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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.24513 |
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| _version_ | 1866915313566613504 |
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| author | Ranjan, Paritosh Majumder, Surajit Roy, Prodip |
| author_facet | Ranjan, Paritosh Majumder, Surajit Roy, Prodip |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24513 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Airborne Neural Network Ranjan, Paritosh Majumder, Surajit Roy, Prodip Machine Learning Neural and Evolutionary Computing 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. |
| title | Airborne Neural Network |
| topic | Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2505.24513 |