MobDL: A Framework for Profiling Deep Learning Models: A Case Study using Mobile Digital Health Applications

Abstract

Smart mobile devices coupled with the Internet of Things (IoT) and Artificial Intelligence (AI) have emerged as a key enabler of modern digital health applications. While cloud computing is now a well established paradigm for analysing IoT captured data in mobile health applications, on-board analysis of data using AI approaches such as Deep Learning (DL) is gaining significant momentum. This is driven primarily by advances in on-board resources enabling modern mobile devices to execute complex DL models, while also offering improved response time and accuracy for rapid decision-making, and enhanced user privacy. While the number of mobile digital health applications that use IoT and DL is increasing, progress is currently impeded by a lack of framework for profiling and evaluating the performance of DL models on mobile devices. To this end, we propose MobDL, a framework for profiling and evaluating DL models running on smart mobile devices. We present the architecture of this framework and devise a novel evaluation methodology for conducting quantitative comparisons of various DL models running on mobile devices. Three diverse digital health applications using heterogeneous data (e.g. image, time series) are introduced. We conduct extensive experimental evaluations using several DL models that have been developed using the data sets obtained for the three digital health applications to validate the effectiveness of the proposed MobDL framework.