打造自动化工具链，帮开发者快速选型并部署嵌入式AI模型
EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools
- 构建开源系统EdgeMark，自动完成模型生成、优化与部署流程
- 对比TFLM、Edge Impulse等主流工具，实测展示性能差异
- 适合想快速落地嵌入式AI的工程师和研究者使用
将人工智能融入嵌入式设备（即嵌入式AI或微型机器学习）正推动产业变革，实现边缘智能数据处理。然而该领域工具繁多，开发者难以抉择。本文综述现有eAI工具的特性、权衡与局限，并提出开源自动化系统EdgeMark，用于简化在嵌入式平台部署与基准测试机器学习模型的流程。EdgeMark支持模型生成、优化、转换与部署，强调模块化、可复现性与可扩展性。实验对TensorFlow Lite Micro（TFLM）、Edge Impulse、Ekkono、Renesas eAI Translator等常用工具进行了广泛测试，揭示其在多种模型上的相对优劣，为研究人员和开发者根据应用需求选择合适工具提供依据。同时，EdgeMark降低了eAI技术的采用门槛。
原文摘要 · Abstract (English)
The integration of artificial intelligence (AI) into embedded devices, a paradigm known as embedded artificial intelligence (eAI) or tiny machine learning (TinyML), is transforming industries by enabling intelligent data processing at the edge. However, the many tools available in this domain leave researchers and developers wondering which one is best suited to their needs. This paper provides a review of existing eAI tools, highlighting their features, trade-offs, and limitations. Additionally, we introduce EdgeMark, an open-source automation system designed to streamline the workflow for deploying and benchmarking machine learning (ML) models on embedded platforms. EdgeMark simplifies model generation, optimization, conversion, and deployment while promoting modularity, reproducibility, and scalability. Experimental benchmarking results showcase the performance of widely used eAI tools, including TensorFlow Lite Micro (TFLM), Edge Impulse, Ekkono, and Renesas eAI Translator, across a wide range of models, revealing insights into their relative strengths and weaknesses. The findings provide guidance for researchers and developers in selecting the most suitable tools for specific application requirements, while EdgeMark lowers the barriers to adoption of eAI technologies.
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