Design Of Deep Learning Image Classification And Recognition System Based On Zynq

Convolutional neural network applications are currently implemented on CPUs or GPUs at the expense of performance and energy consumption.Therefore,the research hotspot has turned to how to implement deep learning related algorithms with high performance and low power consumption.In recent years,more and more embedded machine vision-related applications(real-time image and video processing)have emerged endlessly.However,traditional embedded processors have very limited computing power,and many scene applications can no longer meet the requirements of real-time processing.After analyzing the current embedded processor to implement image processing based on deep learning related algorithms,an ARM+FPAG design method is finally used to perform image processing based on deep learning related algorithms,which can make full use of convolutional neural networks.The parallel characteristics,and has the advantages of real-time and low power consumption.On the basis of the Xilinx Zynq-7000 architecture,a reasonable image and software system platform is finally set up through reasonable hardware and software design.The main work in the paper is:Deep learning has significant effect on the recognition of abstract problems.It is concluded that the basic algorithm of convolutional neural network in deep learning network can be well used in image processing,and on this basis,the current convolutional neural network algorithm is analyzed.The calculation method of the hardware and software in the computing part of the method and technical means.Based on the understanding of the development features of the image classification system framework based on Zynq So C hardware architecture and the ARM part of the Zynq-7000 development platform,the operating environment of the image classification system was designed,and the Linux embedded operating system was implemented according to the system startup mode.Porting involves the design of U-boot,device tree files,and file systems.A synchronous data stream IO model is proposed to realize the design of convolutional neural network deployment on FPGA.The advantage is to achieve the best performance with the smallest memory footprint.Implement the IP design of the FPGA part of Zynq with Vivado HLS development tools.At the same time,according to the data stream transmission architecture,32-bit floating-point and fixed-point algorithms are compared to design each layer of IP in the convolutional neural network.Based on the Zynq-7000 platform,the software and hardware co-design of the image classification and recognition system is given,and the software and hardware development flow is given.The hardware driver under the Linux part of the software was written,and a programming interface was provided to serve the upper users.The accelerator verified that the CIFAR-10 prototype showed up to 43 times the acceleration,while maintaining a classification accuracy of 73.7% and low power consumption of 2.063 W,confirming that the Zynq-7000 platform can achieve good image classification and recognition in embedded image applications.It has high real-time performance.