Search Results - "Nagornov, N.N."

Application of the residue number system to reduce hardware costs of the convolutional neural network implementation by Valueva, M.V., Nagornov, N.N., Lyakhov, P.A., Valuev, G.V., Chervyakov, N.I.

“…Convolutional neural networks are a promising tool for solving the problem of pattern recognition. Most well-known convolutional neural networks…”
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High-performance digital image filtering architectures in the residue number system based on the Winograd method by Valueva, M.V., Lyakhov, P.A., Nagornov, N.N., Valuev, G.V.

“…Continuous improvement of methods for visual information registration, processing and storage leads to the need of improving technical characteristics of…”
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Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity by Lyakhov, P.A., Nagornov, N.N., Semyonova, N.F., Abdulsalyamova, A.S.

“…The fast increase of the amount of quantitative and qualitative characteristics of digital visual data calls for the improvement of the performance of modern…”
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Residue Number System-Based Solution for Reducing the Hardware Cost of a Convolutional Neural Network by Chervyakov, N.I., Lyakhov, P.A., Deryabin, M.A., Nagornov, N.N., Valueva, M.V., Valuev, G.V.

“…Convolutional neural networks (CNNs) represent deep learning architectures that are currently used in a wide range of applications, including computer vision,…”
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Hardware implementation of a convolutional neural network using calculations in the residue number system by Chervyakov, N.I., Lyakhov, P.A., Nagornov, N.N., Valueva, M.V., Valuev, G.V.

“…Modern convolutional neural networks architectures are very resource intensive which limits the possibilities for their wide practical application. We propose…”
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