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Energy-Efficient Memristor-Based Neu...

Canales Verdial, Jorge Ivan.

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Energy-Efficient Memristor-Based Neuromorphic Computing Circuits and Systems for Radiation Detection Applications.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Energy-Efficient Memristor-Based Neuromorphic Computing Circuits and Systems for Radiation Detection Applications./
Author:	Canales Verdial, Jorge Ivan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	167 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
Subject:	Electrical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30421575
ISBN:	9798380606967

Energy-Efficient Memristor-Based Neuromorphic Computing Circuits and Systems for Radiation Detection Applications.
Canales Verdial, Jorge Ivan.

Energy-Efficient Memristor-Based Neuromorphic Computing Circuits and Systems for Radiation Detection Applications. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 167 p.

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--The University of New Mexico, 2023.

This item must not be sold to any third party vendors.

Radionuclide spectroscopic sensor data is analyzed with minimal power consumption through the use of neuromorphic computing architectures. Memristor crossbars are harnessed as the computational substrate in this non-conventional computing platform and integrated with CMOS-based neurons to mimic the computational dynamics observed in the mammalian brain's visual cortex. Functional prototypes using spiking sparse locally competitive approximations are presented. The architectures are evaluated for classification accuracy and energy efficiency. The proposed systems achieve a 90% true positive accuracy with a high-resolution detector and 86% with a low-resolution detector.

ISBN: 9798380606967Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Computational dynamics

Energy-Efficient Memristor-Based Neuromorphic Computing Circuits and Systems for Radiation Detection Applications.
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245 1 0 $a Energy-Efficient Memristor-Based Neuromorphic Computing Circuits and Systems for Radiation Detection Applications.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 167 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
500 $a Advisor: Zarkesh-Ha, Payman;Figueroa Toro, Miguel.
502 $a Thesis (Ph.D.)--The University of New Mexico, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Radionuclide spectroscopic sensor data is analyzed with minimal power consumption through the use of neuromorphic computing architectures. Memristor crossbars are harnessed as the computational substrate in this non-conventional computing platform and integrated with CMOS-based neurons to mimic the computational dynamics observed in the mammalian brain's visual cortex. Functional prototypes using spiking sparse locally competitive approximations are presented. The architectures are evaluated for classification accuracy and energy efficiency. The proposed systems achieve a 90% true positive accuracy with a high-resolution detector and 86% with a low-resolution detector.
590 $a School code: 0142.
650 4 $a Electrical engineering. $3 649834
650 4 $a Nuclear engineering. $3 595435
650 4 $a Nanotechnology. $3 526235
653 $a Computational dynamics
653 $a Detection applications
653 $a Memristor crossbars
653 $a Neuromorphic computing
653 $a Radionuclide
690 $a 0544
690 $a 0552
690 $a 0652
710 2 $a The University of New Mexico. $b Engineering. $3 3551847
773 0 $t Dissertations Abstracts International $g 85-04B.
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791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30421575