NV-BNN: An accurate deep convolutional neural network based on binary STT-MRAM for adaptive ai edge

Chih Cheng Chang; Ming Hung Wu; Jia Wei Lin; Chun Hsien Li; Vivek Parmar; Heng Yuan Lee; Jeng Hua Wei; Shyh Shyuan Sheu; Manan Suri; Tian-Sheuan Chang; Tuo-Hung Hou

doi:10.1145/3316781.3317872

NV-BNN: An accurate deep convolutional neural network based on binary STT-MRAM for adaptive ai edge

Chih Cheng Chang, Ming Hung Wu, Jia Wei Lin, Chun Hsien Li, Vivek Parmar, Heng Yuan Lee, Jeng Hua Wei, Shyh Shyuan Sheu, Manan Suri, Tian-Sheuan Chang, Tuo-Hung Hou

研究成果: Conference contribution › 同行評審

11 引文斯高帕斯（Scopus）

摘要

Binary STT-MRAM is a highly anticipated embedded nonvolatile memory technology in advanced logic nodes < 28 nm. How to enable its in-memory computing (IMC) capability is critical for enhancing AI Edge. Based on the soon-available STTMRAM, we report the first binary deep convolutional neural network (NV-BNN) capable of both local and remote learning. Exploiting intrinsic cumulative switching probability, accurate online training of CIFAR-10 color images (∼ 90%) is realized using a relaxed endurance spec (switching ≤ 20 times) and hybrid digital/IMC design. For offline training, the accuracy loss due to imprecise weight placement can be mitigated using a rapid noniterative training-with-noise and fine-tuning scheme.

原文	English
主出版物標題	Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019
發行者	Institute of Electrical and Electronics Engineers Inc.
ISBN（電子）	9781450367257
DOIs	https://doi.org/10.1145/3316781.3317872
出版狀態	Published - 2 6月 2019
事件	56th Annual Design Automation Conference, DAC 2019 - Las Vegas, United States 持續時間: 2 6月 2019 → 6 6月 2019

出版系列

名字	Proceedings - Design Automation Conference
ISSN（列印）	0738-100X

Conference

Conference	56th Annual Design Automation Conference, DAC 2019
國家/地區	United States
城市	Las Vegas
期間	2/06/19 → 6/06/19

存取文件

10.1145/3316781.3317872

其它文件與鏈接

Link to publication in Scopus

引用此

Chang, C. C., Wu, M. H., Lin, J. W., Li, C. H., Parmar, V., Lee, H. Y., Wei, J. H., Sheu, S. S., Suri, M., Chang, T-S., & Hou, T-H. (2019). NV-BNN: An accurate deep convolutional neural network based on binary STT-MRAM for adaptive ai edge. 於 Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019 Article a30 (Proceedings - Design Automation Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1145/3316781.3317872

@inproceedings{37cc255131c0441a8852e2f3c3aefb19,

title = "NV-BNN: An accurate deep convolutional neural network based on binary STT-MRAM for adaptive ai edge",

abstract = "Binary STT-MRAM is a highly anticipated embedded nonvolatile memory technology in advanced logic nodes < 28 nm. How to enable its in-memory computing (IMC) capability is critical for enhancing AI Edge. Based on the soon-available STTMRAM, we report the first binary deep convolutional neural network (NV-BNN) capable of both local and remote learning. Exploiting intrinsic cumulative switching probability, accurate online training of CIFAR-10 color images (∼ 90%) is realized using a relaxed endurance spec (switching ≤ 20 times) and hybrid digital/IMC design. For offline training, the accuracy loss due to imprecise weight placement can be mitigated using a rapid noniterative training-with-noise and fine-tuning scheme.",

author = "Chang, {Chih Cheng} and Wu, {Ming Hung} and Lin, {Jia Wei} and Li, {Chun Hsien} and Vivek Parmar and Lee, {Heng Yuan} and Wei, {Jeng Hua} and Sheu, {Shyh Shyuan} and Manan Suri and Tian-Sheuan Chang and Tuo-Hung Hou",

year = "2019",

month = jun,

day = "2",

doi = "10.1145/3316781.3317872",

language = "English",

series = "Proceedings - Design Automation Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019",

address = "United States",

note = "56th Annual Design Automation Conference, DAC 2019 ; Conference date: 02-06-2019 Through 06-06-2019",

}

Chang, CC, Wu, MH, Lin, JW, Li, CH, Parmar, V, Lee, HY, Wei, JH, Sheu, SS, Suri, M, Chang, T-S & Hou, T-H 2019, NV-BNN: An accurate deep convolutional neural network based on binary STT-MRAM for adaptive ai edge. 於 Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019., a30, Proceedings - Design Automation Conference, Institute of Electrical and Electronics Engineers Inc., 56th Annual Design Automation Conference, DAC 2019, Las Vegas, United States, 2/06/19. https://doi.org/10.1145/3316781.3317872

NV-BNN: An accurate deep convolutional neural network based on binary STT-MRAM for adaptive ai edge. / Chang, Chih Cheng; Wu, Ming Hung; Lin, Jia Wei 等.
Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. a30 (Proceedings - Design Automation Conference).

研究成果: Conference contribution › 同行評審

TY - GEN

T1 - NV-BNN

T2 - 56th Annual Design Automation Conference, DAC 2019

AU - Chang, Chih Cheng

AU - Wu, Ming Hung

AU - Lin, Jia Wei

AU - Li, Chun Hsien

AU - Parmar, Vivek

AU - Lee, Heng Yuan

AU - Wei, Jeng Hua

AU - Sheu, Shyh Shyuan

AU - Suri, Manan

AU - Chang, Tian-Sheuan

AU - Hou, Tuo-Hung

PY - 2019/6/2

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N2 - Binary STT-MRAM is a highly anticipated embedded nonvolatile memory technology in advanced logic nodes < 28 nm. How to enable its in-memory computing (IMC) capability is critical for enhancing AI Edge. Based on the soon-available STTMRAM, we report the first binary deep convolutional neural network (NV-BNN) capable of both local and remote learning. Exploiting intrinsic cumulative switching probability, accurate online training of CIFAR-10 color images (∼ 90%) is realized using a relaxed endurance spec (switching ≤ 20 times) and hybrid digital/IMC design. For offline training, the accuracy loss due to imprecise weight placement can be mitigated using a rapid noniterative training-with-noise and fine-tuning scheme.

AB - Binary STT-MRAM is a highly anticipated embedded nonvolatile memory technology in advanced logic nodes < 28 nm. How to enable its in-memory computing (IMC) capability is critical for enhancing AI Edge. Based on the soon-available STTMRAM, we report the first binary deep convolutional neural network (NV-BNN) capable of both local and remote learning. Exploiting intrinsic cumulative switching probability, accurate online training of CIFAR-10 color images (∼ 90%) is realized using a relaxed endurance spec (switching ≤ 20 times) and hybrid digital/IMC design. For offline training, the accuracy loss due to imprecise weight placement can be mitigated using a rapid noniterative training-with-noise and fine-tuning scheme.

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M3 - Conference contribution

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BT - Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Chang CC, Wu MH, Lin JW, Li CH, Parmar V, Lee HY 等. NV-BNN: An accurate deep convolutional neural network based on binary STT-MRAM for adaptive ai edge. 於 Proceedings of the 56th Annual Design Automation Conference 2019, DAC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. a30. (Proceedings - Design Automation Conference). doi: 10.1145/3316781.3317872

NV-BNN: An accurate deep convolutional neural network based on binary STT-MRAM for adaptive ai edge

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