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优化深度学习模型通过神经架构搜索来进行轨道部署
Roberto Del Prete1,2, Parampuneet Kaur Thind3,4, Andrea Mazzeo5
1Φ-lab, European Space Agency (ESA), ESRIN, Via Galileo Galilei, Frascati, 00044, Italy. roberto.delprete@esa.int.
Scientific reports
|October 30, 2025
概括
本研究介绍了一个神经架构搜索 (NAS) 框架,用于优化CubeSats上的AI模型. 该NAS方法实现了空间边缘计算的高效实时内置处理,优于现有的方法.
科学领域:
- 太空边缘计算在太空中的边缘计算.
- 卫星中的人工智能 (AI)
- 神经架构搜索 (NAS) 是一种神经架构搜索.
背景情况:
- 立方体卫星面临着在机载人工智能处理方面显著的能量和内存限制.
- 轻量级的人工智能模型对于在太空中自主处理数据至关重要.
- 现有的模型压缩技术可能不适合硬件特定约束.
研究的目的:
- 开发和评估一个以进化为基础的NAS框架,用于优化资源有限的CubeSats上的AI模型.
- 为了实现有效的,对硬件有意识的模型压缩,用于内部处理.
- 为了平衡精度,大小和延迟,在轨道上实时推断.
主要方法:
- 设计了一个以进化为基础的NAS框架,结合了硬件意识.
- 为CubeSat级硬件 (NVIDIA Jetson AGX,英特尔Myriad X) 联合优化了网络架构和部署.
- 评估了燃烧区域细分和分类任务的框架.
主要成果:
- 实现了具有<1MB内存足迹的模型,使实时,高分辨率推断成为可能.
- 模型显示的延迟比手工制作的基线低3倍,同时保持了竞争力.
- 在分类上获得0.974的马修相关系数 (MCC),比EfficientNet-lite0.0.4的速度提高了47倍.
结论:
- 纳斯框架有效地优化了用于太空边缘计算的AI模型.
- 该方法提供了可扩展的解决方案,从资源有限的设备到数据中心加速器.
- 这项工作支持下一代轨道计算架构的开发.
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