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CMOS and Memristive Hardware for Neuromorphic Computing

Rahimi Azghadi, M; Chen, Y-C; Eshraghian, JK; Chen, J; Lin, C-Y; Amirsoleimani, A; Mehonic, A; ... Chang, Y-F; + view all (2020) CMOS and Memristive Hardware for Neuromorphic Computing. Advanced Intelligent Systems , 2 (5) , Article 1900189. 10.1002/aisy.201900189. Green open access

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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.

Type: Article
Title: CMOS and Memristive Hardware for Neuromorphic Computing
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/aisy.201900189
Publisher version: https://doi.org/10.1002/aisy.201900189
Language: English
Additional information: © 2020 The Authors. Published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: complementary metal-oxide semiconductors, memristors, neuromorphic computing, resistive random access memory, unconventional computing
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10094758
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