Journal
IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
Volume 27, Issue 6, Pages 1404-1415Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVLSI.2019.2896776
Keywords
Multicores; runtime management (RTM); thermal prediction
Funding
- EPSRC [EP/L000563/1]
- PRiME Programme [EP/K034448/1]
- EPSRC [EP/K034448/1, EP/L000563/1] Funding Source: UKRI
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Current multicore platforms contain different types of cores, organized in clusters (e.g., ARM's big. LITTLE). These platforms deal with concurrently executing applications, having varying workload profiles and performance requirements. Runtime management is imperative for adapting to such performance requirements and workload variabilities and to increase energy and temperature efficiency. Temperature has also become a critical parameter since it affects reliability, power consumption, and performance and, hence, must be managed. This paper proposes an accurate temperature prediction scheme coupled with a runtime energy management approach to proactively avoid exceeding temperature thresholds while maintaining performance targets. Experiments show up to 20% energy savings while maintaining high-temperature averages and peaks below the threshold. Compared with state-of-the-art temperature predictors, this paper predicts 35% faster and reduces the mean absolute error from 3.25 to 1.15 degrees C for the evaluated applications' scenarios.
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