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In-memory computing with emerging nonvolatile memory devices

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Abstract

The von Neumann bottleneck and memory wall have posed fundamental limitations in latency and energy consumption of modern computers based on von Neumann architecture. In-memory computing represents a radical shift in the computer architecture that can address such problems by merging computing functions within the memory itself. In this article, we review the emerging nonvolatile memory devices, such as resistance-based and charge-based memory devices, that are explored for in-memory computing applications. We will provide an overview of the materials, mechanisms, and integration of these devices, and discuss the optimizations at the device and array levels that are required to better support in-memory computing. Recent progress in the application of in-memory computing in artificial neural networks, spiking neural networks, digital logic in memory as well as hardware security will also be discussed. Finally, we will discuss the remaining challenges in this field and potential pathways to address them.

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Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2017YFA0207600), National Natural Science Foundation of China (Grant Nos. 61925401, 92064004, 61927901), the Project supported by PKU-Baidu Fund (Grant Nos. 2019BD002, 2020BD010), and the 111 Project (Grant No. B18001). Yuchao YANG acknowledges the support from the Fok Ying-Tong Education Foundation, Beijing Academy of Artificial Intelligence (BAAI), and the Tencent Foundation through the XPLORER PRIZE.

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  1. Cheng C D and Tiw P J have the same contribution to this work.

Authors and Affiliations

  1. State Key Laboratory for Advanced Metals and Materials, School of Materials Science and Engineering, University of Science and Technology Beijing, Beijing, 100083, China

    Caidie Cheng & Xiaoqin Yan

  2. Key Laboratory of Microelectronic Devices and Circuits (MOE), Department of Micro/nanoelectronics, Peking University, Beijing, 100871, China

    Caidie Cheng, Pek Jun Tiw, Yimao Cai, Yuchao Yang & Ru Huang

  3. Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, Beijing, 100871, China

    Yimao Cai, Yuchao Yang & Ru Huang

  4. Center for Brain Inspired Intelligence, Chinese Institute for Brain Research (CIBR), Beijing, Beijing, 102206, China

    Yuchao Yang & Ru Huang

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  1. Caidie Cheng
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Correspondence to Xiaoqin Yan, Yuchao Yang or Ru Huang.

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Cheng, C., Tiw, P.J., Cai, Y. et al. In-memory computing with emerging nonvolatile memory devices. Sci. China Inf. Sci. 64, 221402 (2021). https://doi.org/10.1007/s11432-021-3327-7

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