航空工程进展2024，Vol.15Issue(2)：188-194,7.DOI:10.16615/j.cnki.1674-8190.2024.02.21
机载超轻量化卷积神经网络加速器设计
Design of airborne ultra-lightweight convolutional neural network accelerator
石添介 1刘飞阳 1张晓1
作者信息
- 1. 航空工业西安航空计算技术研究所 预先研究部,西安 710068
- 折叠
摘要
Abstract
The huge weight parameters and complex network layer structure of convolutional neural network make its computational complexity too high,and the required computing resources and storage resources also increase rapidly with the increase of network layers,so it is difficult to deploy in airborne embedded computing systems with strict requirements on resources and power consumption,which restricts the development of airborne embedded computing systems towards high intelligence.Aiming at the demand of ultra-lightweight intelligent computing in the resource-constrained airborne embedded computing system,a set of optimization and acceleration strategy of convolutional neural network model is proposed.After ultra-lightweight processing of the algorithm model,a con-volutional neural network accelerator is built by combining acceleration operators,and the function verification of network model reasoning process is carried out based on FPGA.The results show that the established accelerator can significantly reduce the occupancy rate of hardware resources and obtain a good algorithm speedup ratio,which is of important significance for the design of airborne embedded intelligent computing system.关键词
嵌入式计算系统/卷积神经网络/轻量化/硬件加速器/FPGA验证Key words
embedded computing system/convolutional neural network/ultra-lightweight/hardware accelerator/FPGA implementation分类
航空航天引用本文复制引用
石添介,刘飞阳,张晓..机载超轻量化卷积神经网络加速器设计[J].航空工程进展,2024,15(2):188-194,7.
