Adaptive extreme edge computing for wearable devices

Covi, E., Donati, E., Liang, X., Kappel, D., Heidari, H. , Payvand, M. and Wang, W. (2021) Adaptive extreme edge computing for wearable devices. Frontiers in Neuroscience, 15, 611300. (doi: 10.3389/fnins.2021.611300)

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Abstract

Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system adaptation are vital in future smart wearable devices. The visioning and forecasting of how to bring computation to the edge in smart sensors have already begun, with an aspiration to provide adaptive extreme edge computing. Here, we provide a holistic view of hardware and theoretical solutions towards smart wearable devices that can provide guidance to research in this pervasive computing era. We propose various solutions for biologically plausible models for continual learning in neuromorphic computing technologies for wearable sensors. To envision this concept, we provide a systematic outline in which prospective low power and low latency scenarios of wearable sensors in neuromorphic platforms are expected. We successively describe vital potential landscapes of neuromorphic processors exploiting complementary metal-oxide semiconductors (CMOS) and emerging memory technologies (e.g. memristive devices). Furthermore, we evaluate the requirements for edge computing within wearable devices in terms of footprint, power consumption, latency, and data size. We additionally investigate the challenges beyond neuromorphic computing hardware, algorithms and devices that could impede enhancement of adaptive edge computing in smart wearable devices.

Item Type:Articles
Additional Information:This work was partially supported by the UK EPSRC under grants EP/R511705/1 and EP/W522168/1. EC and MP acknowledge funding by the European Union's Horizon 2020 research and innovation programme under grant agreement No 871737.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Heidari, Dr Hadi and Liang, Xiangpeng
Authors: Covi, E., Donati, E., Liang, X., Kappel, D., Heidari, H., Payvand, M., and Wang, W.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:Frontiers in Neuroscience
Publisher:Frontiers Media
ISSN:1662-4548
ISSN (Online):1662-453X
First Published:First published in Frontiers in Neuroscience 15:611300

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
304896EPSRC-IAA: Early Stage Commercialisation of a PET Imaging Agent for the Detection of Cardiovascular Disease and CancerAndrew SutherlandEngineering and Physical Sciences Research Council (EPSRC)EP/R511705/1Chemistry