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, Professor Qammer and Imran, Professor Muhammad and Yu, Zheqi and Zahid, Mr Adnan and Heidari, Professor 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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