Spike-driven Transformer V2: Meta Spiking Neural Network Architecture
  Inspiring the Design of Next-generation Neuromorphic Chips
Man Yao Jiakui Hu Tianxiang Hu Yifan Xu Zhaokun Zhou Yonghong Tian Bo Xu Guoqi Li
Neuromorphic computing, which exploits Spiking Neural Networks (SNNs) on
neuromorphic chips, is a promising energy-efficient alternative to traditional
AI. CNN-based SNNs are the current mainstream of neuromorphic computing. By
contrast, no neuromorphic chips are designed especially for Transformer-based
SNNs, which have just emerged, and their performance is only on par with
CNN-based SNNs, offering no distinct advantage. In this work, we propose a
general Transformer-based SNN architecture, termed as ``Meta-SpikeFormer",
whose goals are: 1) Lower-power, supports the spike-driven paradigm that there
is only sparse addition in the network; 2) Versatility, handles various vision
tasks; 3) High-performance, shows overwhelming performance advantages over
CNN-based SNNs; 4) Meta-architecture, provides inspiration for future
next-generation Transformer-based neuromorphic chip designs. Specifically, we
extend the Spike-driven Transformer in \citet{yao2023spike} into a meta
architecture, and explore the impact of structure, spike-driven self-attention,
and skip connection on its performance. On ImageNet-1K, Meta-SpikeFormer
achieves 80.0\% top-1 accuracy (55M), surpassing the current state-of-the-art
(SOTA) SNN baselines (66M) by 3.7\%. This is the first direct training SNN
backbone that can simultaneously supports classification, detection, and
segmentation, obtaining SOTA results in SNNs. Finally, we discuss the
inspiration of the meta SNN architecture for neuromorphic chip design. Source
code and models are available at
\url{https://github.com/BICLab/Spike-Driven-Transformer-V2}.