In-memory computing with emerging nonvolatile memory devices

查看全文 作　　者：Caidie [1,2]CHENG;Pek Jun [2]TIW;Yimao [2,3]CAI;Xiaoqin [1]YAN;Yuchao [2,3,4]YANG;Ru [2,3,4]HUANG

机构地区：[1]State Key Laboratory for Advanced Metals and Materials,School of Materials Science and Engineering,University of Science and Technology Beijing,Beijing 100083,China;[2]Key Laboratory of Microelectronic Devices and Circuits(MOE'),Department of Micro/nanoelectronics,Peking University,Beijing 100871,China;[3]Center for Brain Inspired Chips,Institute for Artificial Intelligence,Peking University,Beijing 100871,China;[4]Center for Brain Inspired Intelligence,Chinese Institute for Brain Research(CIBR),Beijing,Beijing 102206,China

出　　处：《Science China(Information Sciences)》索引2021年第64卷第12期,共46页

基　　金：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);the 111 Project (Grant No. B18001) the support from the Fok Ying-Tong Education Foundation;Beijing Academy of Artificial Intelligence (BAAI);the Tencent Foundation through the XPLORER PRIZE。 摘　　要：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. 关键词：in-memory computing von Neumann bottleneck nonvolatile memory energy efficiency neural network

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In-memory computing with emerging nonvolatile memory devices

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