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Energy-Efficient Cache Design Using Criticality Metrics

Learn how to improve cache performance by exploiting critical instructions and data placement techniques. This research explores energy-delay trade-offs and the benefits of classifying instructions as critical or non-critical. Discover the Hot-and-Cold microarchitecture approach and its impact on cache efficiency.

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Energy-Efficient Cache Design Using Criticality Metrics

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  1. Hot-and-Cold: Using Criticality in the Design of Energy-Efficient Caches Rajeev Balasubramonian, University of Utah Viji Srinivasan, IBM T.J. Watson Sandhya Dwarkadas, University of Rochester Alper Buyuktosunoglu, IBM T.J. Watson

  2. All Instructions are not Created Equal • Critical instructions – lie on the program critical path • Non-critical instructions – can be slowed without • increasing execution time • Potential to improve cache performance (?) • [Srinivasan ’01] [Fisk ’99] • Prioritization policies [Fields ’01] [Tune ’01] • Energy-efficient ALUs [Seng ’01]

  3. Energy-Delay Trade-Offs • Example energy-delay trade-off techniques: • Voltage scaling, transistor sizing, way prediction, serial-access • Gated-ground cells, high Vt Transistor sizing Variable threshold voltage

  4. Exploiting Criticality • Design two static banks – • hot bank: fast and high power • cold bank: slow and low power • Instructions have to be classified as critical or not • and • Data has to be placed in one of two banks • Energy-efficient ALUs are easier to handle as there is no • associated storage

  5. Criticality Metric • Oldest-N: The N oldest instructions in the queue • are critical • Younger instructions are likely to be on • mispredicted paths or can tolerate latencies • N can be varied based on program needs • Minimal hardware overhead • Behavior comparable to more complex metrics

  6. Instruction Classification

  7. Data Classification Exclusively critical Exclusively non-critical

  8. Hot-and-Cold Microarchitecture Dispatch Bank Predictor Issue Queue Cold bank Criticality Counters Hot bank Placement Predictor L2

  9. Performance Results

  10. Energy Results

  11. Results Summary • Bank mispredict rate of 9.5% • Criticality mismatch rate of 26% • Performance loss = 2.7% (data reorganization) • + (0.8 x slowdown) • L1 cache energy savings of 37%

  12. Related Work • Recent split-cache organization by Abella and • Gonzalez [ICCD’03] Base Slow Fast • Data allocation based on criticality of accessing • instruction

  13. Conclusions • Data and instruction classification is reasonably • accurate • Overhead from contention is non-trivial • Results are worthwhile in limited settings • The use of criticality for data cache reorganization • yields little benefit

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