Energy-Efficient Ferroelectric Field-Effect Transistor-Based Oscillators for Neuromorphic System Design

Eslahi, H; Hamilton, TJ; Khandelwal, S

Energy-Efficient Ferroelectric Field-Effect Transistor-Based Oscillators for Neuromorphic System Design

Eslahi, H Hamilton, TJ Khandelwal, S

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2020, 6, (2), pp. 122-129
Issue Date:: 2020-12-01

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Field	Value	Language
dc.contributor.author	Eslahi, H
dc.contributor.author	Hamilton, TJ
dc.contributor.author	Khandelwal, S
dc.date.accessioned	2021-04-14T06:14:06Z
dc.date.available	2021-04-14T06:14:06Z
dc.date.issued	2020-12-01
dc.identifier.citation	IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 2020, 6, (2), pp. 122-129
dc.identifier.issn	2329-9231
dc.identifier.issn	2329-9231
dc.identifier.uri	http://hdl.handle.net/10453/148097
dc.description.abstract	Neuromorphic or bioinspired computational platforms, as an alternative for von-Neumann structures, have benefitted from the excellent features of emerging technologies in order to emulate the behavior of the biological brain in an accurate and energy-efficient way. Integrability with CMOS technology and low power consumption make ferroelectric field-effect transistor (FEFET) an attractive candidate to perform such paradigms, particularly for image processing. In this article, we use the FEFET device to make energy-efficient oscillatory neurons as the main parts of neural networks for image processing applications, especially for edge detection. Based on our simulation results, we estimated a significant energy efficiency compared with other technologies, which shows roughly 5-120\times reduction, depending on the design.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
dc.relation.isbasedon	10.1109/JXCDC.2020.3027541
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Energy-Efficient Ferroelectric Field-Effect Transistor-Based Oscillators for Neuromorphic System Design
dc.type	Journal Article
utslib.citation.volume	6
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	open_access	*
pubs.consider-herdc	true
dc.date.updated	2021-04-14T06:14:04Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	6
utslib.citation.issue	2

Abstract:

Neuromorphic or bioinspired computational platforms, as an alternative for von-Neumann structures, have benefitted from the excellent features of emerging technologies in order to emulate the behavior of the biological brain in an accurate and energy-efficient way. Integrability with CMOS technology and low power consumption make ferroelectric field-effect transistor (FEFET) an attractive candidate to perform such paradigms, particularly for image processing. In this article, we use the FEFET device to make energy-efficient oscillatory neurons as the main parts of neural networks for image processing applications, especially for edge detection. Based on our simulation results, we estimated a significant energy efficiency compared with other technologies, which shows roughly 5-120\times reduction, depending on the design.

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http://hdl.handle.net/10453/148097