Power analysis of embedded software: a first step towards software power minimization
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This paper proposes a novel technique for power-performance trade-off based on profile-driven code execution. Specifically, we show that there is an optimal level of parallelism for energy consumption and propose a compiler-assisted technique for code annotation that can be used at run-time to adaptively trade-off power and performance. As shown by experimental results, our approach is up to 23% better than clock throttling and is as efficient as voltage scaling (up to 10% better in some cases). The technique proposed in this paper can be used by an ACPI-compliant power manager for prolonging battery life or as a passive cooling feature for thermal management.