Complementary metal‐oxide semiconductor and memristive hardware for neuromorphic computing

Rahimi Azghadi, Mostafa, Chen, Ying-Chen, Eshraghian, Jason K., Chen, Jia, Lin, Chih-Yang, Amirsoleimani, Amirali, Mehonic, Adnan, Kenyon, Anthony J., Fowler, Burt, Lee, Jack C., and Chang, Yao-Feng (2020) Complementary metal‐oxide semiconductor and memristive hardware for neuromorphic computing. Advanced Intelligent Systems, 2 (5). 1900189.

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The ever‐increasing processing power demands of digital computers cannot continue to be fulfilled indefinitely unless there is a paradigm shift in computing. Neuromorphic computing, which takes inspiration from the highly parallel, low‐power, high‐speed, and noise‐tolerant computing capabilities of the brain, may provide such a shift. Many researchers from across academia and industry have been studying materials, devices, circuits, and systems, to implement some of the functions of networks of neurons and synapses to develop neuromorphic computing platforms. These platforms are being designed using various hardware technologies, including the well‐established complementary metal‐oxide semiconductor (CMOS), and emerging memristive technologies such as SiOx‐based memristors. Herein, recent progress in CMOS, SiOx‐based memristive, and mixed CMOS‐memristive hardware for neuromorphic systems is highlighted. New and published results from various devices are provided that are developed to replicate selected functions of neurons, synapses, and simple spiking networks. It is shown that the CMOS and memristive devices are assembled in different neuromorphic learning platforms to perform simple cognitive tasks such as classification of spike rate‐based patterns or handwritten digits. Herein, it is envisioned that what is demonstrated is useful to the unconventional computing research community by providing insights into advances in neuromorphic hardware technologies.

Item ID: 65717
Item Type: Article (Research - C1)
ISSN: 2640-4567
Keywords: complementary metal-oxide semiconductors, memristors, neuromorphiccomputing, resistive random access memory, unconventional computing
Copyright Information: © 2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. ©2020 The Authors. Published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Funders: James Cook University (JCU), Engineering and Physical Sciences Research Council (EPSRC), Leverhulme Trust (LT)
Projects and Grants: EPSRC Grant number P/K01739X/1, LT Grant number RPG-2016-135
Date Deposited: 10 Feb 2021 00:29
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