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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Veröffentlicht in:	Mathematics and computers in simulation Jg. 177; S. 232 - 243
Hauptverfasser:	Valueva, M.V., Nagornov, N.N., Lyakhov, P.A., Valuev, G.V., Chervyakov, N.I.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.11.2020
Schlagworte:	Convolutional neural networks Field-programmable gate array (FPGA) Image processing Quantization noise Residue number system Quantization noise Residue number system Image processing Field-programmable gate array (FPGA) Convolutional neural networks
ISSN:	0378-4754, 1872-7166
Online-Zugang:	Volltext
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