%A ZHANG Ying, TAO Lei-yan, CAO Jian, WANG Shi-hui, ZHAO Qian, ZHANG Xing %T Real-time Low Power Consumption Aircraft Neural Network %0 Journal Article %D 2021 %J Computer Science %R 10.11896/jsjkx.191200142 %P 196-200 %V 48 %N 3 %U {https://www.jsjkx.com/CN/abstract/article_19781.shtml} %8 2021-03-15 %X 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.