对比两款嵌入式平台的微型AI模型实时性能，指导实际部署优化。
Real-Time Performance Benchmarking of TinyML Models in Embedded Systems (PICO: Performance of Inference, CPU, and Operations)
- 构建模块化框架，跨平台评估推理延迟、CPU占用等指标。
- 贝叶斯单板机延迟稳定，树莓派4在资源效率上更优。
- 适合做边缘AI部署的开发者和硬件选型参考。
本文提出PICO-TINYML-BENCHMARK，一个模块化且平台无关的框架，用于评估微型AI模型在资源受限嵌入式系统中的实时性能。通过分析推理延迟、CPU利用率、内存效率和预测稳定性等关键指标，该框架揭示了计算权衡与平台特异性优化方案。我们在贝叶斯单板机AI64和树莓派4上，基于真实数据集对三种典型微型AI模型——手势分类、关键词识别和MobileNet V2进行了基准测试。结果表明：贝叶斯单板机在专用任务中表现出一致的推理延迟，而树莓派4在资源效率和成本效益方面更具优势。这些发现为微型AI部署提供了可操作的优化建议，弥合了理论进展与实际应用之间的差距。
原文摘要 · Abstract (English)
This paper presents PICO-TINYML-BENCHMARK, a modular and platform-agnostic framework for benchmarking the real-time performance of TinyML models on resource-constrained embedded systems. Evaluating key metrics such as inference latency, CPU utilization, memory efficiency, and prediction stability, the framework provides insights into computational trade-offs and platform-specific optimizations. We benchmark three representative TinyML models -- Gesture Classification, Keyword Spotting, and MobileNet V2 -- on two widely adopted platforms, BeagleBone AI64 and Raspberry Pi 4, using real-world datasets. Results reveal critical trade-offs: the BeagleBone AI64 demonstrates consistent inference latency for AI-specific tasks, while the Raspberry Pi 4 excels in resource efficiency and cost-effectiveness. These findings offer actionable guidance for optimizing TinyML deployments, bridging the gap between theoretical advancements and practical applications in embedded systems.
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