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Open AccessReview
A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms
by
Seham Al Abdul Wahid
Seham Al Abdul Wahid *,†,
Arghavan Asad
Arghavan Asad † and
Farah Mohammadi
Farah Mohammadi †
Electrical and Computer Engineering Department, Toronto Metropolitan University, 350 Victoria St, Toronto, ON M5B 2K3, Canada
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Electronics 2024, 13(15), 2963; https://doi.org/10.3390/electronics13152963 (registering DOI)
Submission received: 30 June 2024
/
Revised: 13 July 2024
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Accepted: 24 July 2024
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Published: 26 July 2024
Abstract
Neuromorphic computing, a brain-inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimising it for large-scale applications remains a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin–Huxley Model, Izhikevich model, integrate-and-fire model, and spike response model. Hardware implementation platforms of the chip follow three approaches: analogue, digital, or a combination of both. Each platform can be implemented using various memory topologies which interconnect with the learning mechanism. Current neuromorphic computing systems typically use the unsupervised learning spike timing-dependent plasticity algorithms. However, algorithms such as voltage-dependent synaptic plasticity have the potential to enhance performance. This review summarises the potential neuromorphic chip architecture specifications and highlights which applications they are suitable for.
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MDPI and ACS Style
Al Abdul Wahid, S.; Asad, A.; Mohammadi, F.
A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms. Electronics 2024, 13, 2963.
https://doi.org/10.3390/electronics13152963
AMA Style
Al Abdul Wahid S, Asad A, Mohammadi F.
A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms. Electronics. 2024; 13(15):2963.
https://doi.org/10.3390/electronics13152963
Chicago/Turabian Style
Al Abdul Wahid, Seham, Arghavan Asad, and Farah Mohammadi.
2024. "A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms" Electronics 13, no. 15: 2963.
https://doi.org/10.3390/electronics13152963
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