Approximate Computing

Approximate Computing is a computational paradigm that strategically relaxes the requirement for full precision in data processing to achieve significant gains in energy efficiency, performance, or resource utilization by exploiting the inherent tolerance of certain applications to output inaccuracies.

612

Publications

32.5K

Citations

1.4K

Authors

399

Institutions

Publications per year

2017–2026

392

Authors

1.4K

  1. AR
    Anand Raghunathan

    Princeton University, United States

    22 publications · 2.7K citations

    20

    h-index

  2. MS
    Muhammad Shafique

    Karlsruhe Institute of Technology, Germany

    22 publications · 1.1K citations

    19

    h-index

  3. SV
    Swagath Venkataramani

    Purdue University West Lafayette, United States

    16 publications · 1.2K citations

    16

    h-index

  4. KR
    Kaushik Roy

    Purdue University West Lafayette, United States

    16 publications · 2.4K citations

    16

    h-index

  5. JH
    Jie Han

    University of Alberta, Canada

    16 publications · 2.7K citations

    16

    h-index

1–5 of 1.4K

Venues

  1. 20

    h-index

  2. 15

    h-index

  3. IEEE Access

    14 publications · 601 citations

    14

    h-index

  4. IEEE Transactions on Computers

    12 publications · 1.1K citations

    12

    h-index

  5. 11

    h-index

1–5