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arXiv · 2511.17567

Temporal-adaptive Weight Quantization for Spiking Neural Networks

Han Zhang·Qingyan Meng·Jiaqi Wang·Baiyu Chen·Zhengyu Ma·Xiaopeng Fan
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Abstract

Weight quantization in spiking neural networks (SNNs) could further reduce energy consumption. However, quantizing weights without sacrificing accuracy remains challenging. In this study, inspired by astrocyte-mediated synaptic modulation in the biological nervous systems, we propose Temporal-adaptive Weight Quantization (TaWQ), which incorporates weight quantization with temporal dynamics to adaptively allocate ultra-low-bit weights along the temporal dimension. Extensive experiments on static (e.g., ImageNet) and neuromorphic (e.g., CIFAR10-DVS) datasets demonstrate that our TaWQ maintains high energy efficiency (4.12M, 0.63mJ) while incurring a negligible quantization loss of only 0.22% on ImageNet.

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Han Zhang, Qingyan Meng, Jiaqi Wang, Baiyu Chen, Zhengyu Ma, Xiaopeng Fan. 2025-11-14. Temporal-adaptive Weight Quantization for Spiking Neural Networks. https://arxiv.org/abs/2511.17567

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