AI RESEARCH
Spiking Layer-Adaptive Magnitude-based Pruning
arXiv CS.LG
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ArXi:2603.14946v1 Announce Type: new 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.