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A general 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 ability

Active Publication Date: 2022-03-04
JILIN UNIV
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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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  • A general convolution-pooling synchronous processing convolution kernel system
  • A general convolution-pooling synchronous processing convolution kernel system
  • A general 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 general convolution-pooling synchronous processing convolution kernel system, which belongs to the technical field of convolutional neural network acceleration in machine learning. Aiming at the problems that the existing machine learning methods are realized by software, which has limited computing power and high cost, the present invention adopts hardware design to realize machine learning, and realizes the acceleration of convolutional neural network by means of convolution-pool synchronous processing. The purpose is to realize machine learning quickly, with low power consumption and high efficiency without changing the accuracy rate. The usual convolution kernel of the existing convolutional neural network is a fixed size, which cannot adapt to various design needs. The convolution kernel in the present invention can change parameters such as the convolution kernel size and step size, and can adapt to various situations. design needs.

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 Patents(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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