Deep Learning On Gpus

Deep Learning On GPUs is a computational framework that leverages the parallel processing architecture of graphics processing units to accelerate the training and inference of deep neural networks, distinguishing it from traditional CPU-based deep learning approaches by its ability to efficiently handle large-scale matrix operations inherent in hierarchical feature learning.

1.1K

Publications

186.8K

Citations

4K

Authors

788

Institutions

Publications per year

2017–2026

916

Authors

4K

  1. AA
    Ammar Ahmad Awan

    The Ohio State University, United States

    17 publications · 853 citations

    15

    h-index

  2. XC
    Xiaowen Chu

    Hong Kong Baptist University, Hong Kong SAR China

    14 publications · 1.1K citations

    14

    h-index

  3. SS
    Shaohuai Shi

    Hong Kong Baptist University, Hong Kong SAR China

    11 publications · 880 citations

    11

    h-index

  4. DK
    Dhabaleswar K. Panda

    The Ohio State University, United States

    11 publications · 450 citations

    11

    h-index

  5. HS
    Hari Subramoni

    The Ohio State University, United States

    11 publications · 363 citations

    11

    h-index

1–5 of 4K

Venues

  1. arXiv (Cornell University)

    Repository · 239 publications · 49.1K citations

    74

    h-index

  2. IEEE Transactions on Parallel and Distributed Systems

    TPDS · 24 publications · 1.1K citations

    20

    h-index

  3. IEEE Access

    22 publications · 2K citations

    16

    h-index

  4. Sensors

    11 publications · 985 citations

    11

    h-index

  5. Applied Sciences

    9 publications · 466 citations

    9

    h-index

1–5