SQUAT: Stateful Quantization-Aware Training in Recurrent Spiking Neural Networks

arXiv:2404.19668v1 Announce Type: cross
Abstract: Weight quantization is used to deploy high-performance deep learning models on resource-limited hardware, enabling the use of low-precision integers for storage and computation. Spiking neural networks (SNNs) share the goal of enhancing efficiency, but adopt an 'event-driven' approach to reduce the power consumption of neural network inference. While extensive research has focused on weight quantization, quantization-aware training (QAT), and their application to SNNs, the precision reduction of state variables during training has been largely overlooked, …

arxiv cs.lg cs.ne networks neural networks quantization spiking neural networks training type