基于精准通信建模的脉冲神经网络工作负载自动映射器

Workload automatic mapper for spiking neural network based on precise communication modeling

在分布式计算平台上运行大规模的脉冲神经网络(SNN)是提升类脑计算智能水平的基本手段之一,它的难点在于如何将SNN部署到对应数量的计算节点上,使整体系统的运行能效最佳。针对以上问题,在基于NEST的SNN工作负载自动映射器(SWAM)的基础上,提出一种基于精准通信建模的SNN工作负载自动映射器(SWAM2)。在SWAM2中,基于NEST仿真器对SNN工作负载的通信部分进行精准建模,并改进工作负载模型中参数的量化方法,设计了最大网络规模预测方法。在SNN典型案例上的实验结果表明,在工作负载通信以及计算时间的预测中,SWAM2的平均预测误差比SWAM分别降低12.62和5.15个百分点;在对工作负载最佳映射的预测中,SWAM2的平均准确率为97.55%,比SWAM高13.13个百分点。SWAM2通过自动预测SNN工作负载在计算平台上的最佳部署/映射,避免了手动反复实验的过程。

Running a large-scale Spiking Neural Network(SNN)on a distributed computing platform is one of the basic means to improve the level of brain-like computing intelligence.The difficulty lies in how to deploy the SNN to the corresponding number of computing nodes in order to make the overall system run with the best energy efficiency.To solve this problem,on the basis of NEural Simulation Tool-based(NEST-based)Workload Automatic Mapper for SNN(SWAM)proposed by others before,a workload automatic mapper for SNN,named SWAM2,based on precise communication modeling was proposed.In SWAM2,based on the NEST simulator,the communication part of the SNN workload was further accurately modeled;the quantization method of the parameters in the workload model was improved;the maximum network scale prediction method was designed.Experimental results on typical cases of SNN show that,the average prediction errors of SWAM2 were reduced by about 12.62 and 5.15 percentage points respectively compared with those of SWAM in workload communication and computing time prediction.When predicting the optimal mapping of the workload,the average accuracy of SWAM2 reached 97.55%,which was 13.13 percentage points higher than that of SWAM.SWAM2 avoids the process of manual trial and error by automatically predicting the optimal deployment/mapping of SNN workload on computing platform.