On-device Inference

On-Device Inference is an academic concept referring to the execution of machine learning inference directly on edge devices, distinguished from cloud-based or centralized inference by its reliance on local computational resources to enable low-latency, privacy-preserving, and energy-efficient AI deployment.

127

Publications

7K

Citations

548

Authors

142

Institutions

Publications per year

2017–2026

121

Authors

548

  1. MX
    Mengwei Xu

    Beijing University of Posts and Telecommunications, China

    6 publications · 132 citations

    6

    h-index

  2. ND
    Nicholas D. Lane

    University of Cambridge, United Kingdom

    6 publications · 934 citations

    6

    h-index

  3. FK
    Fahim Kawsar

    Nokia (United Kingdom), United Kingdom

    5 publications · 698 citations

    5

    h-index

  4. AM
    Akhil Mathur

    Nokia (United Kingdom), United Kingdom

    4 publications · 296 citations

    4

    h-index

  5. MG
    Matthias Grundmann

    Google (United States), United States

    4 publications · 176 citations

    4

    h-index

1–5 of 548

Venues

  1. arXiv (Cornell University)

    Repository · 23 publications · 650 citations

    14

    h-index

  2. 4

    h-index

  3. Sensors

    3 publications · 48 citations

    3

    h-index

  4. IEEE Transactions on Mobile Computing

    TMCCI · 4 publications · 75 citations

    3

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 152 citations

    3

    h-index

1–5