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.

翻译：本研究提出了一种实用的基准测试框架，用于在ARM Cortex处理器（M0+、M4、M7）上优化人工智能（AI）模型，重点关注嵌入式系统中的能效、精度和资源利用率。通过设计一个自动化测试平台，我们提供了一种系统性的方法来评估关键性能指标（KPI），并确定处理器与AI模型的最佳组合。研究揭示了浮点运算（FLOPs）与推理时间之间近乎线性的相关性，为估算计算需求提供了一个可靠的度量标准。利用帕累托分析，我们展示了如何在能耗与模型精度之间权衡取舍，确保AI应用在满足性能要求的同时不损害可持续性。关键发现表明，M7处理器适用于短推理周期，而M4处理器在较长的推理任务中能提供更好的能效。M0+处理器虽然对于复杂AI模型效率较低，但仍适用于较简单的任务。本研究为开发者提供了洞见，指导他们设计出在实际应用中既能提供高性能又具有高能效的AI系统。