MemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems

Large-scale computational problems that need to be addressed in modern computers, such as deep learning or big data analysis, cannot be solved in a single computer, but can be solved with distributed computer systems. Since most distributed computing systems, consisting of a large number of networke...

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Autores principales:	Yongseok Choi, Eunji Lim, Jaekwon Shin, Cheol-Hoon Lee
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	distributed system shared memory deep learning big data FPGA ASIC Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/b92e0307e18440e6a8a3705ccf35bbbf
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id	oai:doaj.org-article:b92e0307e18440e6a8a3705ccf35bbbf
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spelling	oai:doaj.org-article:b92e0307e18440e6a8a3705ccf35bbbf2021-11-11T15:42:45ZMemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems10.3390/electronics102127202079-9292https://doaj.org/article/b92e0307e18440e6a8a3705ccf35bbbf2021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2720https://doaj.org/toc/2079-9292Large-scale computational problems that need to be addressed in modern computers, such as deep learning or big data analysis, cannot be solved in a single computer, but can be solved with distributed computer systems. Since most distributed computing systems, consisting of a large number of networked computers, should propagate their computational results to each other, they can suffer the problem of an increasing overhead, resulting in lower computational efficiencies. To solve these problems, we proposed an architecture of a distributed system that used a shared memory that is simultaneously accessible by multiple computers. Our architecture aimed to be implemented in FPGA or ASIC. Using an FPGA board that implemented our architecture, we configured the actual distributed system and showed the feasibility of our system. We compared the results of the deep learning application test using our architecture with that using Google Tensorflow’s parameter server mechanism. We showed improvements in our architecture beyond Google Tensorflow’s parameter server mechanism and we determined the future direction of research by deriving the expected problems.Yongseok ChoiEunji LimJaekwon ShinCheol-Hoon LeeMDPI AGarticledistributed systemshared memorydeep learningbig dataFPGAASICElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2720, p 2720 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	distributed system shared memory deep learning big data FPGA ASIC Electronics TK7800-8360
spellingShingle	distributed system shared memory deep learning big data FPGA ASIC Electronics TK7800-8360 Yongseok Choi Eunji Lim Jaekwon Shin Cheol-Hoon Lee MemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems
description	Large-scale computational problems that need to be addressed in modern computers, such as deep learning or big data analysis, cannot be solved in a single computer, but can be solved with distributed computer systems. Since most distributed computing systems, consisting of a large number of networked computers, should propagate their computational results to each other, they can suffer the problem of an increasing overhead, resulting in lower computational efficiencies. To solve these problems, we proposed an architecture of a distributed system that used a shared memory that is simultaneously accessible by multiple computers. Our architecture aimed to be implemented in FPGA or ASIC. Using an FPGA board that implemented our architecture, we configured the actual distributed system and showed the feasibility of our system. We compared the results of the deep learning application test using our architecture with that using Google Tensorflow’s parameter server mechanism. We showed improvements in our architecture beyond Google Tensorflow’s parameter server mechanism and we determined the future direction of research by deriving the expected problems.
format	article
author	Yongseok Choi Eunji Lim Jaekwon Shin Cheol-Hoon Lee
author_facet	Yongseok Choi Eunji Lim Jaekwon Shin Cheol-Hoon Lee
author_sort	Yongseok Choi
title	MemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems
title_short	MemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems
title_full	MemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems
title_fullStr	MemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems
title_full_unstemmed	MemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems
title_sort	membox: shared memory device for memory-centric computing applicable to deep learning problems
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b92e0307e18440e6a8a3705ccf35bbbf
work_keys_str_mv	AT yongseokchoi memboxsharedmemorydeviceformemorycentriccomputingapplicabletodeeplearningproblems AT eunjilim memboxsharedmemorydeviceformemorycentriccomputingapplicabletodeeplearningproblems AT jaekwonshin memboxsharedmemorydeviceformemorycentriccomputingapplicabletodeeplearningproblems AT cheolhoonlee memboxsharedmemorydeviceformemorycentriccomputingapplicabletodeeplearningproblems
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MemBox: Shared Memory Device for Memory-Centric Computing Applicable to Deep Learning Problems

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