TY - JOUR
T1 - An overview of neuromorphic computing for artificial intelligence enabled hardware-based hopfield neural network
AU - Yu, Zheqi
AU - Abdulghani, Amir M.
AU - Zahid, Adnan
AU - Heidari, Hadi
AU - Imran, Muhammad Ali
AU - Abbasi, Qammer H.
N1 - Funding Information:
The work of Zheqi Yu was supported by the Joint Industrial Scholarship Between the University of Glasgow and Transreport Ltd., London. The work of Adnan Zahid was supported by the Engineering and Physical Sciences Research Council (EPSRC), Doctoral Training Grant (DTG) under Grant EPSRC DTG EP/N509668/1 Eng.
Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - Hopfield algorithm
KW - neuro-inspired model
KW - Neuromorphic computing
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U2 - 10.1109/ACCESS.2020.2985839
DO - 10.1109/ACCESS.2020.2985839
M3 - Article
AN - SCOPUS:85084109442
SN - 2169-3536
VL - 8
SP - 67085
EP - 67099
JO - IEEE Access
JF - IEEE Access
M1 - 9057570
ER -