An overview of neuromorphic computing for artificial intelligence enabled hardware-based hopfield neural network

Yu, Z., Abdulghani, A. M., Zahid, A., Heidari, H. , Imran, M. A. and Abbasi, Q. H. (2020) An overview of neuromorphic computing for artificial intelligence enabled hardware-based hopfield neural network. IEEE Access, 8, pp. 67085-67099. (doi: 10.1109/ACCESS.2020.2985839)

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Abstract

Compared with von Neumann’s computer architecture, neuromorphic systems offer more unique and novel solutions to the artificial intelligence discipline. Inspired by biology, this novel system has implemented the theory of human brain modeling by connecting feigned neurons and synapses to reveal the new neuroscience concepts. Many researchers have vastly invested in neuro-inspired models, algorithms, learning approaches, operation systems for the exploration of the neuromorphic system and have implemented many corresponding applications. Recently, some researchers have demonstrated the capabilities of Hopfield algorithms in some large-scale notable hardware projects and seen significant progression. This paper presents a comprehensive review and focuses extensively on the Hopfield algorithm’s model and its potential advancement in new research applications. Towards the end, we conclude with a broad discussion and a viable plan for the latest application prospects to facilitate developers with a better understanding of the aforementioned model in accordance to build their own artificial intelligence projects.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Abbasi, Dr Qammer and Imran, Professor Muhammad and Yu, Zheqi and Zahid, Mr Adnan and Heidari, Dr Hadi and Abdulghani, Dr Amir Mohamed
Authors: Yu, Z., Abdulghani, A. M., Zahid, A., Heidari, H., Imran, M. A., and Abbasi, Q. H.
College/School:College of Science and Engineering > School of Engineering
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Access
Publisher:IEEE
ISSN:2169-3536
ISSN (Online):2169-3536
Published Online:06 April 2020
Copyright Holders:Copyright © 2020 IEEE
First Published:First published in IEEE Access 8: 67085-97099
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172865EPSRC DTP 16/17 and 17/18Tania GalabovaEngineering and Physical Sciences Research Council (EPSRC)EP/N509668/1Research and Innovation Services