Tiny Machine Learning

Tiny Machine Learning is a machine learning paradigm that enables the deployment of optimized models capable of performing inference and training directly on resource-constrained microcontroller-based edge devices within Internet of Things embedded systems, distinguished by extreme efficiency in memory, power, and computational requirements.

228

Publications

10.8K

Citations

723

Authors

254

Institutions

Publications per year

2017–2026

227

Authors

723

  1. VJ
    Vijay Janapa Reddi

    Harvard University Press, United States

    8 publications · 737 citations

    8

    h-index

  2. CG
    Chuang Gan

    IBM (United States), United States

    7 publications · 441 citations

    7

    h-index

  3. CB
    Colby Banbury

    Harvard University Press, United States

    7 publications · 570 citations

    7

    h-index

  4. SH
    Song Han

    Massachusetts Institute of Technology, United States

    7 publications · 521 citations

    7

    h-index

  5. BS
    Bharath Sudharsan

    Ollscoil na Gaillimhe – University of Galway, Ireland

    6 publications · 209 citations

    6

    h-index

1–5 of 723

Venues

  1. arXiv (Cornell University)

    Repository · 29 publications · 1.9K citations

    19

    h-index

  2. IEEE Internet of Things Journal

    7 publications · 227 citations

    12

    h-index

  3. Sensors

    10 publications · 484 citations

    12

    h-index

  4. IEEE Access

    8 publications · 623 citations

    10

    h-index

  5. 5

    h-index

1–5