ArXiv Preprint
Recent breakthrough technological progressions of powerful mobile computing
resources such as low-cost mobile GPUs along with cutting-edge, open-source
software architectures have enabled high-performance deep learning on mobile
platforms. These advancements have revolutionized the capabilities of today's
mobile applications in different dimensions to perform data-driven intelligence
locally, particularly for smart health applications. Unlike traditional machine
learning (ML) architectures, modern on-device deep learning frameworks are
proficient in utilizing computing resources in mobile platforms seamlessly, in
terms of producing highly accurate results in less inference time. However, on
the flip side, energy resources in a mobile device are typically limited.
Hence, whenever a complex Deep Neural Network (DNN) architecture is fed into
the on-device deep learning framework, while it achieves high prediction
accuracy (and performance), it also urges huge energy demands during the
runtime. Therefore, managing these resources efficiently within the spectrum of
performance and energy efficiency is the newest challenge for any mobile
application featuring data-driven intelligence beyond experimental evaluations.
In this paper, first, we provide a timely review of recent advancements in
on-device deep learning while empirically evaluating the performance metrics of
current state-of-the-art ML architectures and conventional ML approaches with
the emphasis given on energy characteristics by deploying them on a smart
health application. With that, we are introducing a new framework through an
energy-aware, adaptive model comprehension and realization (EAMCR) approach
that can be utilized to make more robust and efficient inference decisions
based on the available computing/energy resources in the mobile device during
the runtime.
G. Dumindu Samaraweera, Hung Nguyen, Hadi Zanddizari, Behnam Zeinali, J. Morris Chang
2023-02-01
