Neural network convolution operation device and method

Abstract

The invention discloses a neural network convolution operation device and method. The device is used for realizing convolution operation of a weight value matrix and a neural element matrix in a neural network via a matrix multiplication manner, and comprises a displacement operation unit, a matrix multiplication operation unit and a controller, wherein the displacement operation unit is used forrespectively carrying out winograd transformation on the neural element matrix and weight value matrix so as to obtain a transformed neural element matrix and a transformed weight value matrix; the matrix multiplication operation unit is used for carrying out a matrix multiplication operation such as counterpoint multiplication on the transformed neural element matrix and the transformed weight value matrix so as to obtain a multiplication matrix; the displacement operation unit is furthermore used for carrying out winograd inverse transformation on the multiplication matrix so as to obtain aconvolution operation result; and the controller is used for controlling the displacement operation unit to carry out the winograd transformation or the winograd inverse transformation, and controlling the matrix multiplication operation unit to carry out the matrix multiplication operation.

Description

technical field [0001] The present disclosure relates to the technical field of artificial neural networks, in particular to a neural network convolution operation device and a neural network convolution operation method. Background technique [0002] Multi-layer artificial neural networks are widely used in the fields of pattern recognition, image processing, function approximation, and optimization calculations. In recent years, multi-layer artificial neural networks have been favored by academic circles and industry is getting more and more attention. [0003] In order to meet the increasingly high task requirements, the scale of neural networks has become larger and larger. At present, large-scale convolutional neural networks have included hundreds of layers of network layer structures. The resulting problem is that the neural network needs to do more calculations, especially the convolutional neural network. A large number of convolution operations reduce the calculat...

AI Technical Summary

Problems solved by technology

The resulting problem is that the neural network needs to do more calculations, especially the convolutional neural network. A large number of convolution operations reduce the calculation speed of the neural network and affect the use of the neural network in practical applications.

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