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Residue number system-based solution for reducing the hardware cost of a convolutional neural network

Residue number system-based solution for reducing the hardware cost of a convolutional neural network

Convolutional neural networks (CNNs) represent deep learning architectures that are currently used in a wide range of applications, including computer vision, speech recognition, time series analysis in finance, and many others. At the same time, CNNs are very demanding in terms of the hardware and...

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書誌詳細
主要な著者: Chervyakov, N. I., Червяков, Н. И., Lyakhov, P. A., Ляхов, П. А., Deryabin, M. A., Дерябин, М. А., Nagornov, N. N., Нагорнов, Н. Н., Valueva, M. V., Валуева, М. В., Valuev, G. V., Валуев, Г. В.
フォーマット: Статья
言語:English
出版事項: Elsevier B.V. 2020
主題:
Convolutional neural networks
FPGA
Image recognition
Quantization noise
Residue number system (RNS)
オンライン･アクセス:https://dspace.ncfu.ru/handle/20.500.12258/12151
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https://dspace.ncfu.ru/handle/20.500.12258/12151

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