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Application of the residue number system to reduce hardware costs of the convolutional neural network implementation

Application of the residue number system to reduce hardware costs of the convolutional neural network implementation

Convolutional neural networks are a promising tool for solving the problem of pattern recognition. Most well-known convolutional neural networks implementations require a significant amount of memory to store weights in the process of learning and working. We propose a convolutional neural network a...

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Auteurs principaux: Valueva, M. V., Валуева, М. В., Nagornov, N. N., Нагорнов, Н. Н., Lyakhov, P. A., Ляхов, П. А., Valuev, G. V., Валуев, Г. В., Chervyakov, N. I., Червяков, Н. И.
Format: Статья
Langue:English
Publié: Elsevier B.V. 2020
Sujets:
Convolutional neural networks
Field-programmable gate array (FPGA)
Image processing
Quantization noise
Residue number system (RNS)
Accès en ligne:https://dspace.ncfu.ru/handle/20.500.12258/12120
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https://dspace.ncfu.ru/handle/20.500.12258/12120

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