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Dynamical memristors for higher-complexity neuromorphic computing

Nature Reviews Materials volume 7, pages 575–591 (2022)Cite this article

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Abstract

Research on electronic devices and materials is currently driven by both the slowing down of transistor scaling and the exponential growth of computing needs, which make present digital computing increasingly capacity-limited and power-limited. A promising alternative approach consists in performing computing based on intrinsic device dynamics, such that each device functionally replaces elaborate digital circuits, leading to adaptive ‘complex computing’. Memristors are a class of devices that naturally embody higher-order dynamics through their internal electrophysical processes. In this Review, we discuss how novel material properties enable complex dynamics and define different orders of complexity in memristor devices and systems. These native complex dynamics at the device level enable new computing architectures, such as brain-inspired neuromorphic systems, which offer both high energy efficiency and high computing capacity.

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Fig. 1: Biological and computational adaptation and complexity.
Fig. 2: Concept of memristor and memristive behaviours of various complexity.
Fig. 3: Examples of memristors of different orders of complexity.
Fig. 4: Emulation of higher-order complexity using multiple circuit elements.
Fig. 5: Computing with memristors of different orders of complexity.
Fig. 6: Computing based on spatio-temporal complexity of memristors.
Fig. 7: Accessing non-equilibrium phases.
Fig. 8: Comparison between the future requirements and potential of the main computing technologies.

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Acknowledgements

The authors gratefully acknowledge S. M. Bohaichuk and R. S. Williams for feedback on the manuscript. S.K. was supported by the Laboratory Directed Research and Development programme at Sandia National Laboratories, a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Y.Y. acknowledges support from the National Key Research and Development Program of China (2017YFA0207600), National Natural Science Foundation of China (61925401, 92064004), projects 2019BD002 and 2020BD010 supported by PKU-Baidu Fund, the Fok Ying-Tong Education Foundation, Beijing Academy of Artificial Intelligence (BAAI) and the Tencent Foundation through the XPLORER PRIZE. X.W. and W.D.L. acknowledge financial support from the National Science Foundation through awards CCF-1900675 and DMR-1810119.

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Authors and Affiliations

  1. Sandia National Laboratories, Livermore, CA, USA

    Suhas Kumar

  2. Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor, MI, USA

    Xinxin Wang & Wei D. Lu

  3. Peter Grünberg Institute (PGI-14), Forschungszentrum Jülich GmbH, Jülich, Germany

    John Paul Strachan

  4. RWTH Aachen University, Aachen, Germany

    John Paul Strachan

  5. School of Integrated Circuits, Center for Brain Inspired Chips, Institute for Artificial Intelligence, Peking University, Beijing, China

    Yuchao Yang

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

    Yuchao Yang

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All the authors contributed to the conception of the ideas, literature review, writing of the manuscript, preparation of the figures and editing.

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Kumar, S., Wang, X., Strachan, J.P. et al. Dynamical memristors for higher-complexity neuromorphic computing. Nat Rev Mater 7, 575–591 (2022). https://doi.org/10.1038/s41578-022-00434-z

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