Cache Associativity Optimization

Cache Associativity Optimization is a methodological approach in computer architecture that systematically determines and configures the optimal degree of set associativity in cache memories to minimize access latency and power consumption while maintaining cache effectiveness, distinguishing itself from general cache design techniques by specifically targeting the trade-off between associativity level and energy efficiency in low-power contexts.

32

Publications

1.4K

Citations

83

Authors

34

Institutions

Publications per year

2017–2026

2

Authors

83

  1. CZ
    Chuanjun Zhang

    University of California, Riverside, United States

    3 publications · 175 citations

    3

    h-index

  2. MK
    Moinuddin K. Qureshi

    Georgia Institute of Technology, United States

    3 publications · 309 citations

    3

    h-index

  3. FV
    Frank Vahid

    University of California, Riverside, United States

    3 publications · 175 citations

    3

    h-index

  4. JB
    Jay Buckingham

    University of Edinburgh, United Kingdom

    2 publications · 67 citations

    2

    h-index

  5. AN
    Alex Nicolau

    University of California, Irvine, United States

    2 publications · 37 citations

    2

    h-index

1–5 of 83

Venues

  1. Network Computation in Neural Systems

    2 publications · 67 citations

    2

    h-index

  2. Design, Automation, and Test in Europe

    Conference · 2 publications · 78 citations

    2

    h-index

  3. ACM Transactions on Embedded Computing Systems

    TECS · 1 publications · 87 citations

    1

    h-index

  4. IEEE Transactions on Multimedia

    1 publications · 28 citations

    1

    h-index

  5. 1

    h-index

1–5