SPRCPl: An Efficient Tool for SNN Models Deployment on Multi-Core Neuromorphic Chips via Pilot Running

Liangshun Wu, Lisheng Xie, Jianwei Xue, Faquan Chen, Qingyang Tian, Yifan Zhou, Ziren Wu, Rendong Ying, Peilin Liu

Abstract: This paper introduce SPRCpl, an efficient compiler/toolkit for deploying Spiking Neural Network (SNN) models on multi-core neuromorphic chips. It uses "pilot running" to optimize the deployment process. It includes a front-end compiler, synapse pruning and regeneration optimizer, and a mapping tool. SPRCpl proposes a synapse pruning scheme based on spike firing statistics obtained through pilot running, dynamically reducing model size. It also presents a mapping scheme that minimizes strikes within and between clusters using spike firing statistics and multi-objective optimization. Experimental results demonstrate SPRCpl’s effectiveness in maintaining model accuracy during pruning and outperforming SpiNeMap in terms of communication count, execution time, and memory usage. It achieves lower latency, reduced power consumption, and higher throughput, making it a promising tool for SNN model deployment on multi-core neuromorphic chips.