Correlation-based Prediction

Correlation-Based Prediction is a branch prediction technique in computer architecture that exploits correlations between the outcomes of previously executed branches to improve the accuracy of predicting future branch behavior, differing from simpler predictors by leveraging historical patterns of conditional jumps across multiple control-flow locations.

133

Publications

6.8K

Citations

442

Authors

186

Institutions

Publications per year

2017–2026

38

Authors

442

  1. YN
    Yale N. Patt

    The University of Texas at Austin, United States

    6 publications · 608 citations

    6

    h-index

  2. CY
    Cliff Young

    Google (United States), United States

    5 publications · 366 citations

    5

    h-index

  3. MD
    Michael D. Smith

    Carnegie Mellon University, United States

    4 publications · 211 citations

    4

    h-index

  4. ME
    Marius Evers

    University of Michigan, United States

    3 publications · 146 citations

    3

    h-index

  5. AS
    André Seznec

    Institut de Recherche en Informatique et Systèmes Aléatoires, France

    3 publications · 182 citations

    3

    h-index

1–5 of 442

Venues

  1. ACM SIGARCH Computer Architecture News

    4 publications · 161 citations

    4

    h-index

  2. ACM SIGPLAN Notices

    3 publications · 134 citations

    3

    h-index

  3. Journal of Turbomachinery

    3 publications · 52 citations

    3

    h-index

  4. IEEE Transactions on Evolutionary Computation

    2 publications · 106 citations

    2

    h-index

  5. Ultrasonic Imaging

    2 publications · 108 citations

    2

    h-index

1–5