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Flow-Based Computing on Memristor Crossbars and Its Application in Computer Vision for Edge Detection.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Flow-Based Computing on Memristor Crossbars and Its Application in Computer Vision for Edge Detection./
Author:	Pannu, Jodh Singh.
Description:	1 online resource (67 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-11, Section: B.
Contained By:	Dissertations Abstracts International85-11B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798382610276

Flow-Based Computing on Memristor Crossbars and Its Application in Computer Vision for Edge Detection.
Pannu, Jodh Singh.

Flow-Based Computing on Memristor Crossbars and Its Application in Computer Vision for Edge Detection. - 1 online resource (67 pages)

Source: Dissertations Abstracts International, Volume: 85-11, Section: B.

Thesis (Ph.D.)--The University of Texas at San Antonio, 2024.

Includes bibliographical references

The end of Dennard scaling and the approaching saturation of transistor density for memory devices has resulted in the need for alternate methods of computation. We explore the fabrication, design and use of in-memory computation architecture, namely, memristor nanoscale crossbars. Memristors are non-volatile two terminal devices. They are essentially programmable variable resistors that retain the programmed resistance values even after the device loses its power. This property is used to create memristor crossbar circuits that can perform in-memory computation on the data stored in these crossbars. Flow-based computing has been developed to make use of the unique architecture of memristor crossbars.In this thesis we answer the following questions:• How can flow-based computing be used to implement Boolean formulas on memristor cross-bars.• How do we synthesis memristor crossbar circuits for more specialised computation problems, for example edge detection in images.• Providing a proof of concept by fabricating real memristor crossbar circuits.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382610276Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Computer visionIndex Terms--Genre/Form:

554714
Electronic books.

Flow-Based Computing on Memristor Crossbars and Its Application in Computer Vision for Edge Detection.
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502 $a Thesis (Ph.D.)--The University of Texas at San Antonio, 2024.
504 $a Includes bibliographical references
520 $a The end of Dennard scaling and the approaching saturation of transistor density for memory devices has resulted in the need for alternate methods of computation. We explore the fabrication, design and use of in-memory computation architecture, namely, memristor nanoscale crossbars. Memristors are non-volatile two terminal devices. They are essentially programmable variable resistors that retain the programmed resistance values even after the device loses its power. This property is used to create memristor crossbar circuits that can perform in-memory computation on the data stored in these crossbars. Flow-based computing has been developed to make use of the unique architecture of memristor crossbars.In this thesis we answer the following questions:• How can flow-based computing be used to implement Boolean formulas on memristor cross-bars.• How do we synthesis memristor crossbar circuits for more specialised computation problems, for example edge detection in images.• Providing a proof of concept by fabricating real memristor crossbar circuits.
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653 $a Emerging architecture
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