DeepPower: Fast and scalable energy assessment of mobile sensing applications: Poster abstract

Abstract

Energy-efficiency is a key performance metric of mobile sensing applications. However, assessment of energy-efficiency is greatly limited in practice. The main difficulty is that it requires assessment of power consumption in various user's real-life situation in the long term. This poster presents DeepPower, a system for assessing energy-efficiency of mobile sensing applications in fast and scalable manner. DeepPower introduces a sensor trace-based power use prediction technique, which significantly reduces the cost of assessing power consumption compared to existing power emulation techniques. Our experiments with three mobile sensing applications and five 1-hour-long sensor traces show that DeepPower can predict hardware usage of 1-hour-long sensor traces in less than a second, achieving average error rate of 4.6%.