Data processing method and system based on deep neural network, terminal and medium

Abstract

The invention discloses a data processing method based on a deep neural network. The method comprises the steps of obtaining an input matrix input to a hidden layer of the deep neural network; Judgingwhether the operation type of the hidden layer is a matrix multiplication operation or not; If yes, respectively partitioning the input matrix and the network parameter matrix of the hidden layer according to a preset matrix partitioning rule, and respectively distributing the partitioned input matrix and the partitioned network parameter matrix to a plurality of computing nodes to carry out matrix multiplication operation; Obtaining a partitioning calculation result, and combining the partitioning calculation result to obtain an output matrix of the hidden layer; Outputting the output matrix. The invention also discloses a data processing system of the deep neural network, a terminal and a medium. According to the method, the operation speed of matrix multiplication operation in the deepneural network can be improved, so that the overall calculation time is shortened.

Description

technical field [0001] The present invention relates to the technical field of neural networks, in particular to a data processing method, system, terminal and medium based on a deep neural network. Background technique [0002] With the development of artificial intelligence, deep neural networks have been widely used in various fields such as social security and public services, and various deep learning frameworks based on deep neural networks have been continuously proposed; The amount of parameters and calculations of the learning framework are also becoming larger and larger; in the deep learning framework that operates on the matrix as the basic unit, the current calculation method is to use the direct multiplication of matrices, and the disadvantages are: when the matrix If the size is too large, direct multiplication of matrices on the CPU (Central Processing Unit, central processing unit) or GPU (Graphics Processing Unit, graphics processing unit) will bring large ...

AI Technical Summary

Problems solved by technology

[0002] With the development of artificial intelligence, deep neural networks have been widely used in various fields such as social security and public services, and various deep learning frameworks based on deep neural networks have been continuously proposed; The amount of parameters and calculations of the learning framework are also becoming larger and larger; in the deep learning framework that operates on the matrix as the basic unit, the current calculation method is to use the direct multiplication of matrices, and the disadvantages are: when the matrix If the size is too large, direct multiplication of matrices on the CPU (Central Processing Unit, central processing unit) or GPU (Graphics Processing Unit, graphics processing unit) will bring large energy consumption, and the calculation efficiency of the algorithm is low

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