VGG16 hardware design and implementation for CNN in image recognition

Yue Tan; Liji Wu; Zhenhui Zhang; Xiangmin Zhang; Jing Zhou

doi:10.1117/12.3024717

3 April 2024 VGG16 hardware design and implementation for CNN in image recognition

Yue Tan, Liji Wu, Zhenhui Zhang, Xiangmin Zhang, Jing Zhou

Proceedings Volume 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023); 1307809 (2024) https://doi.org/10.1117/12.3024717
Event: Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 2023, Wuhan, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yue Tan, Liji Wu, Zhenhui Zhang, Xiangmin Zhang, and Jing Zhou "VGG16 hardware design and implementation for CNN in image recognition", Proc. SPIE 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 1307809 (3 April 2024); https://doi.org/10.1117/12.3024717

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