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dc.contributor.author
Covi, Erika
dc.contributor.author
Donati, Elisa
dc.contributor.author
Liang, Xiangpeng
dc.contributor.author
Kappel, David
dc.contributor.author
Heidari, Hadi
dc.contributor.author
Payvand, Melika
dc.contributor.author
Wang, Wei
dc.date.accessioned
2021-06-16T15:56:46Z
dc.date.available
2021-06-11T02:26:12Z
dc.date.available
2021-06-16T15:56:46Z
dc.date.issued
2021-05
dc.identifier.issn
1662-453X
dc.identifier.issn
1662-4548
dc.identifier.other
10.3389/fnins.2021.611300
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/489163
dc.identifier.doi
10.3929/ethz-b-000489163
dc.description.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 toward 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.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Frontiers Research Foundation
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
neuromorphic computing
en_US
dc.subject
edge computing
en_US
dc.subject
wearable devices
en_US
dc.subject
learning algorithms
en_US
dc.subject
memristive devices
en_US
dc.title
Adaptive Extreme Edge Computing for Wearable Devices
en_US
dc.type
Review Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-05-11
ethz.journal.title
Frontiers in Neuroscience
ethz.journal.volume
15
en_US
ethz.journal.abbreviated
Front Neurosci
ethz.pages.start
611300
en_US
ethz.size
27 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.scopus
ethz.publication.place
Lausanne
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-06-11T02:26:16Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-06-16T15:56:53Z
ethz.rosetta.lastUpdated
2021-06-16T15:56:53Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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