OSCNN accuracy with different feature extractors.

<div><p>Neuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation. As an alternative, optical implemen...

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Bibliographic Details
Main Author:	Reyhane Ahmadi (14857363) (author)
Other Authors:	Amirreza Ahmadnejad (14857366) (author), Somayyeh Koohi (9993090) (author)
Published:	2024
Subjects:	Neuroscience Biotechnology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified utilize highly efficient typically consume 150 spiking neural networks resistant coding schemes propose advanced micro novel approach inspired exhibited notable success error correction algorithms enhance signal integrity effective feature extraction custom integrated circuits comparative analysis reveals based spiking nature advanced packaging techniques optical neuromorphic engineering conventional electronic ai minimize power consumption utilizes optical components quality optical components maintaining high accuracy inspired computational systems enhance power efficiency oscnn leverages free electronic components positioning systems hardware efficiency optical implementations electronic converters computational principles computational model power operation thermal dissipation temporal processing space optics scale precision rigorously tested promising avenue processing speeds processing capabilities pioneering free pattern detection oscnn consumes oscnn ), onn types intrinsic information initial layer including mnist human eye human brain explored within ensure nanometer employ high developing brain delay conversion component alignment competing favorably benchmark datasets analog computing adaptive optics 6 w 5 ms 44 ms 300 w
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OSCNN accuracy with different feature extractors.

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