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Acceleration method and device for convolutional neural network

A convolutional neural network and acceleration device technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve the problems of slow CNN operation speed and difficulty in implementing real-time requirements, so as to ensure accuracy and reduce redundancy. More calculations, the effect of improving the running speed

Inactive Publication Date: 2017-05-17
CAPITAL NORMAL UNIVERSITY +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] In view of this, the object of the present invention is to provide an acceleration method and device for a convolutional neural network, to solve the technical problems of slow running speed of CNN existing in the prior art and difficulty in performing tasks with high real-time requirements

Method used

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  • Acceleration method and device for convolutional neural network
  • Acceleration method and device for convolutional neural network
  • Acceleration method and device for convolutional neural network

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Embodiment 1

[0032] figure 1 A schematic diagram of the prediction process of the convolutional neural network acceleration method provided in the first embodiment of the present invention, as shown in figure 1 As shown, the method includes the following steps:

[0033] Step S110, setting a half-stop module in the convolutional neural network.

[0034] Specifically, CNN includes multiple convolutional layers and multiple pooling layers and other computing layers. The convolutional layers are used to extract features, and the pooling layers are used for dimensionality reduction. In this embodiment, the half-stop module can be set between any two operation layers of the CNN, so as to interrupt the operation process of the CNN in advance.

[0035] One or more half-stop modules can be provided. For example, when the accuracy requirement is not high, one or more half-stop modules can be set between the first few calculation layers because the calculation results of the first few calculation la...

Embodiment 2

[0077] Image 6 It is a schematic diagram of the module composition corresponding to the prediction process in the acceleration device of the convolutional neural network provided by the second embodiment of the present invention, as shown in Image 6 As shown, the device includes: a setting module 11, which is used to set a half-stop module in the convolutional neural network; a first calculation module 12, which is used for when the half-stop module is executed during the prediction process of the convolutional neural network, Calculate the current prediction result of the prediction process; the first judgment module 13 is used to judge whether the current prediction result meets the preset prediction requirement; the end control module 14 is used to stop the prediction when the current prediction result meets the preset prediction requirement process, and use the current prediction result as the final prediction result of the convolutional neural network, otherwise, contin...

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Abstract

The invention provides an acceleration method and device for a convolutional neural network, and relates to the technical field of image processing. The method comprises the steps that a semi-stop module is arranged in the convolutional neural network; in the prediction process of the convolutional neural network, a current prediction result of the prediction process is calculated when the semi-stop module is executed; whether the current prediction result meets preset prediction conditions or not is judged; when the current prediction result meets the preset prediction conditions, the prediction process stops, the current prediction result is taken as a final prediction result of the convolutional neural network, and otherwise, the prediction process continues to be executed. According to the acceleration method and device for the convolutional neural network, the technical problems that in the prior art, the operation speed of the CNN is low, and tasks which have a high requirement on the real-time performance are difficult to execute can be solved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an acceleration method and device for a convolutional neural network. Background technique [0002] Recently, deep learning has been widely used in related fields such as speech analysis, image recognition, and natural language processing. Convolutional Neural Network (CNN, Convolutional Neural Network) is an important branch of deep learning and has become a research hotspot in the fields of speech analysis and image recognition. Due to its super feature extraction ability and end-to-end global optimization ability, CNN has greatly improved the accuracy of visual target detection, classification and recognition, etc. [0003] CNN is a multi-layer perceptron specially designed to recognize two-dimensional shapes. This network structure is highly invariant to translation, scaling, tilting or other forms of deformation. CNN includes multi-layer convolutional layers and p...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08
CPCG06N3/08G06N3/045
Inventor 周建设张勇东张冬明刘杰史金生张志伟
Owner CAPITAL NORMAL UNIVERSITY
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