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Method for accelerating GPU directed at deep learning super-resolution technology

A technology of super-resolution and deep learning, applied in the field of image super-resolution, can solve problems such as huge computing overhead and cannot meet the needs of practical applications, and achieve the effect of accelerating and optimizing computing speed

Active Publication Date: 2016-08-17
SHANGHAI JIAO TONG UNIV
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, this method relies on a huge amount of computing overhead, and it takes 300 seconds to execute this method with a CPU for each frame (1920*1080 to 3840*2160, single channel, all tests below are based on this resolution), even if GEMM-based GPU convolution and acceleration methods also require close to 1 second for each frame, which cannot meet the needs of practical applications

Method used

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  • Method for accelerating GPU directed at deep learning super-resolution technology
  • Method for accelerating GPU directed at deep learning super-resolution technology
  • Method for accelerating GPU directed at deep learning super-resolution technology

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Embodiment Construction

[0023] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0024] Such as figure 1 Shown, the flowchart of SRCNN. As a preferred embodiment of the present invention, the super-resolution GPU acceleration technology of the present invention is aimed at SRCNN, and its flow chart is as follows figure 1 As shown, it contains bicubic preprocessing (not marked), three convolutional layers and two RELU layers (following the first convolution and the second convolution respectively). The sizes of the three convolutional layers are (according to output channel *...

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Abstract

The invention discloses a method for accelerating a GPU directed at a deep learning super-resolution technology. The method conducts concurrent processing on all the steps of a super-resolution technology which is based on deep learning and a convolutional neural network, and operates on a GPU. The concurrent processing refers to conducting concurrent task dividing on convolutions of the super-resolution technology which is based on deep learning and the convolutional neural network into millions of micro-tasks which are irrelevant to one another and can be concurrently executed in any order so as to fully exhibit the super-strong calculating capability of the GPU. Further, the method uses features of a GPU memory to cache convolution nuclear data and input image data to a shared memory and a register so as to substantially optimize calculating speeds of the convolutions. The method integrates the convolutions and non-linear layers. The method selects the optimal method for the sizes of different convolutions. According to the invention, the method accelerates the high quality super-resolution method to meet velocity requirements for processing videos, and does not cause any image quality loss.

Description

technical field [0001] The present invention relates to an image super-resolution field and a GPU acceleration method, in particular to a GPU acceleration method for deep learning super-resolution technology. Background technique [0002] Image super-resolution is to convert a low-resolution image into a high-resolution image, which has a wide range of applications in image post-processing and video nonlinear editing. Early super-resolution methods (such as bicubic) are often based on simple interpolation, which can work quickly and reliably, and are easy to integrate on chips; however, the high-resolution images obtained by these methods are of poor quality and produce significant artifacts, such as rings, Effects such as aliasing, blurring, etc. Super-resolution methods of such quality are difficult to meet the current high-quality video demands. The current super-resolution methods with advanced performance can generate high-quality images, but with huge computational o...

Claims

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Application Information

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IPC IPC(8): G06T3/40G06T1/20
CPCG06T1/20G06T3/4053
Inventor 宋利赵章宗解蓉
Owner SHANGHAI JIAO TONG UNIV
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