High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network (Adv. Sci. 13/2022) | Zendy

Zhang Kun | Zendy; Jia Xiaotao | Zendy; Cao Kaihua | Zendy; Wang Jinkai | Zendy; Zhang Yue | Zendy; Lin Kelian | Zendy; Chen Lei | Zendy; Feng Xueqiang | Zendy; Zheng Zhenyi | Zendy; Zhang Zhizhong | Zendy; Zhang Youguang | Zendy; Zhao Weisheng | Zendy

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High On/Off Ratio Spintronic Multi‐Level Memory Unit for Deep Neural Network (Adv. Sci. 13/2022)

Author(s) -

Zhang Kun,

Jia Xiaotao,

Cao Kaihua,

Wang Jinkai,

Zhang Yue,

Lin Kelian,

Chen Lei,

Feng Xueqiang,

Zheng Zhenyi,

Zhang Zhizhong,

Zhang Youguang,

Zhao Weisheng

Publication year - 2022

Publication title -

advanced science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 5.388

H-Index - 100

ISSN - 2198-3844

DOI - 10.1002/advs.202270086

Subject(s) - spintronics , artificial neural network , computer science , unit (ring theory) , deep learning , artificial intelligence , physics , ferromagnetism , psychology , mathematics education , quantum mechanics

Spintronic Multi‐Level Memory Unit In article number 2103357 by Kun Zhang, Yue Zhang, and co‐workers, a spintronic multi‐level memory unit (MLMU) with high on/off ratio is constructed by integrating a magnetic tunnel junction chain and a diode in parallel. This MLMU can enable high‐accuracy artificial intelligence applications, such as a deep neural network accelerator for image classification. Just as described in the cover design, the MLMUs are regarded as building blocks to assemble the artificial‐intelligence brain.

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