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Adaptive Extreme Edge Computing for Wearable Devices

Erika Covi; Elisa Donati; Xiangpeng Liang; David Kappel; Hadi Heidari; Melika Payvand; Wei Wang;

Adaptive Extreme Edge Computing for Wearable Devices

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.

Frontiers in Neuroscience, 15

ISSN:1662-4548

ISSN:1662-453X

Countries
Switzerland, United Kingdom
Subjects by Vocabulary

Microsoft Academic Graph classification: Wearable computer Ubiquitous computing Wearable technology business.industry business Computer science Latency (engineering) Neuromorphic engineering Adaptation (computer science) Edge computing Enhanced Data Rates for GSM Evolution Computer architecture

Keywords

neuromorphic computing, edge computing, wearable devices, learning algorithms, memristive devices, General Neuroscience, 570 Life sciences; biology, 10194 Institute of Neuroinformatics, 2800 General Neuroscience, Emerging Technologies (cs.ET), FOS: Computer and information sciences, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571, neuromorphic computing; edge computing; wearable devices; learning algorithms; memristive devices, Computer Science - Emerging Technologies, Neuroscience, Review

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10. Davies, M. et al. Loihi: A neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82-99 (2018).

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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EC| BeFerroSynaptic
Project
BeFerroSynaptic
BEOL technology platform based on ferroelectric synaptic devices for advanced neuromorphic processors
  • Funder: European Commission (EC)
  • Project Code: 871737
  • Funding stream: H2020 | RIA
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