BASC : Behavior-Aligned Quantization and Pruning for Low-Bit Spiking Neural Networks

• URL: http://arxiv.org/abs/2608.19239v1
• Date: Wed, 12 Aug 2026 03:39:24 GMT
• Title: BASC : Behavior-Aligned Quantization and Pruning for Low-Bit Spiking Neural Networks
• Abstract summary: Spiking Neural Networks (SNNs) encode information through binary spikes and compute in an event-driven manner.<n>High-performance SNNs incur substantial memory and timestep-wise costs that hinder deployment on resource-constrained devices.<n>We propose Behavior-Aligned SNN Compression (BASC), a unified framework with two lightweight modules.
• Score: 6.5713207503171285
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Spiking Neural Networks (SNNs) encode information through binary spikes and compute in an event-driven manner, offering an energy-efficient paradigm for machine intelligence. However, high-performance SNNs incur substantial memory and timestep-wise computation costs that hinder deployment on resource-constrained devices. Quantization and pruning provide complementary routes to reducing these costs, yet both make their decisions with local criteria that overlook temporal task feedback in quantization and inter-channel dependencies in pruning. Consequently, optimizing either criterion can still yield suboptimal compression performance. We refer to this discrepancy as criterion-behavior mismatch and propose Behavior-Aligned SNN Compression (BASC), a unified framework with two lightweight modules. For quantization, the scale is applied to synaptic current at every timestep and therefore shifts spike timing. Temporal-Behavior Scale Correction (TSC) makes the scale learnable under a temporal loss, allowing firing behavior to inform scale optimization. For pruning, channel importance depends on how channels jointly drive the membrane potential across the firing threshold. Boundary-Level Inter-Channel Correction (BIC) uses channelwise importance scores for initial selection and inter-channel information to re-evaluate only channels near the pruning threshold. Extensive experiments on static and neuromorphic benchmarks show that lower-bit BASC models match or outperform higher-bit baselines and retain this accuracy advantage after structured pruning, while further reducing model storage and synaptic operations.

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