Research and Implementation of an Embedded Image Classification Method Based on ZYNQ

FPGA was a programmable chip with powerful high parallel computing capabilities. Through the FPAG&ARM collaborative processing of neural networks, it can improve computing efficiency and reduce energy consumption. Aiming at a large number of matrix operation problems involved in the AlexNet network, this paper uses the OPENBLAS acceleration algorithm to optimize the matrix operation, and proposes a research and implementation of an embedded image classification method based on ZYNQ. This paper uses FPGA to realize the real-time image acquisition, takes the acquired image as the input of the convolutional neural network model, uses the AlexNet network on the ARM side to realize the classification of the image, and finally deploys the neural network model to the ZYNQ-7000 platform. The experimental results show that in the case of ensuring that the accuracy is not reduced during the image classification process, compared with using the AlexNet network to achieve image classification in 23.4s, using the OPENBLAS acceleration algorithm to accelerate the matrix calculation in the AlexNet network, the image classification consumes time is about 4.5s, and the classification performance has been improved by nearly 5.2 time.