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Image feature extraction method, device, apparatus, and readable storage medium

An image feature extraction and image technology, applied in the field of convolutional neural network systems, can solve the problems of bus and memory access conflicts, frequent queuing of computing cores to access shared memory, affecting system efficiency, etc., to improve system operation efficiency and good parallel communication. capacity, the effect of improving the efficiency of data transmission

Active Publication Date: 2019-01-22
ZHENGZHOU YUNHAI INFORMATION TECH CO LTD
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  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] When the above method is used for convolution data processing, a large number of computing cores will frequently queue up to access shared memory, forming bus and memory access conflicts, thereby affecting system efficiency

Method used

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  • Image feature extraction method, device, apparatus, and readable storage medium
  • Image feature extraction method, device, apparatus, and readable storage medium
  • Image feature extraction method, device, apparatus, and readable storage medium

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Embodiment Construction

[0049] The core of the present invention is to provide an image feature extraction method, which splits the extracted feature map data into several data blocks, and adopts a routing method for data transmission processing, which can improve data transmission efficiency and reduce convolution data calculation time , and then improve the overall image recognition efficiency; another core of the present invention is to provide an image feature extraction device, image feature extraction equipment and a readable storage medium.

[0050] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments ...

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Abstract

The invention discloses an image feature extraction method, which comprises the following steps: preprocessing an input image to be recognized to obtain an input feature map; setting the parameter ofa node in the convolution node on-chip network according to the node coefficients obtained in advance, splitting and packaging the input characteristic map data to obtain a communication data frame; sending the communication data frames to each node in the convolution node on-chip network in turn according to the corresponding node flow order for data processing to obtain output data; recombiningthe output data of each data frame to obtain the output data of the characteristic map, carrying out feature classification according to the output data of the feature map. The method divides the extracted feature map data into several data blocks and processes the data by routing, which can improve the efficiency of data transmission, reduce the operation time of convolution data and improve theefficiency of image recognition. The invention also discloses an image feature extraction device, an apparatus and a readable storage medium, which have the beneficial effects.

Description

technical field [0001] The invention relates to the field of convolutional neural network systems, in particular to an image feature extraction method, an image feature extraction device, an image feature extraction device, and a readable storage medium. Background technique [0002] In image recognition and classification, due to the superiority of convolutional neural network performance, convolutional neural networks are often used for image feature extraction. [0003] When hardware-accelerating the convolutional neural network, since each layer of neural network has multi-channel feature map input, filter coefficient input and corresponding multi-channel result output, in order to improve calculation speed and increase parallelism, the system Multiple computing cores are used for parallel computing. [0004] For convolutional neural networks, the input data is shared, and all output channels use these input data. At present, the input feature map data is generally pla...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F15/173G06K9/46G06N3/04G06N3/08
CPCG06F15/17318G06N3/08G06V10/40G06N3/045
Inventor 杨宏斌方兴刘栩辰董刚程云
Owner ZHENGZHOU YUNHAI INFORMATION TECH CO LTD
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