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Hardware implementation of a convolutional neural network using calculations in the residue number system

Modern convolutional neural networks architectures are very resource intensive which limits the possibilities for their wide practical application. We propose a convolutional neural network architecture in which the neural network is divided into hardware and software parts to increase performance a...

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Bibliographic Details
Main Authors: Chervyakov, N. I., Червяков, Н. И., Lyakhov, P. A., Ляхов, П. А., Nagornov, N. N., Нагорнов, Н. Н., Valueva, M. V., Валуева, М. В., Valuev, G. V., Валуев, Г. В.
Format: Статья
Language:Russian
Published: Institution of Russian Academy of Sciences 2019
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Online Access:https://www.scopus.com/record/display.uri?eid=2-s2.0-85075043198&origin=resultslist&sort=plf-f&src=s&st1=Hardware+implementation+of+a+convolutional+neural+network+using+calculations+in+the+residue+number+system&st2=&sid=89f29b0d3c0c6fc7964e417a86d76dbf&sot=b&sdt=b&sl=120&s=TITLE-ABS-KEY%28Hardware+implementation+of+a+convolutional+neural+network+using+calculations+in+the+residue+number+system%29&relpos=0&citeCnt=0&searchTerm=
https://dspace.ncfu.ru/handle/20.500.12258/8604
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