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Title: | Theoretical Analysis of the Convolutional Neural Networks Acceleration by Organizing Calculations According to the Winograd Method |
Authors: | Abdulsalyamova, A. S. Абдулсалямова, А. Ш. Lyakhov, P. A. Ляхов, П. А. Kalita, D. I. Калита, Д. И. |
Keywords: | Computational complexity;Digital filtering;Winograd method;Speeding up convolutional neural networks;Convolutional neural networks |
Issue Date: | 2022 |
Citation: | Abdulsalyamova, A.S., Lyakhov, P.A., Kalita, D.I. Theoretical Analysis of the Convolutional Neural Networks Acceleration by Organizing Calculations According to the Winograd Method // Proceedings of the 2022 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS . - 2022. - 2022, pp. 58-61. - DOI: 10.1109/ITQMIS56172.2022.9976703 |
Series/Report no.: | Proceedings of the 2022 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS |
Abstract: | Convolutional neural networks are promising tool for pattern recognition. Implementations of convolutional neural networks require a significant number of computations during training and processing. In this paper we propose to use Winograd method to reduce computational complexity in convolutional layers of convolutional neural networks. Theoretical analysis of the convolutional layer’s computational complexity of convolutional neural networks showed that the use of the Winograd method can reduce the computational complexity compared with direct method by 26,75%, 17,01%, with filter sizes of 3×3, 5×5, respectively. A promising area of further research is the hardware implementation of convolutional layers with Winograd calculations on modern computing devices, such as FPGA and ASIC. |
URI: | http://hdl.handle.net/20.500.12258/22737 |
Appears in Collections: | Статьи, проиндексированные в SCOPUS, WOS |
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