{"ID":2841509,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.17567","arxiv_id":"2511.17567","title":"Temporal-adaptive Weight Quantization for Spiking Neural Networks","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.","short_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 Quantizati...","url_abs":"https://arxiv.org/abs/2511.17567","url_pdf":"https://arxiv.org/pdf/2511.17567v1","authors":"[\"Han Zhang\",\"Qingyan Meng\",\"Jiaqi Wang\",\"Baiyu Chen\",\"Zhengyu Ma\",\"Xiaopeng Fan\"]","published":"2025-11-14T05:08:14Z","proceeding":"cs.NE","tasks":"[\"cs.NE\",\"cs.AI\",\"cs.CV\"]","methods":"[]","has_code":false}