A convolutional neural network processing method, device, equipment and storage medium

Abstract

Disclosed are a convolutional neural network processing method and apparatus, a device, and a storage medium. The method comprises: acquiring an original weighting value matrix and an original input neuron matrix of a convolutional neural network; sequentially performing Winograd transformation and quantisation processing on the original weighting value matrix to obtain a target weighting value matrix, and sequentially performing quantisation processing and Winograd transformation on the original input neuron matrix to obtain a target input neuron matrix; and, on the basis of the target weighting value matrix and the target input neuron matrix, obtaining an output neuron matrix of the convolutional neural network.

Description

technical field [0001] Embodiments of the present invention relate to deep learning technology, and in particular to a convolutional neural network processing method, device, device, and storage medium. Background technique [0002] Since AlexNet was proposed in 2012, the convolutional neural network has achieved great success in the field of image processing. In major image competitions, the effect of the convolutional neural network far exceeds the traditional algorithm, and frequently refreshes various evaluation indicators in the industry. [0003] Due to the requirements of information security and low latency, the calculation of the neural network needs to be migrated from the cloud to the mobile terminal. However, with the improvement of the effect of the convolutional neural network, the model of the convolutional neural network becomes more and more complex, and the amount of calculation increases sharply. In the cloud, the convolutional neural network can be accele...

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Problems solved by technology

[0003] Due to the requirements of information security and low latency, the calculation of the neural network needs to be migrated from the cloud to the mobile terminal. However, with the improvement of the effect of the convolutional neural network, the model of the convolutional neural network becomes more and more complex, and the amount of calculation increases sharply.

In the cloud, the convolutional neural network can be accelerated by relying on GPU (Graphics Processing Unit) parallel computing, while in the mobile terminal, due to the relative lack of computing resources, the calculation speed of the convolutional neural network cannot be improved, and thus Unable to realize real-time operation on mobile terminals

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