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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Characterization and Optimization of ReRAM-Based Analog Crossbar Arrays for Neuromorphic Computing./
作者:	Short, Jesse.
面頁冊數:	1 online resource (72 pages)
附註:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
標題:	Energy. -
電子資源:	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:

784773
Energy.
Subjects--Index Terms:

Tensor processing unitIndex Terms--Genre/Form:

554714
Electronic books.

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