Pipeline Mapping

Pipeline Mapping is a methodological framework within distributed deep learning that orchestrates the assignment of sequential layers of a neural network to distinct computational devices in a pipeline structure, thereby enabling efficient parallel execution during model training.

29

Publications

1.4K

Citations

122

Authors

42

Institutions

Publications per year

2017–2026

21

Authors

122

  1. HC
    Huanhuan Chen

    University of Science and Technology of China, China

    3 publications · 56 citations

    3

    h-index

  2. VK
    Viktor K. Prasanna

    University of Southern California, United States

    3 publications · 321 citations

    3

    h-index

  3. XZ
    Xuehai Zhou

    University of Science and Technology of China, China

    2 publications · 150 citations

    2

    h-index

  4. CW
    Chao Wang

    University of Science and Technology of China, China

    2 publications · 150 citations

    2

    h-index

  5. NZ
    Noa Zilberman

    University of Cambridge, United Kingdom

    2 publications · 201 citations

    2

    h-index

1–5 of 122

Venues

  1. IEEE Transactions on Computers

    3 publications · 102 citations

    3

    h-index

  2. 3

    h-index

  3. 1

    h-index

  4. Mechatronics

    1 publications · 27 citations

    1

    h-index

  5. IEEE Access

    1 publications · 11 citations

    1

    h-index

1–5