Neural Dynamics Pruning for Energy-Efficient Spiking Neural Networks
Haoyu Huang, Linxuan He, Faqiang Liu, Rong Zhao, Luping Shi · 2024
Spiking neural networks (SNNs) excel in spatio-temporal information processing, yet computational intensity limits their adoption. Although prior research have focused on spatial pruning or neuronal dynamics optimization for energy-efficiency, pruning based on neuronal dynamics parameters remains unexplored. This paper introduces a two-stage evolutionary compression technique that utilizes rich dynamics patterns to guide the pruning process, termed Spiking Neuronal Dynamics Pruning (SNDP). SNDP comprises Neuronal Dynamics Searcher (NDS) and Dynamics Pattern Transducer (DPT). NDS uses an evolutionary strategy to identify neuron clusters with optimal dynamics for achieving high performance. DPT then prunes neurons by retaining one from each cluster, preserving key parameters and synaptic weights identified by NDS. Experiments show that SNDP reduces inference energy by up to 97.5% in various SNNs, with minimal performance loss across diverse neuromorphic vision and speech datasets. Furthermore, pruned networks show enhanced noise robustness compared to same-sized traditional trained SNNs. SNDP shows promise in compressing SNNs by harnessing the power of neural dynamics.