Workload- and User-aware Battery Lifetime Management for Mobile SoCs
Sofiane Chetouia and Sherief Redab
School of Engineering, Brown University, Providence, Rhode Island 02912
asofiane_chetoui@brown.edu
bsherief reda@brown.edu
ABSTRACT
Mobile devices have become an essential part of daily life with the increased computing capabilities and features. For battery powered devices, the user experience depends on both quality-of-service (QoS) and battery lifetime. Previous works have been proposed to balance QoS and battery lifetime of mobile devices; however, they often consider only the CPU. Additionally, they fail in considering the user’s desired battery lifetime while having a high QoS variation, which undermine the user satisfaction. In this work, we propose a CPU-GPU workload- and useraware battery lifetime management technique for mobile devices using machine learning. Firstly, we design a workload-aware governor through an offline and an online analysis. A set of CPU and GPU performance counters is used during the offline analysis to identify a set of canonical phases (CP). In runtime, k-means is used to classify each sample of the performance counters to one of the predefined CP. Afterwards, we build a model that predicts the energy consumption given the user usage history. Finally, the energy model is used to find the optimal frequency settings for the CPU and GPU to provide the best QoS while meeting the target battery lifetime. The evaluation of the proposed work against state of the art techniques in a commercial smartphone, shows 15.8% and 9.4% performance improvement on the CPU and GPU, respectively. The proposed technique also shows 10⨯ improvement in QoS variation, while meeting the desired battery lifetime.
Keywords: Mobile Devices, CPU, GPU, Battery Management
