An Efficient and Flexible Learning Framework for Dynamic Power and Thermal Co-Management

Abstract

At the era of Artificial Intelligence and Internet of Things (AIoT), battery-powered mobile devices are required to perform more sophisticated tasks featured with fast varying workloads and constrained power supply, demanding more efficient run-time power management. In this paper, we propose a deep reinforcement learning framework for dynamic power and thermal co-management. We build several machine learning models that incorporate the physical details for an ARM Cortex-A72, with on average 3% and 1% error for power and temperature predictions, respectively. We then build an efficient deep reinforcement learning control incorporating the machine learning models and facilitating the run-time dynamic voltage and frequency scaling (DVFS) strategy selection based on the predicted power, workloads and temperature. We evaluate our proposed framework, and compare the performance with existing management methods. The results suggest that our proposed framework can achieve 6.8% performance improvement compared with other alternatives. CCS CONCEPTS • Computing methodologies → Modeling and simulation; • Hardware → Chip-level power issues; • Computer systems organization → Embedded systems.