Universal convolution-pooling synchronous processing convolution kernel system

A convolution kernel and pooling technology, applied in physical implementation, neural architecture, biological neural network model, etc., can solve problems such as high cost and limited capacity

Active Publication Date: 2019-07-05
JILIN UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

However, although the computing power of the GPU has been improved to a certain extent, even if the capacity is still limited, and the cost is relatively high

Method used

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  • Universal convolution-pooling synchronous processing convolution kernel system
  • Universal convolution-pooling synchronous processing convolution kernel system
  • Universal convolution-pooling synchronous processing convolution kernel system

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Embodiment Construction

[0056] In order to make the design solution of the present invention more clear, the implementation of the examples of the present invention will be described in detail below in conjunction with the drawings in the examples of the present invention.

[0057] The present invention is the accelerated hardware realization of the forward propagation discrimination process of the convolutional neural network, including the convolutional layer and the pooling layer of the convolutional neural network. Among them, the formula of the convolutional layer is:

[0058]

[0059] Among them, l represents the number of layers, a l Represents the output tensor, * represents convolution, b represents bias, M represents the number of sub-matrices, σ represents the activation function, generally Relu.

[0060] The pooling layer formula is

[0061] a l =pool(a l-1 )

[0062] Among them, pool refers to the process of reducing the input tensor according to the pooling area size and pooling...

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Abstract

The invention discloses a universal convolution-pooling synchronous processing convolution kernel system, and belongs to the technical field of convolution neural network acceleration in machine learning. An existing machine learning method is realized by adopting software, so that the problems of limited computing power, higher cost and the like exist. According to the present invnetino, the machine learning is realized by adopting the hardware design, the purpose of accelerating the convolutional neural network is realized in a convolutional-pooling synchronous processing manner, and the machine learning can be realized quickly and efficiently with low power consumption on the premise that the accuracy is not changed. According to an existing convolutional neural network, a convolution kernel is generally of a fixed size and cannot adapt to various design requirements, but the convolution kernel of the invention can change the parameters such as the size and the step length of the convolution kernel and can adapt to the design requirements under various conditions.

Description

technical field [0001] The invention belongs to the technical field of convolutional neural network acceleration in machine learning. [0002] technical background [0003] Artificial Intelligence (Artificial Intelligence) is a major development trend in today's era, and it is widely used in many fields such as computers, medicine, biology, and machinery. Machine learning (Machine Learning), as one of the important branches, has received extensive attention in recent years and achieved rapid development. It can be trained multiple times through a large number of data samples to obtain ideal results, and is widely used in image recognition, object tracking, voice processing and other fields. Convolutional Neural Network (CNN) is one of the important methods of machine learning, which has attracted a large number of scholars to do research. Among them, Lenet, Alexnet, VGG, etc. are its more representative models, which have excellent performance in practical applications. ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/063G06N3/04
CPCG06N3/063G06N3/045
Inventor 张宝林姬梦飞常玉春李东泽丁宁戴加海慕雨松蒋佳奇马玉美郭玉萍孙畅宫浩然王若溪李捷菲
Owner JILIN UNIV
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