A Review on the emerging technology of TinyML

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS ACM Computing Surveys Pub Date : 2024-04-30 DOI:10.1145/3661820
Vasileios Tsoukas, Anargyros Gkogkidis, Eleni Boumpa, Athanasios Kakarountas
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Abstract

Tiny Machine Learning (TinyML) is an emerging technology proposed by the scientific community for developing autonomous and secure devices that can gather, process, and provide results without transferring data to external entities. The technology aims to democratize AI by making it available to more sectors and contribute to the digital revolution of intelligent devices. In this work, a classification of the most common optimization techniques for Neural Network compression is conducted. Additionally, a review of the development boards and TinyML software is presented. Furthermore, the work provides educational resources, a classification of the technology applications, and future directions and concludes with the challenges and considerations.

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TinyML 新兴技术综述
微型机器学习(TinyML)是科学界提出的一项新兴技术,用于开发自主、安全的设备,这些设备可以收集、处理数据并提供结果,而无需将数据传输给外部实体。该技术旨在通过让更多行业使用人工智能来实现人工智能的民主化,并为智能设备的数字革命做出贡献。在这项工作中,对最常见的神经网络压缩优化技术进行了分类。此外,还介绍了开发板和 TinyML 软件。此外,作品还提供了教育资源、技术应用分类和未来发展方向,并在最后提出了挑战和注意事项。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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