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RESISTIVE MEMORY

Efficient AI with MRAM

Nature Electronics volume 5, pages 67–68 (2022)Cite this article

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In-memory computing chips based on magnetoresistive random-access memory devices can provide energy-efficient hardware for machine learning tasks.

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Fig. 1: In-memory computing based on resistive memories.

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Authors and Affiliations

  1. Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Qiming Shao

  2. Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China

    Zhongrui Wang

  3. Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

    J. Joshua Yang

Authors
  1. Qiming Shao
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  2. Zhongrui Wang
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  3. J. Joshua Yang
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Corresponding authors

Correspondence to Zhongrui Wang or J. Joshua Yang.

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The authors declare no competing interests.

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Shao, Q., Wang, Z. & Yang, J.J. Efficient AI with MRAM. Nat Electron 5, 67–68 (2022). https://doi.org/10.1038/s41928-022-00725-x

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