Convolutional neural network acceleration method based on OpenCL standard
A convolutional neural network and data technology, applied in the field of computer vision object detection and convolutional neural network acceleration, can solve the problem that the CPU efficiency cannot meet the demand, achieve high-performance parallel computing, improve computing speed, and large data throughput amount of effect
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[0032] The technical solutions and effects of the present invention will be further described in detail below in conjunction with the accompanying drawings.
[0033] refer to figure 1 , the implementation steps of the present invention include as follows:
[0034] Step 1, read in the original 3D image data and transfer it to the global memory of the GPU.
[0035] 1.1) The input size is a three-dimensional color road picture of 448*448, and the original picture data is read into the host memory;
[0036] 1.2) Choose but not limited to AMD R9 200 GPU as the acceleration device, expand the original image data in the host memory by one bit each, and then transfer it to the global memory of the GPU.
[0037] Step 2, read the weight data and bias data into the global memory of the GPU.
[0038] 2.1) The weight data and bias data obtained by the convolutional neural network training are first stored in a text file, and then the text file is read into the host memory;
[0039] 2.2...
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