TRIM: Temporal-subspace Reconstruction Induced by Moments for Post-Training Structured Pruning of Spiking Neural Networks

Spiking neural networks (SNNs) offer energy-efficient computation through sparse and event-driven processing, but their growing scale imposes substantial storage and computational burdens. Existing structured pruning methods for SNNs typically rely on sparsity-aware training or post-pruning fine-tuning, making model compression expensive. To our knowledge, we propose the first post-training structured pruning framework for SNNs that requires neither retraining nor fine-tuning. We begin by establishing a surrogate membrane-potential reconstruction baseline that uses a small calibration set to guide structure selection and weight compensation. However, under tight structural budgets and given the distinctive dynamics of SNNs, accurately reconstructing full temporal trajectories would require the retained structures to capture both low-order temporal components and fine-grained temporal variations. To address this challenge, we propose TRIM, namely Temporal-subspace Reconstruction Induced by Moments, which projects surrogate membrane potential trajectories onto a fixed temporal subspace that captures their mean level and linear temporal trend. This shifts the reconstruction objective toward preserving these low-order temporal components rather than matching entire trajectories. Experiments across diverse visual SNNs and spiking language models demonstrate competitive accuracy–compression trade-offs without any fine-tuning.