TinyML for Empowering Low-Power IoT Edge Consumer Devices

被引：0
|
作者：
Jhaveri, Rutvij H. [1 ]
Chi, Hao Ran [2 ,3 ]
Wu, Huaming [4 ]
机构：
[1] Pandit Deendayal Energy Univ, Sch Technol, Dept Comp Sci & Engn, Gandhinagar 382007, India
[2] Inst Telecomunicaoes, P-3810164 Aveiro, Portugal
[3] Univ Aveiro, P-3810164 Aveiro, Portugal
[4] Tianjin Univ, Ctr Appl Math, Tianjin 300072, Peoples R China
来源：
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS | 2024年 / 70卷 / 04期
关键词：
Tiny machine learning; Consumer electronics;
D O I：
10.1109/TCE.2024.3482353
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Pervasive Artificial Intelligence (AI) has been promoted to be applicable to multiple services and markets, based on the recent surge in AI and machine learning (ML) techniques. Together with the fact that the market size of edge computing has been boosted to 16 billion USD last year (and a forecast to reach more than 200 billion USD by 2030), TinyML will be one of the main forces to embrace the new era of pervasive AI, by embedding the main operations (e.g., training, modeling, and others) in edge computing, relying on its relatively short physical distance to the users/end devices. Therefore, TinyML has promised to support ultra-low latency, enhanced security/privacy, highly demanded scalability, and potentially sustainability by reducing the frequency accessing centralized cloud computing.
引用
收藏
页码：7318 / 7321
页数：4
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