面向可持续嵌入式系统的ARM Cortex处理器上AI模型的帕累托最优基准测试
Pareto Optimal Benchmarking of AI Models on ARM Cortex Processors for Sustainable Embedded Systems
AI总结：
本文通过帕累托分析优化ARM Cortex处理器上的AI模型，平衡能耗与精度，为可持续嵌入式系统提供高效能解决方案。
摘要：
本文提出了一种实用的基准测试框架，用于优化ARM Cortex处理器（M0+、M4、M7）上的人工智能模型，重点在于嵌入式系统中的能效、精度和资源利用率。通过设计自动化测试平台，我们提供了一种系统的方法来评估关键性能指标（KPIs）并识别处理器和AI模型的最佳组合。研究强调了浮点运算（FLOPs）与推理时间之间近线性的相关性，提供了一种可靠的指标来估算计算需求。利用帕累托分析，我们展示了如何在能耗和模型精度之间取得平衡，确保AI应用在不牺牲可持续性的前提下满足性能要求。关键发现表明，M7处理器适合短推理周期，而M4处理器在更长的推理任务中具有更好的能效。M0+处理器虽然在复杂AI模型上效率较低，但仍适用于简单任务。本文为开发者提供了见解，指导他们设计高效能的AI系统，以在实际应用中实现高性能。
英文摘要：
This work presents a practical benchmarking framework for optimizing artificial intelligence (AI) models on ARM Cortex processors (M0+, M4, M7), focusing on energy efficiency, accuracy, and resource utilization in embedded systems. Through the design of an automated test bench, we provide a systematic approach to evaluate across key performance indicators (KPIs) and identify optimal combinations of processor and AI model. The research highlights a nearlinear correlation between floating-point operations (FLOPs) and inference time, offering a reliable metric for estimating computational demands. Using Pareto analysis, we demonstrate how to balance trade-offs between energy consumption and model accuracy, ensuring that AI applications meet performance requirements without compromising sustainability. Key findings indicate that the M7 processor is ideal for short inference cycles, while the M4 processor offers better energy efficiency for longer inference tasks. The M0+ processor, while less efficient for complex AI models, remains suitable for simpler tasks. This work provides insights for developers, guiding them to design energy-efficient AI systems that deliver high performance in realworld applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
