國立虎尾科技大學 |

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

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Energy-Efficient Memristor-Based Neuromorphic Computing Circuits and Systems for Radiation Detection Applications./
Author:	Canales Verdial, Jorge Ivan.
Description:	1 online resource (167 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
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. - 1 online resource (167 pages)

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

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

Includes bibliographical references

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.

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

Mode of access: World Wide Web

ISBN: 9798380606967Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Computational dynamicsIndex Terms--Genre/Form:

554714
Electronic books.

Energy-Efficient Memristor-Based Neuromorphic Computing Circuits and Systems for Radiation Detection Applications.
LDR:02138ntm a22003977 4500 001 1141716
005 20240414211454.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798380606967
035 $a (MiAaPQ)AAI30421575
035 $a AAI30421575
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Canales Verdial, Jorge Ivan. $3 1465663
245 1 0 $a Energy-Efficient Memristor-Based Neuromorphic Computing Circuits and Systems for Radiation Detection Applications.
264 0 $c 2023
300 $a 1 online resource (167 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
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.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
650 4 $a Nuclear engineering. $3 655622
650 4 $a Nanotechnology. $3 557660
653 $a Computational dynamics
653 $a Detection applications
653 $a Memristor crossbars
653 $a Neuromorphic computing
653 $a Radionuclide
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0552
690 $a 0652
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of New Mexico. $b Engineering. $3 1465664
773 0 $t Dissertations Abstracts International $g 85-04B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30421575 $z click for full text (PQDT)