计算机工程与应用2025，Vol.61Issue(11)：156-165,10.DOI:10.3778/j.issn.1002-8331.2407-0164
将SNN部署到类脑处理器的映射优化算法研究
Research on Mapping Optimization Algorithms for Deploying SNN to Brain-Inspired Processors
摘要
Abstract
In recent years,spiking neural network(SNN),which offers biological plausibility and energy efficiency,has garnered widespread attention.However,current mapping schemes for deploying SNN on neuromorphic processors face issues such as high communication delays,severe congestion,high energy consumption,and insufficient node connectivity,thereby reducing their practicality and execution efficiency.To address these problems,an improved deployment algo-rithm based on KL(Kernighan-Lin)and BADE(Boltzmann anneal differential evolution)has been proposed to map SNN onto resource-constrained neuromorphic processors.This algorithm comprises two steps:partitioning and mapping.In the partitioning phase,a global optimization strategy(GRBKL)is introduced into the recursive KL algorithm to minimize communication delay between clusters.In the mapping phase,an attractor-guided BADE algorithm(BAFDE)is proposed to find an allocation scheme that minimizes communication delay and maximum congestion.Finally,the algorithm is eval-uated using five SNN instances,and the results show that,compared to methods like SNEAP and SpiNeMap,the pro-posed algorithm significantly reduces communication delay(by 55.41%and 94.73%,respectively)and maximum conges-tion(by 81.27%and 97.79%,respectively).关键词
脉冲神经网络(SNN)/类脑处理器/启发式算法/片上网络(NOC)Key words
spiking neural network(SNN)/brain-inspired processor/heuristic algorithm/network-on-chip(NOC)分类
信息技术与安全科学引用本文复制引用
陈奥新,陈亮,李千鹏,王智超,徐东君..将SNN部署到类脑处理器的映射优化算法研究[J].计算机工程与应用,2025,61(11):156-165,10.基金项目
科技创新2030-"脑科学与类脑研究"重大项目(2021ZD0200300). （2021ZD0200300）
