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
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
相关论文
共 50 条
  • [1] An Energy Harvesting Algorithm for UAV-Assisted TinyML Consumer Electronic in Low-Power IoT Networks
    Huang, Jie
    Yu, Tao
    Chakraborty, Chinmay
    Yang, Fan
    Lai, Xianzhi
    Alharbi, Abdullah
    Yu, Keping
    IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2024, 70 (04) : 7346 - 7356
  • [2] Computation Offloading and Resource Allocation for Low-power IoT Edge Devices
    Samie, Farzad
    Tsoutsouras, Vasileios
    Bauer, Lars
    Xydis, Sotirios
    Soudris, Dimitrios
    Henkel, Joerg
    2016 IEEE 3RD WORLD FORUM ON INTERNET OF THINGS (WF-IOT), 2016, : 7 - 12
  • [3] TinyML-Enabled Intelligent Question-Answer Services in IoT Edge Consumer Devices
    Wu, Xuan
    Lin, Xuanye
    Zhang, Zhen
    Chen, Chien-Ming
    Gadekallu, Thippa R.
    Kumari, Saru
    Kumar, Sachin
    IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, 2024, 70 (04) : 7322 - 7333
  • [4] AssureSense: A Framework for Enabling Sensor Fault Detection in Low-Power IoT Edge Devices
    Attarha, Shadi
    Foerster, Anna
    IEEE SENSORS JOURNAL, 2024, 24 (20) : 33791 - 33805
  • [5] Service Management for Enabling Self-Awareness in Low-Power IoT Edge Devices
    Attarha, Shadi
    2022 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS WORKSHOPS AND OTHER AFFILIATED EVENTS (PERCOM WORKSHOPS), 2022,
  • [6] Toward Robust Facial Authentication for Low-Power Edge-AI Consumer Devices
    Yao, Wang
    Varkarakis, Viktor
    Costache, Gabriel
    Lemley, Joseph
    Corcoran, Peter
    IEEE ACCESS, 2022, 10 : 123661 - 123678
  • [7] TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications
    Alajlan, Norah N.
    Ibrahim, Dina M.
    MICROMACHINES, 2022, 13 (06)
  • [8] Low-Power Beam-Switching Technique for Power-Efficient Collaborative IoT Edge Devices
    Oh, Semyoung
    Park, Daejin
    APPLIED SCIENCES-BASEL, 2021, 11 (04): : 1 - 14
  • [9] Extremely Low-power Edge Connected Devices
    Brennan, Robert L.
    Lee, Taylor
    2024 IEEE 67TH INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS, MWSCAS 2024, 2024, : 674 - 677
  • [10] Low-Power Approximate Arithmetic Circuits for IoT Devices
    Thakur, Garima
    Sohal, Harsh
    Jain, Shruti
    RECENT ADVANCES IN ELECTRICAL & ELECTRONIC ENGINEERING, 2022, 15 (05) : 421 - 428