GEMM-ArchProfiler: A simulation framework for hardware-level profiling and performance analysis of General Matrix Multiplication in real CNN workloads on heterogeneous CPU architectures

IF 2.4 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

SoftwareX Pub Date : 2025-06-28 DOI:10.1016/j.softx.2025.102243

Binu Ayyappan , G. Santhosh Kumar

引用次数: 0

Abstract

In this paper, the authors present GEMM-ArchProfiler, a simulation framework for evaluating General Matrix Multiplication performance in convolutional neural networks. Targeted at resource-constrained edge and IoT systems, which rely on CPU-based architectures, the framework addresses hardware limitations through optimized workload profiling. Powered by the gem5 simulator, GEMM-ArchProfiler provides insights into memory usage, cache behavior, execution latency, and energy consumption. It integrates customized Darknet libraries to simulate realistic CNN workloads and includes a user-friendly CPU configuration mechanism and event analysis script. This tool bridges workload analysis and deployment, aiding efficient AI implementation on diverse CPU architectures.

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GEMM-ArchProfiler：一个模拟框架，用于在异构CPU架构上的真实CNN工作负载中进行通用矩阵乘法的硬件级分析和性能分析

在本文中，作者提出了GEMM-ArchProfiler，一个用于评估卷积神经网络中一般矩阵乘法性能的仿真框架。针对资源受限的边缘和物联网系统，这些系统依赖于基于cpu的架构，该框架通过优化工作负载分析来解决硬件限制。在gem5模拟器的支持下，gem - archprofiler提供了对内存使用、缓存行为、执行延迟和能耗的洞察。它集成了定制的Darknet库来模拟真实的CNN工作负载，并包括一个用户友好的CPU配置机制和事件分析脚本。该工具连接了工作负载分析和部署，帮助在不同CPU架构上高效地实现AI。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

SoftwareX COMPUTER SCIENCE, SOFTWARE ENGINEERING-

CiteScore

5.50

自引率

2.90%

发文量

184

审稿时长

9 weeks

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