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A method and a device for improving the computing performance of a GEMM

A technology of computing performance and algorithms, applied in the computer field, can solve problems such as poor results

Active Publication Date: 2019-03-12
XFUSION DIGITAL TECH CO LTD
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  • Application Information

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Problems solved by technology

For the detection stage of deep learning, the existing methods to improve the computing performance of GEMM are not effective.

Method used

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  • A method and a device for improving the computing performance of a GEMM
  • A method and a device for improving the computing performance of a GEMM
  • A method and a device for improving the computing performance of a GEMM

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Embodiment Construction

[0024] The method and device for improving the computing performance of GEMM disclosed in this application can be applied in the process of detecting targets using deep convolutional neural networks. Such as figure 1 As shown, after completing the training of the deep convolutional neural network, the deep convolutional neural network is used to detect the acquired objects. For example, a face image is obtained through an image acquisition device, and as a target, a trained deep convolutional neural network is used to detect the face image, and a detection result indicating the identity of the face is obtained.

[0025] Among them, in the process of detecting the target, the deep convolutional neural network converts the convolution calculation into GEMM calculation. During the research process, the applicant found that the existing methods for improving the performance of GEMM calculations are aimed at large-scale matrices. Therefore, they should be used in the process of tr...

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Abstract

The present application provides a method and apparatus for improving the computational performance of a GEMM, to the method comprises steps of obtaining the parameters of multiplication of the general matrix-matrix and GEMM calculation to be optimized and querying the target parameters from the parameters calculated by the at least one historical GEMM, and the target parameters are the parameterssatisfying a preset relationship with the parameters calculated by the GEMM calculation to be optimized. According to the preset correspondence between the parameters and the optimization method, anoptimization method corresponding to the objective parameters is determined. And optimizing the GEMM calculation to be optimized by using the optimization method corresponding to the objective parameters. Wherein the parameters calculated by the GEMM are determined based on the size of the matrix participating in the GEMM calculation. Because the characteristics of the matrices involved in the GEMM computation are used as the basis for the optimization of the GEMM computation, the performance of the GEMM computation can be improved even if the matrices are small in size or irregular in shape in the process of target detection using depth convolution neural network.

Description

technical field [0001] The present application relates to the computer field, in particular to a method and device for improving GEMM computing performance. Background technique [0002] General Matrix-matrix Multiplication (GEMM) calculations are generally calculations for dense matrices and are widely used in deep learning. Deep learning is a method based on representation learning of data in machine learning. [0003] With the development of deep learning, deep convolutional neural network has become the most widely used network structure, and it is widely used in the fields of image and speech. The core algorithm of the deep convolutional neural network is convolution calculation, and the current mainstream implementation method is to convert convolution calculation into GEMM calculation. Studies have shown that GEMM calculations occupy most of the computing resources of deep convolutional neural networks. It can be seen that the performance of GEMM calculations direct...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/16G06N3/04
CPCG06F17/16G06N3/045
Inventor 齐霁张邵敏贾海鹏
Owner XFUSION DIGITAL TECH CO LTD
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