Guru Prasad Srinivasa, University at Buffalo
David Werner, Tufts University
Mark Hempstead, Tufts University
Geoffrey Challen, University at Illinois
IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), March 2021
Abstract
Heat dissipation and battery life continue to be major challenges for smartphones. Smartphones seldom spend time at their highest performance points due to thermal concerns and frequently undergo thermal-throttling, where performance is limited while the device cools. While overclocking and computational sprinting can be used to increase system performance, these techniques have not been evaluated on smartphones because they exacerbate both heat dissipation and battery life. In recent years, certain machine-learning workloads such as object detection and speech recognition have been moving away from the cloud and towards the edge. These workloads are short and user-facing making them excellent candidates for sprinting. To successfully overclock any workload however, any applied technique must ensure that it avoids forcing the system to throttle. In this paper, we describe and evaluate a system that accurately predicts the impact workloads have on the thermal state of a smartphone. Thus, the system can determine whether overclocking a specific workload will result in thermal-throttling. We show that our system’s careful application of overclocking can decrease the latency of certain user-facing workloads by up to 18%.