Spiking Neural Networks (SNNs) are well known as a promising energy-efficient alternative to conventional artificial neural networks. Subject to the preconceived impression that SNNs are sparse firing, the analysis and optimization of inherent redundancy in SNNs have been largely overlooked, thus the potential advantages of spike-based neuromorphic computing in accuracy and energy efficiency are interfered. In this work, we pose and focus on three key questions regarding the inherent redundancy in SNNs. We argue that the redundancy is induced by the spatio-temporal invariance of SNNs, which enhances the efficiency of parameter utilization but also invites lots of noise spikes. Further, we analyze the effect of spatio-temporal invariance on the spatio-temporal dynamics and spike firing of SNNs. Then, motivated by these analyses, we propose an Advance Spatial Attention (ASA) module to harness SNNs' redundancy, which can adaptively optimize their membrane potential distribution by a pair of individual spatial attention sub-modules. In this way, noise spike features are accurately regulated. Experimental results demonstrate that the proposed method can significantly drop the spike firing with better performance than state-of-the-art SNN baselines. Our code is available in \url{https://github.com/BICLab/ASA-SNN}.

翻译：脉冲神经网络（SNNs）因其作为传统人工神经网络有前景的低能耗替代方案而广为人知。受限于SNN具有稀疏发放的先入为主的印象，对SNN中固有冗余性的分析与优化在很大程度上被忽视，从而干扰了基于脉冲的神经形态计算在精度和能效方面的潜在优势。本文提出并聚焦于SNN中固有冗余性的三个关键问题。我们论证了这种冗余性源于SNN的时空不变性，该特性虽提升了参数利用效率，但也引发了大量噪声脉冲。进一步地，我们分析了时空不变性对SNN时空动力学及脉冲发放的影响。基于这些分析，我们提出了一种先进空间注意力模块来调控SNN的冗余性，该模块通过一对独立的子模块自适应优化其膜电位分布，从而准确调节噪声脉冲特征。实验结果表明，所提方法能在显著降低脉冲发放率的同时，取得优于现有SNN基线的性能。我们的代码发布于 \url{https://github.com/BICLab/ASA-SNN}。