AI 中文总结
本文针对卫星作为AI计算平台的系统问题，提出太空AI基础设施愿景，结合在轨案例研究验证其可行性，为跨天地的可持续AI服务提供研究方向。
AI 中文摘要
卫星正成为可编程计算平台，可运行日益严苛的AI工作负载，这一转变引发了一个系统层面的问题：当计算能力、连通性、能源和热裕度随轨道时间变化时，AI服务在发射后如何保持可部署、可管理和可恢复？本文提出了太空AI基础设施的系统愿景，将其定义为管理航天器、轨道网络、地面站和云后端之间AI能力的系统层，同时将轨道和物理状态视为资源模型的一部分。我们整合了地面AI基础设施、卫星网络和卫星边缘计算的相关基础，研究了直接影响系统设计的物理约束，并通过三个覆盖节点、平台和服务级别的在轨案例研究验证了该愿景：来自BUPT-1卫星的遥测数据显示，可用计算能力受限于热和能源 envelopes；BUPT-2卫星上的SateLight将应用更新传输延迟平均降低56.54%，最高降低91.18%，且更新正确率达100%；一个有状态VLM服务案例进一步表明，热中断使执行状态恢复成为一类重要的系统问题。这些观察结果为太空原生资源管理、生命周期支持以及跨天地的可持续AI服务提供了研究议程。
英文摘要
Satellites are becoming programmable computing platforms capable of running increasingly demanding AI workloads. This shift raises a systems problem: how can AI services remain deployable, manageable, and recoverable after launch when compute capacity, connectivity, energy, and thermal headroom vary over orbital time? This paper develops a systems vision for AI infrastructure in space. We define it as the systems layer that manages AI capabilities across spacecraft, orbital networks, ground stations, and cloud backends, while treating orbital and physical state as part of the resource model. We synthesize relevant foundations from terrestrial AI infrastructure, satellite networking, and satellite edge computing, and examine the physical constraints that directly shape system design. We further ground this vision in three in-orbit case studies spanning the node, platform, and service levels. Telemetry from BUPT-1 satellite shows that usable compute capacity is bounded by thermal and energy envelopes. SateLight on BUPT-2 satellite reduces application-update transmission latency by 56.54% on average and up to 91.18%, with 100% update correctness. A stateful VLM serving case further shows that thermal interruptions make execution-state recovery a first-class systems problem. These observations motivate a research agenda for space-native resource management, lifecycle support, and sustained AI service across space and ground.
Comments20 pages, 4 figures
