SEMS: Scalable Embedding Memory System Exploiting Near-data Processing
- Title
- SEMS: Scalable Embedding Memory System Exploiting Near-data Processing
- Author(s)
- Sejin Kim
- DGIST Authors
- Sejin Kim ; Sungjin Lee ; Jaeha Kung
- Advisor
- 이성진
- Co-Advisor(s)
- Jaeha Kung
- Issued Date
- 2022
- Awarded Date
- 2022/02
- Type
- Thesis
- Description
- Near-data processing, Machine learning, Embedding, System architecture
- Table Of Contents
-
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
- URI
-
http://dgist.dcollection.net/common/orgView/200000594560
http://hdl.handle.net/20.500.11750/16324
- Degree
- Master
- Department
- Information and Communication Engineering
- Publisher
- DGIST
- Related Researcher
-
-
Lee, Sungjin
- Research Interests Computer System; System Software; Storage System; Non-volatile Memory; Flash-based SSD; Distributed Storage Systems
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- Files in This Item:
-
There are no files associated with this item.
- Appears in Collections:
- Department of Electrical Engineering and Computer Science Theses Master
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