VGG16 hardware design and implementation for CNN in image recognition

Convolutional neural networks (CNN) are computationally intensive algorithms with rich application scenarios. In some applications, convolutional neural networks need to be deployed in embedded devices close to sensors. Field Programing Gate Arrays (FPGA) is highly favored in the research of convolutional neural network accelerators due to its design flexibility and low power consumption. Therefore, this article designs a convolutional neural network VGG16 based on FPGA. To reduce the area of hardware layout and design complexity, the design verification of VGG16 is completed on the Xilinx Zynq XC7020 development board and solved the problem of insufficient internal resources.