SHEEO: Continuous Energy Efficiency Optimization in Autonomous Embedded Systems

Xinkai Wang, Chao Li, Lingyu Sun, Qizheng Lyu, Xiaofeng Hou, Jingwen Leng, Minyi Guo

Abstract: The emerging trend of autonomous embedded systems minimizing human intervention has raised new questions about continuously maximizing system energy efficiency faced with stochastic runtime variance, which is costly for resource-constrained autonomous embedded systems. Considering heterogeneous hardware and variable software, we envision opportunities for vertical and horizontal shadow cycles within the AES pipeline for management facilities. This paper introduces SHEEO, a continuous energy efficiency optimizer that exploits underutilized heterogeneous computing resources to pursue variability-aware power management. To achieve this, SHEEO constantly monitors inner and outer variances and customizes reinforcement learning into two phases for stochastic runtime variance. We implement and deploy SHEEO on a commercial edge platform. The evaluation results show that SHEEO harvests up to 88% shadow cycles and improves up to 39% energy efficiency compared to state-of-the-art power management techniques with negligible overheads.