Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory

Hao Yu; Yuhao Wang; Shuai Chen; Wei Fei; Chuliang Weng; Junfeng Zhao; Zhulin Wei

doi:10.1109/ASPDAC.2014.6742888

Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory

Hao Yu^*
, Yuhao Wang
, Shuai Chen
, Wei Fei
, Chuliang Weng
, Junfeng Zhao
, Zhulin Wei

^*此作品的通讯作者

Nanyang Technological University
Huawei Shannon Laboratory

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

28 引用（Scopus）

摘要

Image processing in conventional logic-memory I/O-integrated systems will incur significant communication congestion at memory I/Os for excessive big image data at exa-scale. This paper explores an in-memory machine learning on neural network architecture by utilizing the newly introduced domain-wall nanowire, called DW-NN. We show that all operations involved in machine learning on neural network can be mapped to a logic-in-memory architecture by non-volatile domain-wall nanowire. Domain-wall nanowire based logic is customized for in machine learning within image data storage. As such, both neural network training and processing can be performed locally within the memory. The experimental results show that system throughput in DW-NN is improved by 11.6x and the energy efficiency is improved by 92x when compared to conventional image processing system.

源语言	英语
主期刊名	2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014 - Proceedings
页	191-196
页数	6
DOI	https://doi.org/10.1109/ASPDAC.2014.6742888
出版状态	已出版 - 2014
已对外发布	是
活动	2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014 - Suntec, 新加坡期限: 20 1月 2014 → 23 1月 2014

出版系列

姓名	Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

会议

会议	2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014
国家/地区	新加坡
市	Suntec
时期	20/01/14 → 23/01/14

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

可持续发展目标 7 经济适用的清洁能源

访问文件

10.1109/ASPDAC.2014.6742888

其它文件与链接

链接到 Scopus 的出版物

引用此

Yu, H., Wang, Y., Chen, S., Fei, W., Weng, C., Zhao, J., & Wei, Z. (2014). Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory. 在 2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014 - Proceedings (页码 191-196). 文章 6742888 (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC). https://doi.org/10.1109/ASPDAC.2014.6742888

@inproceedings{6dc93cfbaffa4bda818319ac3245725a,

title = "Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory",

abstract = "Image processing in conventional logic-memory I/O-integrated systems will incur significant communication congestion at memory I/Os for excessive big image data at exa-scale. This paper explores an in-memory machine learning on neural network architecture by utilizing the newly introduced domain-wall nanowire, called DW-NN. We show that all operations involved in machine learning on neural network can be mapped to a logic-in-memory architecture by non-volatile domain-wall nanowire. Domain-wall nanowire based logic is customized for in machine learning within image data storage. As such, both neural network training and processing can be performed locally within the memory. The experimental results show that system throughput in DW-NN is improved by 11.6x and the energy efficiency is improved by 92x when compared to conventional image processing system.",

author = "Hao Yu and Yuhao Wang and Shuai Chen and Wei Fei and Chuliang Weng and Junfeng Zhao and Zhulin Wei",

year = "2014",

doi = "10.1109/ASPDAC.2014.6742888",

language = "英语",

isbn = "9781479928163",

series = "Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC",

pages = "191--196",

booktitle = "2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014 - Proceedings",

note = "2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014 ; Conference date: 20-01-2014 Through 23-01-2014",

}

Yu, H, Wang, Y, Chen, S, Fei, W, Weng, C, Zhao, J & Wei, Z 2014, Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory. 在 2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014 - Proceedings., 6742888, Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC, 页码 191-196, 2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014, Suntec, 新加坡, 20/01/14. https://doi.org/10.1109/ASPDAC.2014.6742888

Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory. / Yu, Hao; Wang, Yuhao; Chen, Shuai 等.
2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014 - Proceedings. 2014. 页码 191-196 6742888 (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory

AU - Yu, Hao

AU - Wang, Yuhao

AU - Chen, Shuai

AU - Fei, Wei

AU - Weng, Chuliang

AU - Zhao, Junfeng

AU - Wei, Zhulin

PY - 2014

Y1 - 2014

N2 - Image processing in conventional logic-memory I/O-integrated systems will incur significant communication congestion at memory I/Os for excessive big image data at exa-scale. This paper explores an in-memory machine learning on neural network architecture by utilizing the newly introduced domain-wall nanowire, called DW-NN. We show that all operations involved in machine learning on neural network can be mapped to a logic-in-memory architecture by non-volatile domain-wall nanowire. Domain-wall nanowire based logic is customized for in machine learning within image data storage. As such, both neural network training and processing can be performed locally within the memory. The experimental results show that system throughput in DW-NN is improved by 11.6x and the energy efficiency is improved by 92x when compared to conventional image processing system.

AB - Image processing in conventional logic-memory I/O-integrated systems will incur significant communication congestion at memory I/Os for excessive big image data at exa-scale. This paper explores an in-memory machine learning on neural network architecture by utilizing the newly introduced domain-wall nanowire, called DW-NN. We show that all operations involved in machine learning on neural network can be mapped to a logic-in-memory architecture by non-volatile domain-wall nanowire. Domain-wall nanowire based logic is customized for in machine learning within image data storage. As such, both neural network training and processing can be performed locally within the memory. The experimental results show that system throughput in DW-NN is improved by 11.6x and the energy efficiency is improved by 92x when compared to conventional image processing system.

UR - https://www.scopus.com/pages/publications/84897875079

U2 - 10.1109/ASPDAC.2014.6742888

DO - 10.1109/ASPDAC.2014.6742888

M3 - 会议稿件

AN - SCOPUS:84897875079

SN - 9781479928163

T3 - Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

SP - 191

EP - 196

BT - 2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014 - Proceedings

T2 - 2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014

Y2 - 20 January 2014 through 23 January 2014

ER -

Yu H, Wang Y, Chen S, Fei W, Weng C, Zhao J 等. Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory. 在 2014 19th Asia and South Pacific Design Automation Conference, ASP-DAC 2014 - Proceedings. 2014. 页码 191-196. 6742888. (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC). doi: 10.1109/ASPDAC.2014.6742888

Energy efficient in-memory machine learning for data intensive image-processing by non-volatile domain-wall memory

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此