Approximate Computing For Power Reduction

Approximate Computing for Power Reduction is a computational methodology that strategically trades off computational accuracy for reduced power consumption by exploiting error resilience in applications to achieve energy efficiency.

363

Publications

20.8K

Citations

801

Authors

252

Institutions

Publications per year

2017–2026

258

Authors

801

  1. AR
    Anand Raghunathan

    Princeton University, United States

    18 publications · 2.5K citations

    18

    h-index

  2. MS
    Muhammad Shafique

    Karlsruhe Institute of Technology, Germany

    15 publications · 769 citations

    15

    h-index

  3. FL
    Fabrizio Lombardi

    Northeastern University, United States

    14 publications · 1.4K citations

    14

    h-index

  4. JH
    Jie Han

    University of Alberta, Canada

    14 publications · 2.4K citations

    14

    h-index

  5. WL
    Weiqiang Liu

    Nanjing University of Aeronautics and Astronautics, China

    13 publications · 699 citations

    13

    h-index

1–5 of 801

Venues

  1. 19

    h-index

  2. 11

    h-index

  3. 11

    h-index

  4. IEEE Transactions on Computers

    10 publications · 1.2K citations

    11

    h-index

  5. IEEE Transactions on Emerging Topics in Computing

    ITETBT · 9 publications · 507 citations

    9

    h-index

1–5