基于PYNQ集群的内存负载分析系统设计  

Design of Memory Load Analysis System Based on PYNQ Cluster

作　　者：华夏 柴志雷 张曦煌 HUA Xia;CHAI Zhilei;ZHANG Xihuang(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,China;Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence,Wuxi 214122,China)

机构地区：[1]江南大学人工智能与计算机学院,江苏无锡214122 [2]江苏省模式识别与计算智能工程实验室,江苏无锡214122

出　　处：《现代信息科技》2022年第8期1-5,共5页Modern Information Technology

基　　金：国家自然科学基金资助项目(61972180)。

摘　　要：在分布式计算平台上研究脉冲神经网络(SNN)的工作负载特性时,快速确定SNN模型构建所需的内存消耗以及平台的网络承载能力,是提高工作负载研究效率的重要手段。针对该问题,文章搭建了PYNQ集群分布式计算平台,设计了集群内存负载分析系统。实验表明:内存负载分析系统在内存消耗的预测方面取得了97.98%的平均准确率,在预测集群网络承载能力方面取得了97.19%的准确率,通过分析集群承载SNN模型时的内存负载,有效提升了集群上的SNN工作负载研究效率。When studying the workload characteristics of Spiking Neural Network(SNN) on distributed computing platform, it is an important means to improve the efficiency of workload research to quickly determine the memory consumption required for SNN model construction and the network carrying capacity of the platform. To solve this problem, PYNQ cluster distributed computing platform is built and a cluster memory load analysis system is designed. The experimental results show that the average accuracy of the memory load analysis system is 97.98% in the prediction of memory consumption and 97.19% in the prediction of cluster network carrying capacity. By analyzing the memory load when the cluster carries the SNN model, the research efficiency of SNN workload on cluster is effectively improved.

关 键 词：脉冲神经网络(SNN) 分布式计算平台 计算能效 NEST仿真器 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]