Code Offloading Solutions for Audio Processing in Mobile Healthcare Applications: A Case Study

Abstract

In this paper, we present a real-life case study of a mobile healthcare application that leverages code offloading techniques to accelerate the execution of a complex deep neural network algorithm for analyzing audio samples. Resource-intensive machine learning tasks take a significant time to complete on high-end devices, while lower-end devices may outright crash when attempting to run them. In our experiments, offloading granted the former a 3.6x performance improvement, and up to 80% reduction in energy consumption; while the latter gained the capability of running a process they originally could not.