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dc.contributor.advisor 이성진 -
dc.contributor.author Sejin Kim -
dc.date.accessioned 2022-03-07T16:00:39Z -
dc.date.available 2022-03-07T16:00:39Z -
dc.date.issued 2022 -
dc.identifier.uri http://dgist.dcollection.net/common/orgView/200000594560 en_US
dc.identifier.uri http://hdl.handle.net/20.500.11750/16324 -
dc.description Near-data processing, Machine learning, Embedding, System architecture -
dc.description.statementofresponsibility N -
dc.description.tableofcontents I. Introduction 1
II. Background 3
2.1 Embedding Layer 3
2.2 Recommender System 4
III. Motivation 6
IV. Design of SEMS 9
4.1 Overall Architecture 9
4.2 Near-data Processing 10
4.3 Developing Applications 12
4.4 Interface 12
4.5 Model Parallelism 15
4.6 Optimization 18
V. Evaluation 20
5.1 Methodology 20
5.2 Experimental Results 21
VI. Related Work 26
VII. Conclusion 28
References 29
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dc.format.extent 33 -
dc.language eng -
dc.publisher DGIST -
dc.subject Near-data processing, Machine learning, Embedding, System architecture -
dc.title SEMS: Scalable Embedding Memory System Exploiting Near-data Processing -
dc.type Thesis -
dc.identifier.doi 10.22677/thesis.200000594560 -
dc.description.degree Master -
dc.contributor.department Information and Communication Engineering -
dc.contributor.coadvisor Jaeha Kung -
dc.date.awarded 2022/02 -
dc.publisher.location Daegu -
dc.description.database dCollection -
dc.citation XT.IM 김54 202202 -
dc.date.accepted 1/21/22 -
dc.contributor.alternativeDepartment 정보통신융합전공 -
dc.embargo.liftdate 20270228 -
dc.contributor.affiliatedAuthor Sejin Kim -
dc.contributor.affiliatedAuthor Sungjin Lee -
dc.contributor.affiliatedAuthor Jaeha Kung -
dc.contributor.alternativeName 김세진 -
dc.contributor.alternativeName Sungjin Lee -
dc.contributor.alternativeName 궁재하 -
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Department of Electrical Engineering and Computer Science Theses Master

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