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Recent breakthrough technological progressions of powerful mobile computingresources such as low-cost mobile GPUs along with cutting-edge, open-sourcesoftware architectures have enabled high-performance deep learning on mobileplatforms. These advancements have revolutionized the capabilities of today'smobile applications in different dimensions to perform data-driven intelligencelocally, particularly for smart health applications. Unlike traditional machinelearning (ML) architectures, modern on-device deep learning frameworks areproficient in utilizing computing resources in mobile platforms seamlessly, interms of producing highly accurate results in less inference time. However, onthe flip side, energy resources in a mobile device are typically limited.Hence, whenever a complex Deep Neural Network (DNN) architecture is fed intothe on-device deep learning framework, while it achieves high predictionaccuracy (and performance), it also urges huge energy demands during theruntime. Therefore, managing these resources efficiently within the spectrum ofperformance and energy efficiency is the newest challenge for any mobileapplication featuring data-driven intelligence beyond experimental evaluations.In this paper, first, we provide a timely review of recent advancements inon-device deep learning while empirically evaluating the performance metrics ofcurrent state-of-the-art ML architectures and conventional ML approaches withthe emphasis given on energy characteristics by deploying them on a smarthealth application. With that, we are introducing a new framework through anenergy-aware, adaptive model comprehension and realization (EAMCR) approachthat can be utilized to make more robust and efficient inference decisionsbased on the available computing/energy resources in the mobile device duringthe runtime.

Towards Implementing Energy-aware Data-driven Intelligence for Smart Health Applications on Mobile Platforms