SmartCap: Using Machine Learning for Power Adaptation of Smartphone's Application Processor

摘要

Power efficiency is increasingly critical to battery-powered smartphones. Given that the using experience is most valued by the user, we propose that the power optimization should directly respect the user experience. We conduct a statistical sample survey and study the correlation among the user experience, system runtime activities, and computational performance of an application processor. We find that there exists a minimal frequency requirement, called "saturated frequency". Above this frequency, the device consumes more power but provides little improvements in user experience. This study motivates an intelligent self-adaptive scheme, SmartCap, that automatically identifies the most power-efficient state of the application processor. Compared to prior Linux power adaptation schemes, SmartCap can help save power from 11% to 84%, depending on applications, with little decline in user experience.