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Research Progress on Memristor: From Synapses to Computing Systems

Publication ,  Journal Article
Yang, X; Taylor, B; Wu, A; Chen, Y; Chua, LO
Published in: IEEE Transactions on Circuits and Systems I: Regular Papers
May 1, 2022

As the limits of transistor technology are approached, feature size in integrated circuit transistors has been reduced very near to the minimum physically-realizable channel length, and it has become increasingly difficult to meet expectations outlined by Moore's law. As one of the most promising devices to replace transistors, memristors have many excellent properties that can be leveraged to develop new types of neural and non-von Neumann computing systems, which are expected to revolutionize information-processing technology. This survey provides a comparative overview of research progress on memristors. Different memristor synaptic devices are classified according to stimulation patterns and the working mechanisms of these various synaptic devices are analyzed in detail. Crossbar-based memristors have demonstrated advantages in physically executing vector-matrix multiplication and enabling highly power-efficient and area-efficient neuromorphic system designs. The extensive uses of crossbar-based memristors cover in-memory logic, vector-matrix multiplication, and many other fundamental computing operations. Furthermore, memristor-based architectures for efficient neural network training and inference have been studied. However, memristors have non-ideal properties due to programming inaccuracies and device imperfections from fabrication, which lead to error or mismatch in computed results. To build reliable memristor-based designs, circuit-level, algorithm-level, and system-level solutions to memristor reliability issues are being studied. To this end, state-of-the-art realizations of memristor crossbars, crossbar-based designs, and peripheral circuitry are presented, which show both promising full-system inference accuracy and excellent power efficiency in multiple tasks. Memristor in-situ learning benefits from high energy efficiency and biologically-imitative characteristics, which are conducive to further realizing hardware acceleration of cognitive learning. At present, the learning and training processes of brain-like networks are complex, presenting great challenges for network design and implementation.

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Published In

IEEE Transactions on Circuits and Systems I: Regular Papers

DOI

EISSN

1558-0806

ISSN

1549-8328

Publication Date

May 1, 2022

Volume

69

Issue

5

Start / End Page

1845 / 1857

Related Subject Headings

  • Electrical & Electronic Engineering
  • 4009 Electronics, sensors and digital hardware
  • 0906 Electrical and Electronic Engineering
 

Citation

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Yang, X., Taylor, B., Wu, A., Chen, Y., & Chua, L. O. (2022). Research Progress on Memristor: From Synapses to Computing Systems. IEEE Transactions on Circuits and Systems I: Regular Papers, 69(5), 1845–1857. https://doi.org/10.1109/TCSI.2022.3159153
Yang, X., B. Taylor, A. Wu, Y. Chen, and L. O. Chua. “Research Progress on Memristor: From Synapses to Computing Systems.” IEEE Transactions on Circuits and Systems I: Regular Papers 69, no. 5 (May 1, 2022): 1845–57. https://doi.org/10.1109/TCSI.2022.3159153.
Yang X, Taylor B, Wu A, Chen Y, Chua LO. Research Progress on Memristor: From Synapses to Computing Systems. IEEE Transactions on Circuits and Systems I: Regular Papers. 2022 May 1;69(5):1845–57.
Yang, X., et al. “Research Progress on Memristor: From Synapses to Computing Systems.” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 69, no. 5, May 2022, pp. 1845–57. Scopus, doi:10.1109/TCSI.2022.3159153.
Yang X, Taylor B, Wu A, Chen Y, Chua LO. Research Progress on Memristor: From Synapses to Computing Systems. IEEE Transactions on Circuits and Systems I: Regular Papers. 2022 May 1;69(5):1845–1857.

Published In

IEEE Transactions on Circuits and Systems I: Regular Papers

DOI

EISSN

1558-0806

ISSN

1549-8328

Publication Date

May 1, 2022

Volume

69

Issue

5

Start / End Page

1845 / 1857

Related Subject Headings

  • Electrical & Electronic Engineering
  • 4009 Electronics, sensors and digital hardware
  • 0906 Electrical and Electronic Engineering