國立虎尾科技大學 |

Characterization and Optimization of ReRAM-Based Analog Crossbar Arrays for Neuromorphic Computing.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Characterization and Optimization of ReRAM-Based Analog Crossbar Arrays for Neuromorphic Computing./
Author:	Short, Jesse.
Description:	1 online resource (72 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798381161809

Characterization and Optimization of ReRAM-Based Analog Crossbar Arrays for Neuromorphic Computing.
Short, Jesse.

Characterization and Optimization of ReRAM-Based Analog Crossbar Arrays for Neuromorphic Computing. - 1 online resource (72 pages)

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.S.)--Arizona State University, 2023.

Includes bibliographical references

Machine learning advancements have led to increasingly complex algorithms, resulting in significant energy consumption due to heightened memory-transfer requirements and inefficient vector matrix multiplication (VMM). To address this issue, many have proposed ReRAM analog in-memory computing (AIMC) as a solution. AIMC enhances the time-energy efficiency of VMM operations beyond conventional VMM digital hardware, such as a tensor processing unit (TPU), while substantially reducing memory-transfer demands through in-memory computing. As AIMC gains prominence as a solution, it becomes crucial to optimize ReRAM and analog crossbar architecture characteristics.This thesis introduces an application-specific integrated circuit (ASIC) tailored for characterizing ReRAM within a crossbar array architecture and discusses the interfacing techniques employed. It discusses ReRAM forming and programming techniques and showcases chip's ability to utilize the write-verify programming method to write image pixels on a conductance heat map. Additionally, this thesis assesses the ASIC's capability to characterize different aspects of ReRAM, including drift and noise characteristics. The research employs the chip to extract ReRAM data and models it within a crossbar array simulator, enabling its application in the classification of the CIFAR-10 dataset.

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

Mode of access: World Wide Web

ISBN: 9798381161809Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Tensor processing unitIndex Terms--Genre/Form:

554714
Electronic books.

Characterization and Optimization of ReRAM-Based Analog Crossbar Arrays for Neuromorphic Computing.
LDR:02717ntm a22003857 4500 001 1141798
005 20240414211521.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381161809
035 $a (MiAaPQ)AAI30695243
035 $a AAI30695243
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Short, Jesse. $3 1465785
245 1 0 $a Characterization and Optimization of ReRAM-Based Analog Crossbar Arrays for Neuromorphic Computing.
264 0 $c 2023
300 $a 1 online resource (72 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: Masters Abstracts International, Volume: 85-06.
500 $a Advisor: Marinella, Matthew.
502 $a Thesis (M.S.)--Arizona State University, 2023.
504 $a Includes bibliographical references
520 $a Machine learning advancements have led to increasingly complex algorithms, resulting in significant energy consumption due to heightened memory-transfer requirements and inefficient vector matrix multiplication (VMM). To address this issue, many have proposed ReRAM analog in-memory computing (AIMC) as a solution. AIMC enhances the time-energy efficiency of VMM operations beyond conventional VMM digital hardware, such as a tensor processing unit (TPU), while substantially reducing memory-transfer demands through in-memory computing. As AIMC gains prominence as a solution, it becomes crucial to optimize ReRAM and analog crossbar architecture characteristics.This thesis introduces an application-specific integrated circuit (ASIC) tailored for characterizing ReRAM within a crossbar array architecture and discusses the interfacing techniques employed. It discusses ReRAM forming and programming techniques and showcases chip's ability to utilize the write-verify programming method to write image pixels on a conductance heat map. Additionally, this thesis assesses the ASIC's capability to characterize different aspects of ReRAM, including drift and noise characteristics. The research employs the chip to extract ReRAM data and models it within a crossbar array simulator, enabling its application in the classification of the CIFAR-10 dataset.
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 Energy. $3 784773
653 $a Tensor processing unit
653 $a Vector matrix multiplication
653 $a Energy consumption
653 $a Machine learning
655 7 $a Electronic books. $2 local $3 554714
690 $a 0544
690 $a 0800
690 $a 0791
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Arizona State University. $b Electrical Engineering. $3 845389
773 0 $t Masters Abstracts International $g 85-06.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30695243 $z click for full text (PQDT)