舰船电子工程2026，Vol.46Issue(1)：122-128,7.DOI:10.3969/j.issn.1672-9730.2026.01.025
面向无人机的卷积神经网络硬件加速方案设计
Design of Convolutional Neural Network Hardware Acceleration Scheme for UAV
缪丹丹 1崔敏 2张鹏 2张鑫宇2
作者信息
- 1. 上海航天电子技术研究所 上海 201109||中北大学仪器与电子学院 太原 030051
- 2. 中北大学仪器与电子学院 太原 030051
- 折叠
摘要
Abstract
Efficient convolutional neural networks are difficult to adapt to small edge computing systems carried by UAV due to their large number of parameters and operations.This paper proposes a FPGA+ARM software-hardware heterogeneous scheme to offload the CNN product operation to FPGA hardware acceleration to achieve high-performance and low-energy target detection.The convolutional gas pedal is designed based on lightweight YOLOv4-tiny network on ZYNQ platform,using DMA to achieve data streaming between IP cores,and using FPGA parallel processing advantages to perform deep optimization through data multiplex-ing,pipelining and other operations.The experimental results show that the average accuracy of the model is 73%,the power con-sumption of the system is controlled within 3 W,the number of transmitted frames per second is FPS24.6,and the energy efficiency ratio reaches 8.14 GOP/W,which is a 5-fold enhancement compared to GPU.关键词
卷积神经网络/目标检测/ZYNQ/硬件加速Key words
convolutional neural network/target detection/ZYNQ/hardware acceleration分类
信息技术与安全科学引用本文复制引用
缪丹丹,崔敏,张鹏,张鑫宇..面向无人机的卷积神经网络硬件加速方案设计[J].舰船电子工程,2026,46(1):122-128,7.
