# Temporal-adaptive Weight Quantization for Spiking Neural Networks

Source: [Temporal-adaptive Weight Quantization for Spiking Neural Networks](<https://www.sota2.com/research/paper/6925382fd6b0dd334292f565>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-11-26T05:04:09.144Z

Authors: Han Zhang, Qingyan Meng, Jiaqi Wang, Baiyu Chen, Zhengyu Ma, Xiaopeng Fan

Published: 2025-11-14T05:08:14.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2511.17567>)

[PDF](<https://arxiv.org/pdf/2511.17567.pdf>)