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Memristor-Based In-Memory Computing Architecture for Scientific Computing

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Memristor Computing Systems

Abstract

Owing to their ability to demonstrate parallel multiplication and accumulation operations in a memristive crossbar array, memristors are well suited to in-memory analog computing architecture. In addition to neuromorphic computing that tolerates low computing precision and other non-ideal memristor characteristics, numerical analysis and scientific computing, which are widely used in many data-intensive scientific and engineering applications, require high-precision and accurate solutions and, thus, pose significant challenges for the memristive approach. In this chapter, we first briefly introduce the principle of vector–matrix multiplication operation in a memristor crossbar array. Second, the principles of memristor-based in-memory scientific computing accelerators are explained, focusing on solving systems of linear equations and partial differential equations. Accordingly, the key works in this field are reviewed. Finally, we scrutinize the challenges and opportunities at the device and system levels.

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Authors and Affiliations

  1. Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan, China

    Jiancong Li, Yi Li, Ling Yang & Xiangshui Miao

Authors
  1. Jiancong Li
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  2. Yi Li
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  3. Ling Yang
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  4. Xiangshui Miao
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Corresponding author

Correspondence to Yi Li .

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Editors and Affiliations

  1. Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkley, CA, USA

    Leon O. Chua

  2. Fundamentals of Electrical Engineering, TU Dresden, Dresden, Germany

    Ronald Tetzlaff

  3. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Angela Slavova

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Li, J., Li, Y., Yang, L., Miao, X. (2022). Memristor-Based In-Memory Computing Architecture for Scientific Computing. In: Chua, L.O., Tetzlaff, R., Slavova, A. (eds) Memristor Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-90582-8_7

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  • DOI: https://doi.org/10.1007/978-3-030-90582-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90581-1

  • Online ISBN: 978-3-030-90582-8

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