针对脉冲神经网络的剪枝难题，提出时序感知的自适应剪枝方法。
Spiking Layer-Adaptive Magnitude-based Pruning
- 基于时序失真约束，动态分配各层剪枝比例
- 在多个数据集上实现高压缩率下精度基本不变
- 适合追求低功耗部署的脉冲神经网络研究者
脉冲神经网络（SNNs）具备低功耗计算优势，但其部署受限于密集连接和高昂的脉冲操作开销。现有基于重要性的剪枝方法在直接应用于SNN时，未能考虑时间累积效应、非均匀时间步贡献及膜电位稳定性，常导致性能严重下降。本文提出脉冲层自适应幅度剪枝（SLAMP），一种理论指导的剪枝框架，通过显式控制跨层与时间步的最坏情况输出失真，将稀疏性分配建模为时序失真约束优化问题，得到时间感知的层重要性评分，在单时间步极限下退化为传统层自适应剪枝。设计了两阶段高效流程：先估计时序重要性得分，再全局分配稀疏度并结合重训练恢复稳定性。在CIFAR10、CIFAR100和事件驱动的CIFAR10-DVS数据集上的实验表明，SLAMP显著降低连接密度与脉冲操作量，同时保持准确率，实现了高效可部署的SNN推理。
原文摘要 · Abstract (English)
Spiking Neural Networks (SNNs) provide energy-efficient computation but their deployment is constrained by dense connectivity and high spiking operation costs. Existing magnitude-based pruning strategies, when naively applied to SNNs, fail to account for temporal accumulation, non-uniform timestep contributions, and membrane stability, often leading to severe performance degradation. This paper proposes Spiking Layer-Adaptive Magnitude-based Pruning (SLAMP), a theory-guided pruning framework that generalizes layer-adaptive magnitude pruning to temporal SNNs by explicitly controlling worst-case output distortion across layers and timesteps. SLAMP formulates sparsity allocation as a temporal distortion-constrained optimization problem, yielding time-aware layer importance scores that reduce to conventional layer-adaptive pruning in single-timestep limit. An efficient two-stage procedure is derived, combining temporal score estimation, global sparsity allocation, and magnitude pruning with retraining for stability recovery. Experiments on CIFAR10, CIFAR100, and the event-based CIFAR10-DVS datasets demonstrate that SLAMP achieves substantial connectivity and spiking operation reductions while preserving accuracy, enabling efficient and deployable SNN inference.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。