Large-scale neuromorphic computing hardware for analog AI enabled by epitaxial random access memory
Name
1142635152-MIT.pdf
Size
1.52 MB
Format
Adobe PDF
Checksum (MD5)
3fd05963ad4b46b776189f3542aa441c
Author(s)
Choi, Chanyeol.
Advisor(s)
Jeehwan Kim.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
A neuromorphic computing on memristor-based crossbars is one of promising next generation analog computing methods since it features fast switching speed, extremely small cell footprint, low energy consumption for matrix-vector multiplication, capability of both storage and computing, three-dimensionality, and many analog weight steps. Although there have been intensive studies on the development of an analog memristive device and its large-scale crossbar to implement neuromorphic hardware systems for deep neural networks, only limited approaches, such as inference task, were suggested due to spatial/temporal variations and nonlinear/step-limited weight update properties. In order to address those issues, this thesis presents epitaxial random access memory and relevant techniques at material-, device-, array-, architecture-, algorithm-level. The proposed methods have great potential to improve device performance and relax the large-scale system-level requirements for analog AI computing.
Description
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages [47]-50).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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