Quantization of Spiking Neural Networks Beyond Accuracy

• URL: http://arxiv.org/abs/2604.14487v1
• Date: Wed, 15 Apr 2026 23:55:11 GMT
• Title: Quantization of Spiking Neural Networks Beyond Accuracy
• Abstract summary: We show that firing activity governs effective sparsity, state storage, and event-processing load in SNNs.<n>We propose Earth Mover's Distance as a diagnostic metric for firing distribution divergence.<n>We find that uniform quantization induces distributional drift even when accuracy is preserved, while LQ-Net style learned quantization maintains firing behavior close to the full-precision baseline.
• Score: 3.43677310385818
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantization is a natural complement to the sparse, event-driven computation of Spiking Neural Networks, reducing memory bandwidth and arithmetic cost for deployment on resource-constrained hardware. However, existing SNN quantization evaluation focuses almost exclusively on accuracy, overlooking whether a quantized network preserves the firing behavior of its full-precision counterpart. We demonstrate that quantization method, clipping range, and bit-width can produce substantially different firing distributions at equivalent accuracy, differences invisible to standard metrics but relevant to deployment, where firing activity governs effective sparsity, state storage, and event-processing load. To capture this gap, we propose Earth Mover's Distance as a diagnostic metric for firing distribution divergence, and apply it systematically across weight and membrane quantization on SEW-ResNet architectures trained on CIFAR-10 and CIFAR-100. We find that uniform quantization induces distributional drift even when accuracy is preserved, while LQ-Net style learned quantization maintains firing behavior close to the full-precision baseline. Our results suggest that behavior preservation should be treated as an evaluation criterion alongside accuracy, and that EMD provides a principled tool for assessing it.

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