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

Zheqi Yu, Amir M. Abdulghani, Adnan Zahid, Hadi Heidari, Muhammad Ali Imran, Qammer H. Abbasi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
4 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)67085-67099
Number of pages15
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 6 Apr 2020

Keywords

  • artificial intelligence
  • Hopfield algorithm
  • neuro-inspired model
  • Neuromorphic computing

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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