将神经网络拆分到空中设备上协同计算，实现飞行中的实时智能决策。
Airborne Neural Network
- 把神经网络分成多份部署在空中的设备，由控制器协同调度计算。
- 支持飞行中实时学习与推理，适用于空管、气象等高时效场景。
- 为未来空基AI系统提供分布式计算新范式，适合航空航天领域应用。
深度学习通过神经网络推动人工智能取得突破性进展，使系统能够像人脑一样学习和适应。这些模型在数据密集型领域表现卓越，但依赖大规模计算基础设施。在航空领域，由于对实时数据处理和极低延迟的严苛要求，此类系统的部署仍面临挑战。本文提出一种新概念：空中神经网络（Airborne Neural Network），即一种分布式架构，多个空中设备各自托管神经网络的部分神经元，通过空中网络控制器和层级控制器协同工作，实现在飞行过程中的实时学习与推理。该方法有望彻底改变航空航天应用，包括空中交通管制、实时天气与地理预测、动态地理空间数据处理等。通过在空中环境中实现大规模神经网络运算，本研究为下一代智能航空系统奠定了基础。
原文摘要 · Abstract (English)
Deep Learning, driven by neural networks, has led to groundbreaking advancements in Artificial Intelligence by enabling systems to learn and adapt like the human brain. These models have achieved remarkable results, particularly in data-intensive domains, supported by massive computational infrastructure. However, deploying such systems in Aerospace, where real time data processing and ultra low latency are critical, remains a challenge due to infrastructure limitations. This paper proposes a novel concept: the Airborne Neural Network a distributed architecture where multiple airborne devices each host a subset of neural network neurons. These devices compute collaboratively, guided by an airborne network controller and layer specific controllers, enabling real-time learning and inference during flight. This approach has the potential to revolutionize Aerospace applications, including airborne air traffic control, real-time weather and geographical predictions, and dynamic geospatial data processing. By enabling large-scale neural network operations in airborne environments, this work lays the foundation for the next generation of AI powered Aerospace systems.
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