TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks

arXiv:2206.10177v3 Announce Type: replace
Abstract: Spiking Neural Networks (SNNs) are attracting widespread interest due to their biological plausibility, energy efficiency, and powerful spatio-temporal information representation ability. Given the critical role of attention mechanisms in enhancing neural network performance, the integration of SNNs and attention mechanisms exhibits potential to deliver energy-efficient and high-performance computing paradigms. We present a novel Temporal-Channel Joint Attention mechanism for SNNs, referred to as TCJA-SNN. The proposed TCJA-SNN framework can effectively assess the significance of spike sequence …

arxiv attention cs.ai cs.cv networks neural networks snn spiking neural networks temporal type