A Silicon Neuron-based Bio-Front-End for Ultra Low Power Bio-Monitoring at the Edge

Shivangi, TP; Rahimi, M; Gargiulo, G; Kailath, BJ; Hamilton, TJ

A Silicon Neuron-based Bio-Front-End for Ultra Low Power Bio-Monitoring at the Edge

Shivangi, TP Rahimi, M Gargiulo, G Kailath, BJ Hamilton, TJ

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2021, 00, pp. 3043-3048
Issue Date:: 2021-01-05

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Field	Value	Language
dc.contributor.author	Shivangi, TP
dc.contributor.author	Rahimi, M
dc.contributor.author	Gargiulo, G
dc.contributor.author	Kailath, BJ
dc.contributor.author	Hamilton, TJ
dc.date	2020-12-01
dc.date.accessioned	2022-06-06T03:20:20Z
dc.date.available	2022-06-06T03:20:20Z
dc.date.issued	2021-01-05
dc.identifier.citation	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2021, 00, pp. 3043-3048
dc.identifier.isbn	9781728125473
dc.identifier.uri	http://hdl.handle.net/10453/157963
dc.description.abstract	This paper presents the circuits for an edge-based bio-front-end implemented using an integrate-and-fire silicon neuron model in 22nm SOI CMOS Technology. The proposed implementation encodes both positive and negative input signals separately and, like its biological counterpart, provides asynchronous output. This asynchronous output allows for maximum sensitivity to high-information content input signals and low sensitivity for low-information content. In the proposed design, the firing rate can be controlled by an adaptation circuit to achieve maximum power savings. We demonstrate this design with a sinusoidal test signal and pre-recorded ECG signals. The proposed design achieves ultra-low-power consumption; by applying a sinusoidal input and ECG input the power consumption without adaptation (with adaptation) is 4. 069SnW(3.999nW) and 5. 1529nW (3.311SnW), respectively. In addition, the reconstruction of the ECG signal is demonstrated and the signal to error for the reconstructed ECG signal is 30.2 dB.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
dc.relation.ispartof	IEEE Symposium Series on Computational Intelligence
dc.relation.isbasedon	10.1109/SSCI47803.2020.9308445
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Silicon Neuron-based Bio-Front-End for Ultra Low Power Bio-Monitoring at the Edge
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Australia
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-06T03:20:18Z
pubs.finish-date	2020-12-04
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2020-12-01
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

This paper presents the circuits for an edge-based bio-front-end implemented using an integrate-and-fire silicon neuron model in 22nm SOI CMOS Technology. The proposed implementation encodes both positive and negative input signals separately and, like its biological counterpart, provides asynchronous output. This asynchronous output allows for maximum sensitivity to high-information content input signals and low sensitivity for low-information content. In the proposed design, the firing rate can be controlled by an adaptation circuit to achieve maximum power savings. We demonstrate this design with a sinusoidal test signal and pre-recorded ECG signals. The proposed design achieves ultra-low-power consumption; by applying a sinusoidal input and ECG input the power consumption without adaptation (with adaptation) is 4. 069SnW(3.999nW) and 5. 1529nW (3.311SnW), respectively. In addition, the reconstruction of the ECG signal is demonstrated and the signal to error for the reconstructed ECG signal is 30.2 dB.

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http://hdl.handle.net/10453/157963