Theoretical Analysis of the Convolutional Neural Networks Acceleration by Organizing Calculations According to the Winograd Method

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.