神经元计算机操作系统的资源分配方法

A Resource Allocation Method for Neuron Computer Operating System

神经形态计算硬件是专为运行脉冲神经网络(spiking neural network,SNN)应用而设计的专用计算机系统.随着硬件资源规模的增大,神经元计算机能够支持更多数量的SNN应用并发运行,如何有效地为SNN应用分配神经形态计算硬件资源变得极具挑战性.首先提出一种神经元计算机操作系统的资源分配框架,在加载SNN应用到神经形态计算硬件时分配硬件资源以及建立对应的输入输出路由,实现了资源分配过程与编译器间的解耦.其次,创新性地引入了最大空矩形(maximum empty rectangle,MER)算法来处理神经形态计算硬件资源的动态分配问题;针对SNN应用的脉冲输入输出特性,提出了一种最小化脉冲输入输出通信代价的资源分配算法,旨在降低脉冲输入输出能耗、延迟和资源碎片.实验结果显示,所提算法比现有算法表现出较好的性能,其中脉冲输入输出平均延迟最高降低了81%,碎片率降低了92%.

Neuromorphic hardware is a specialized computer system designed for running spiking neural network(SNN)applications.With the increasing scale of hardware resources and the challenge of concurrent execution of numerous SNN applications,efficiently allocating neuromorphic hardware resources to SNN applications has become highly challenging.We propose a resource allocation process for a neural computer operating system that maximizes the decoupling of resource allocation from compiler.We allocate hardware resources and corresponding input-output routing for SNN applications only when loading them onto the neuromorphic hardware.Furthermore,we introduce the innovative maximum empty rectangle(MER)algorithm to address the management and dynamic allocation of neuromorphic hardware resources.Additionally,we present a resource allocation algorithm that minimizes the communication cost of spiking-based input-output in SNNs,aiming to reduce energy consumption,latency,and resource fragmentation.Experimental results demonstrate that our algorithm outperforms existing approaches in terms of energy consumption,latency,and fragmentation rate.