Neural Dynamics Self-Attention for Spiking Transformers
Integrating Spiking Neural Networks (SNNs) with Transformer architectures offers a promising pathway to balance energy efficiency and performance, particularly for edge vision applications. However, existing Spiking Transformers face two critical challenges: (i) a substantial performance gap compared to their Artificial Neural Networks (ANNs) counterparts and (ii) high memory overhead during inference. Through theoretical analysis, we attribute both limitations to the Spiking Self-Attention (SSA) mechanism: the lack of locality bias and the need to store large attention matrices. Inspired by the localized receptive fields (LRF) and membrane-potential dynamics of biological visual neurons,...
| Authors | Dehao Zhang, Fukai Guo, Shuai Wang et al. |
|---|---|
| Primary category | cs.NE |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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