A skyrmion racetrack memory based computing in-memory architecture for binary neural convolutional network

Yu Pan; Peng Ouyang; Yinglin Zhao; Shouyi Yin; Youguang Zhang; Shaojun Wei; Weisheng Zhao

doi:10.1145/3299874.3318015

A skyrmion racetrack memory based computing in-memory architecture for binary neural convolutional network

Yu Pan
, Peng Ouyang^*
, Yinglin Zhao
, Shouyi Yin
, Youguang Zhang
, Shaojun Wei
, Weisheng Zhao

^*Corresponding author for this work

Beihang University
Tsinghua University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

A Skyrmion Racetrack Memory (SRM) based Computing In-Memory Architecture (SRM-CIM) was proposed in this paper. Both data and computing operation can be achieved in SRM-CIM. SRM-CIM is used to support convolutional computing in Binary Convolutional Neural Network (BCNN). Experimental results show that SRM-CIM achieves 98.7% and 82% energy reduction when compared with RRAM and SOT-MRAM based counterparts.

Original language	English
Title of host publication	GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI
Publisher	Association for Computing Machinery
Pages	271-274
Number of pages	4
ISBN (Electronic)	9781450362528
DOIs	https://doi.org/10.1145/3299874.3318015
State	Published - 13 May 2019
Event	29th Great Lakes Symposium on VLSI, GLSVLSI 2019 - Tysons Corner, United States Duration: 9 May 2019 → 11 May 2019

Publication series

Name	Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Conference

Conference	29th Great Lakes Symposium on VLSI, GLSVLSI 2019
Country/Territory	United States
City	Tysons Corner
Period	9/05/19 → 11/05/19

Keywords

Bcnn
Computing in memory
Low power
Skyrmion

Access to Document

10.1145/3299874.3318015

Cite this

Pan, Y., Ouyang, P., Zhao, Y., Yin, S., Zhang, Y., Wei, S., & Zhao, W. (2019). A skyrmion racetrack memory based computing in-memory architecture for binary neural convolutional network. In GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI (pp. 271-274). Article 3318015 (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI). Association for Computing Machinery . https://doi.org/10.1145/3299874.3318015

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title = "A skyrmion racetrack memory based computing in-memory architecture for binary neural convolutional network",

abstract = "A Skyrmion Racetrack Memory (SRM) based Computing In-Memory Architecture (SRM-CIM) was proposed in this paper. Both data and computing operation can be achieved in SRM-CIM. SRM-CIM is used to support convolutional computing in Binary Convolutional Neural Network (BCNN). Experimental results show that SRM-CIM achieves 98.7\% and 82\% energy reduction when compared with RRAM and SOT-MRAM based counterparts.",

keywords = "Bcnn, Computing in memory, Low power, Skyrmion",

author = "Yu Pan and Peng Ouyang and Yinglin Zhao and Shouyi Yin and Youguang Zhang and Shaojun Wei and Weisheng Zhao",

note = "Publisher Copyright: {\textcopyright} 2019 ACM.; 29th Great Lakes Symposium on VLSI, GLSVLSI 2019 ; Conference date: 09-05-2019 Through 11-05-2019",

year = "2019",

month = may,

day = "13",

doi = "10.1145/3299874.3318015",

language = "英语",

series = "Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI",

publisher = "Association for Computing Machinery ",

pages = "271--274",

booktitle = "GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI",

address = "美国",

}

Pan, Y, Ouyang, P, Zhao, Y, Yin, S, Zhang, Y, Wei, S & Zhao, W 2019, A skyrmion racetrack memory based computing in-memory architecture for binary neural convolutional network. in GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI., 3318015, Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI, Association for Computing Machinery , pp. 271-274, 29th Great Lakes Symposium on VLSI, GLSVLSI 2019, Tysons Corner, United States, 9/05/19. https://doi.org/10.1145/3299874.3318015

A skyrmion racetrack memory based computing in-memory architecture for binary neural convolutional network. / Pan, Yu; Ouyang, Peng; Zhao, Yinglin et al.
GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI. Association for Computing Machinery , 2019. p. 271-274 3318015 (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Pan, Yu

AU - Ouyang, Peng

AU - Zhao, Yinglin

AU - Yin, Shouyi

AU - Zhang, Youguang

AU - Wei, Shaojun

AU - Zhao, Weisheng

PY - 2019/5/13

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N2 - A Skyrmion Racetrack Memory (SRM) based Computing In-Memory Architecture (SRM-CIM) was proposed in this paper. Both data and computing operation can be achieved in SRM-CIM. SRM-CIM is used to support convolutional computing in Binary Convolutional Neural Network (BCNN). Experimental results show that SRM-CIM achieves 98.7% and 82% energy reduction when compared with RRAM and SOT-MRAM based counterparts.

AB - A Skyrmion Racetrack Memory (SRM) based Computing In-Memory Architecture (SRM-CIM) was proposed in this paper. Both data and computing operation can be achieved in SRM-CIM. SRM-CIM is used to support convolutional computing in Binary Convolutional Neural Network (BCNN). Experimental results show that SRM-CIM achieves 98.7% and 82% energy reduction when compared with RRAM and SOT-MRAM based counterparts.

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KW - Computing in memory

KW - Low power

KW - Skyrmion

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DO - 10.1145/3299874.3318015

M3 - 会议稿件

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T3 - Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

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T2 - 29th Great Lakes Symposium on VLSI, GLSVLSI 2019

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ER -

Pan Y, Ouyang P, Zhao Y, Yin S, Zhang Y, Wei S et al. A skyrmion racetrack memory based computing in-memory architecture for binary neural convolutional network. In GLSVLSI 2019 - Proceedings of the 2019 Great Lakes Symposium on VLSI. Association for Computing Machinery . 2019. p. 271-274. 3318015. (Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI). doi: 10.1145/3299874.3318015

A skyrmion racetrack memory based computing in-memory architecture for binary neural convolutional network

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