. 1% and 2. 2% on DVS-Gesture and ImageNet respectively, indicating that Spikeformer is a promising architecture for training large-scale SNNs and may be more suitable for SNNs compared to CNN. We believe that this work shall keep the development of SNNs in step with ANNs as much as possible. Code will be available.
