Search Results - "Valueva, M.V."

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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Single Image Super-Resolution Method Based on Bilinear Interpolation and U-Net Combination by Lyakhov, P.A., Valuev, G.V., Valueva, M.V., Kaplun, D.I., Sinitca, A.M.

“…The single image super-resolution issue is studied in this paper. We propose a new method based on a combination of bilinear interpolation and the U-Net neural…”
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A New Method of Sign Detection in RNS Based on Modified Chinese Remainder Theorem by Lyakhov, P.A., Valueva, M.V., Kaplun, D.I., Voznesensky, A.S.

“…A sign detection is a non-modular operation in the residue number system (RNS), that requires calculating a position characteristic of the number represented…”
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A New Method of Cleaning Video from Impulse Noise by Chervyakov, N.I., Lyakhov, P.A., Orazaev, A.R., Valueva, M.V.

“…The paper proposes a new method of impulse noise filtering for video data processing. The method is based on the combined use of iterative processing and…”
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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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