Convolutional neural network acceleration method based on OpenCL standard

Abstract

The invention proposes a convolutional neural network acceleration method based on the OpenCL standard for mainly solving the problem of low efficiency of the existing CPU to process a convolutional neural network. The method comprises the following steps: 1, reading original three-dimensional image data, and transmitting the original three-dimensional image data to a global memory of a GPU; 2, reading weights and offset data in the global memory of the GPU; 3, reading the original image data in the global memory of the GPU in a local memory of the GPU; 4, initializing the parameters, and constructing a linear activation function Leaky-ReLU; 5, calculating picture data of the twelfth layer of the convolutional neural network; 6, calculating the picture data of the fifteenth layer of the convolutional neural network; and 7, calculating the picture data of the eighteenth layer of the convolutional neural network, storing the picture data in the GPU, transmitting the picture data to a host memory, and providing an operation time. By adoption of the convolutional neural network acceleration method, the operation speed of the convolutional neural network is improved, and the convolutional neural network acceleration method can be applied to object detection in computer vision.

Description

technical field [0001] The invention belongs to the technical field of unmanned driving perception, and in particular relates to a convolutional neural network acceleration method, which can be used for object detection of computer vision. Background technique [0002] With the deepening of neural network research, researchers have found that convolution operations on image input are similar to neurons in biological vision receiving local input, and adding convolution operations to neural networks has become a mainstream trend. Due to the specific design of the convolutional neural network CNN in the structure of the neural network for the characteristics of the visual input itself, the convolutional neural network has become an inevitable choice in the field of computer vision. As the domain of computer vision, the perception part of unmanned driving will inevitably become the stage where CNN plays a role. [0003] The main computing tool for traditional deep learning algo...

AI Technical Summary

Problems solved by technology

However, when deep learning algorithms require more and more computing power, especially convolutional neural network algorithms, the efficiency of CPU execution cannot meet the demand

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