{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/124125"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/124125","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Large-scale neuromorphic computing hardware for analog AI enabled by epitaxial random access memory","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Choi, Chanyeol."],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Jeehwan Kim."],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-22T22:21:02Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/124125"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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)."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Large-scale neuromorphic computing hardware for analog AI enabled by epitaxial random access memory"]}]}],"canonical_facts":{"dc:contributor.advisor":["Jeehwan Kim."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","EECS"],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Choi, Chanyeol."],"dc:date.accessioned":["2020-03-09T18:59:06Z"],"dc:date.available":["2020-03-09T18:59:06Z"],"dc:date.issued":["2019"],"dc: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)."],"dc:description.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."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/124125"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["MIT theses are protected by copyright. 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