Towards Energy-Efficient Mobile Sensing: Architectures and Frameworks for Heterogeneous Sensing and Computing

Abstract

Modern sensing apps require continuous and intense computation on data streams. Unfortunately, mobile devices are failing to keep pace despite advances in hardware capability. In contrast to powerful system-on-chips that rapidly evolve, battery capacities merely grow. This hinders the potential of long-running, compute-intensive sensing services such as image/audio processing, motion tracking and health monitoring, especially on small, wearable devices. In this thesis, we present three pieces of work that target at improving the energy efficiency for mobile sensing. (1) In the first work, we study heterogeneous mobile processors that dynamically switch between high-performance and low-power cores according to tasks' performance requirements. We benchmark interactive mobile workloads and quantify the energy improvement of different microarchitectures. (2) Realizing that today's users often carry more than one mobile devices, in the second work, we extend the resource boundary of individual devices by prototyping a distributed framework that coordinates multiple devices. When devices share common sensing goals, the framework schedules sensing and computing tasks according to devices' heterogeneity, improving the performance and latency for compute-intensive sensing apps. (3) In the third work, we study the power breakdown of motion sensing apps on wearable devices and show that traditional offloading schemes cannot mitigate sensing’s high energy costs. We design a framework that allows the phone to take over sensing and computation by predicting the wearable's sensory data, when motions of the two devices are highly correlated. This allows the wearable to offload without communicating raw sensing data, resulting in little performance loss but significant energy savings.