Hardware-software interaction for run-time power optimization: A case study of embedded Linux on multicore smartphones

Applications running on smartphones interact with the hardware and the system software differently, resulting in widely varying power consumption and hence thermal profiles. Typically, these smartphone platforms expose some hardware power control features to users, controlled through software governors such as cpufreq for dynamic voltage-frequency scaling (DVFS) and cpuquiet for dynamic core selection (DCS). Operating systems on these platforms manage these governors conservatively, independent of application's performance requirement. To address this, we propose an alternative approach, which uses reinforcement learning to explore the trade-off between power saving opportunities using DVFS and DCS and application's performance at run-time. The objective is to reduce power consumption, taking into consideration dynamic power, leakage power, and the inter-dependency between temperature and power. The reinforcement learning-based control is validated as a case-study on ARM A15-based nvidia's tegra smartphone through its implementation as a run-time manager (RTM). This RTM interfaces with different hardware performance counters and the embedded Linux Operating System through (1) the cpuquiet API to select cores at run-time; and (2) the cpufreq API to scale the frequency of active cores. Experiments with mobile and high performance applications demonstrate that the proposed approach achieves an average 22% (7-40%) power reduction compared to existing techniques.