Image processing system based on convolutional neural network

Abstract

The invention provides an image processing system based on a convolutional neural network. The convolutional neural network at least comprises at least one convolution operation layer, at least one pooling layer and at least one full connection layer. Particularly, the image processing system comprises a development board, and a field programmable gate array FPGA and a first ARM processor are integrated on the development board. A convolution acceleration kernel is built on the FPGA. The convolution acceleration kernel is at least used for executing at least one convolution operation and at least one pooling operation and outputting a final operation result, wherein the at least one convolution operation is in one-to-one correspondence with the at least one convolution operation layer, andthe at least one pooling operation is in one-to-one correspondence with the at least one pooling layer. The first ARM processor is used for performing at least one full-connection operation on the final operation result, wherein the at least one full connection operation is in one-to-one correspondence with the at least one full connection layer. In the embodiment of the invention, the acceleration of the convolutional neural network is realized by adopting an ARM + FPGA embedded architecture.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to an image processing system based on a convolutional neural network. Background technique [0002] Convolutional neural network (CNN) is an important feed-forward neural network in the field of deep learning, which has been widely used in image classification, recognition and other fields in recent years. Convolutional neural network is a computationally intensive algorithm, and the performance of CPU implementation is not high. Therefore, FPGAs are often used to accelerate them. [0003] The parallel nature of FPGA itself is particularly suitable for accelerating convolutional neural networks, but the commonly used architecture is the PC+FPGA model, and frequent interactions between PC and FPGA will cause a lot of power consumption and delay. Contents of the invention [0004] In view of this, an embodiment of the present invention provides an image processing system ...

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Problems solved by technology

[0004] In view of this, the embodiment of the present invention provides an image processing system based on a convolutional neural network to solve the power consumption and delay problems caused by frequent interaction in the commonly used PC+FPGA mode

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