Computer Science ›› 2021, Vol. 48 ›› Issue (3): 196-200.doi: 10.11896/jsjkx.191200142

• Artificial Intelligence • Previous Articles     Next Articles

Real-time Low Power Consumption Aircraft Neural Network

ZHANG Ying1,2,3, TAO Lei-yan4, CAO Jian1, WANG Shi-hui2,3, ZHAO Qian2,3, ZHANG Xing1   

  1. 1 School of Software and Microelectronics,Peking University,Beijing 100871,China
    2 Beijing Aerospace Automatic Control Institution,Beijing 100854,China
    3 National Key Laboratory of Science and Technology on Aerospace Intelligent Control,Beijing 100854,China
    4 Beijing Institute of Remote Sensing Equipment,Beijing 100854,China
  • Received:2019-12-23 Revised:2020-04-23 Online:2021-03-15 Published:2021-03-05
  • About author:ZHANG Ying,born in 1982,Ph.D,se-nior engineer.Her main research interest is intelligent control.
    CAO Jian,born in 1980,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include edge computing,intelligent hardware and system design.
  • Supported by:
    National Natural Science Foundation of China(51877008).

Abstract: In order to meet the information processing requirements of a large amount of heterogeneous input data in the real-time flight of aircraft,this paper proposes a neural network,including convolution core with fixed-point sliding,pooling core with compression quantization and fully connected core with compression fusion.The input of the system is heterogeneous sensor data,and the output of the system is the identification results.Convolution core can extract data features quickly by eliminating redundant data sliding window.Pooling core improves system execution efficiency by using compression quantization technology.The design meets the on-line intelligent integrationrequirements of high reliability and low power consumption.With the proposed compression quantization method,the peak accuracy is 98.54%,the compression rate is 77.8%,and the running speed increases by 40 times.

Key words: Aircraft, Low power consumption, Neural network, Real-time online

CLC Number: 

  • TP311
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