EdgeMark: An automation and benchmarking system for embedded artificial intelligence tools

IF 3.7 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Journal of Systems Architecture Pub Date : 2025-07-09 DOI:10.1016/j.sysarc.2025.103488

Mohammad Amin Hasanpour , Mikkel Kirkegaard , Xenofon Fafoutis

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引用次数: 0

Abstract

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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EdgeMark：嵌入式人工智能工具的自动化和基准测试系统

将人工智能（AI）集成到嵌入式设备中，这种范式被称为嵌入式人工智能（eAI）或微型机器学习（TinyML），通过在边缘实现智能数据处理，正在改变行业。然而，这个领域中可用的许多工具让研究人员和开发人员想知道哪一个最适合他们的需要。本文回顾了现有的eAI工具，强调了它们的特性、权衡和限制。此外，我们还介绍了EdgeMark，这是一个开源自动化系统，旨在简化嵌入式平台上部署和测试机器学习（ML）模型的工作流程。EdgeMark简化了模型生成、优化、转换和部署，同时提高了模块化、可重复性和可扩展性。实验基准测试结果展示了广泛使用的eAI工具（包括TensorFlow Lite Micro （TFLM）、Edge Impulse、Ekkono和Renesas eAI Translator）在各种模型中的性能，揭示了它们的相对优势和劣势。研究结果为研究人员和开发人员选择最适合特定应用需求的工具提供了指导，而EdgeMark降低了采用eAI技术的障碍。

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

Journal of Systems Architecture 工程技术-计算机：硬件

CiteScore

8.70

自引率

15.60%

发文量

226

审稿时长

46 days

期刊介绍： The Journal of Systems Architecture: Embedded Software Design (JSA) is a journal covering all design and architectural aspects related to embedded systems and software. It ranges from the microarchitecture level via the system software level up to the application-specific architecture level. Aspects such as real-time systems, operating systems, FPGA programming, programming languages, communications (limited to analysis and the software stack), mobile systems, parallel and distributed architectures as well as additional subjects in the computer and system architecture area will fall within the scope of this journal. Technology will not be a main focus, but its use and relevance to particular designs will be. Case studies are welcome but must contribute more than just a design for a particular piece of software. Design automation of such systems including methodologies, techniques and tools for their design as well as novel designs of software components fall within the scope of this journal. Novel applications that use embedded systems are also central in this journal. While hardware is not a part of this journal hardware/software co-design methods that consider interplay between software and hardware components with and emphasis on software are also relevant here.