Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks
文献类型：期刊论文
| 作者 | Chen, Shuo1,2; Liu, Zeshi1; You, Haihang1 |
| 刊名 | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
 |
| 出版日期 | 2025-12-25 |
| 卷号 | 37期号:27-28页码:13 |
| 关键词 | brain-inspired computing network pruning spiking neural network |
| ISSN号 | 1532-0626 |
| DOI | 10.1002/cpe.70404 |
| 英文摘要 | Spiking neural networks (SNNs) have gained significant attention due to their energy-efficient and multiplication-free characteristics. Despite these advantages, deploying large-scale SNNs on edge hardware is challenging due to limited resource availability. Network pruning offers a viable approach to compress the network scale and reduce hardware resource requirements for model deployment. However, existing SNN pruning methods cause high pruning costs and performance loss because they lack efficiency in processing the sparse spike representation of SNNs. In this paper, inspired by the critical brain hypothesis in neuroscience and the high biological plausibility of SNNs, we explore and leverage criticality to facilitate efficient pruning in deep SNNs. We first explain criticality in SNNs from the perspective of maximizing feature information entropy. Second, we propose a low-cost metric to assess neuron criticality in feature transmission and design a pruning-regeneration method that incorporates this criticality into the pruning process. Experimental results demonstrate that our method achieves higher performance than the current state-of-the-art (SOTA) method with up to 95.26% reduction in pruning cost. The criticality-based regeneration process efficiently selects potential structures and facilitates consistent feature representation. Our code is available at |
| 资助项目 | Postdoctoral Fellowship Program (Grade B) of China Postdoctoral Science Foundation[GZB20240760] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001624343100015 |
| 出版者 | WILEY |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/43087]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Liu, Zeshi; You, Haihang |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Chen, Shuo,Liu, Zeshi,You, Haihang. Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks[J]. CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE,2025,37(27-28):13. |
| APA | Chen, Shuo,Liu, Zeshi,&You, Haihang.(2025).Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks.CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE,37(27-28),13. |
| MLA | Chen, Shuo,et al."Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks".CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 37.27-28(2025):13. |
入库方式： OAI收割
来源：计算技术研究所
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