QP-SNN: Quantized and Pruned Spiking Neural Networks

TL;DR

This paper introduces QP-SNN, a lightweight, hardware-friendly spiking neural network that combines quantization and pruning techniques to improve efficiency and performance for resource-limited edge devices.

Contribution

The paper presents a novel combination of uniform quantization and structured pruning with new strategies, significantly improving SNN deployment in resource-constrained environments.

Findings

Achieves state-of-the-art performance with reduced storage and computation.

Introduces a weight rescaling strategy for better quantization.

Develops a pruning criterion based on spike activity singular values.

Abstract

Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to encode information and operate in an asynchronous event-driven manner, offering a highly energy-efficient paradigm for machine intelligence. However, the current SNN community focuses primarily on performance improvement by developing large-scale models, which limits the applicability of SNNs in resource-limited edge devices. In this paper, we propose a hardware-friendly and lightweight SNN, aimed at effectively deploying high-performance SNN in resource-limited scenarios. Specifically, we first develop a baseline model that integrates uniform quantization and structured pruning, called QP-SNN baseline. While this baseline significantly reduces storage demands and computational costs, it suffers from performance decline. To address this, we conduct an in-depth analysis of the challenges in quantization and pruning that lead to performance degradation and propose solutions to enhance the baseline's performance. For weight quantization, we propose a weight rescaling strategy that utilizes bit width more effectively to enhance the model's representation capability. For structured pruning, we propose a novel pruning criterion using the singular value of spatiotemporal spike activities to enable more accurate removal of redundant kernels. Extensive experiments demonstrate that integrating two proposed methods into the baseline allows QP-SNN to achieve state-of-the-art performance and efficiency, underscoring its potential for enhancing SNN deployment in edge intelligence computing.