Method, device and system for data processing

Abstract

The embodiment of the invention discloses a method, device and system for data processing. The method comprises the steps of obtaining input data; obtaining convolution operation characteristics of afirst convolution layer, wherein the first convolution layer is any convolution layer in a neural network model, the neural network model comprises at least one convolution layer, and the convolutionoperation characteristics comprise at least one of the following parameters: the size of a convolution kernel, the moving step of the convolution kernel, the number of input channels and the parallelgranularity of a processor; obtaining a plurality of data blocks with the number being the same as the parallel granularity of the processor from the input data according to the convolution operationcharacteristics, wherein each of the plurality of data blocks is used for performing convolution processing with the convolution kernel; and performing convolution processing on the plurality of datablocks in parallel so as to obtain output data. The method, device and system disclosed by the embodiment of the invention can adaptively select an optimal parallel operation according to the convolution operation characteristics of different convolution layers, so that the parallelism of the convolution operation is improved.

Description

technical field [0001] The present invention relates to a convolutional neural network (Convolution Neural Network, CNN), in particular to a method, device and system for data processing in CNN. Background technique [0002] Convolutional neural network is the most widely used algorithm in deep learning, and it is widely used in image classification, speech recognition, video understanding, face detection and many other applications. Its weight sharing network structure makes it more similar to biological neural networks, reducing the complexity of the network model and reducing the number of weights. This advantage is more obvious when the input of the network is a multi-dimensional image, so that the image can be directly used as the input of the network, avoiding the complicated feature extraction and data reconstruction process in the traditional recognition algorithm. [0003] At present, the parallel acceleration of convolutional neural networks has become a research ...

AI Technical Summary

Problems solved by technology

The main problem of the existing method is that all the convolutional layers in the CNN network use the same parallel method, which is inefficient.

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