AI RESEARCH
Neural Dynamics Self-Attention for Spiking Transformers
arXiv CS.AI
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ArXi:2603.19290v1 Announce Type: cross 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.