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.

翻译：脉冲神经网络（SNNs）中的权重量化可进一步降低能耗。然而，在不牺牲精度的情况下量化权重仍具挑战性。本研究受生物神经系统中星形胶质细胞介导的突触调节机制启发，提出了时序自适应权重量化（TaWQ），该方法将权重量化与时序动态特性相结合，沿时间维度自适应分配超低比特权重。在静态数据集（如ImageNet）和神经形态数据集（如CIFAR10-DVS）上的大量实验表明，我们的TaWQ方法在保持高能效（4.12M，0.63mJ）的同时，在ImageNet上仅产生0.22%的可忽略量化损失。