Google 的“捕日者计划”(Project Suncatcher)旨在将机器学习基础设施部署到太空。
Google’s Project Suncatcher to put ML infrastructure in space

原始链接: https://blog.google/innovation-and-ai/models-and-research/google-research/google-project-suncatcher-facts/

Google 正在开发配备 TPU 芯片的卫星集群,旨在提供可扩展的太空 AI 计算能力。为了支持 AI 处理所需的高带宽,这些卫星将采用卫星间激光通信技术。与目前用于长距离、低带宽数据传输的太空激光系统不同,该技术需要在卫星高速运动的状态下,通过极高的精度来维持短距离的高速链路。 为了验证这一概念,团队计划于 2027 年发射两颗测试卫星。此次任务是迈出的关键第一步,旨在测试硬件在轨道上的耐用性、识别潜在故障,并为后续迭代收集关键数据。通过对这一“登月计划”采取严谨的工程方法,Google 旨在克服太空 AI 计算的物理挑战,并为分布式高性能计算的新时代铺平道路。

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Future designs of our satellites will each carry dozens of TPU chips while orbiting the Earth in clusters. To maintain the bandwidth necessary to process AI, every satellite has to know both its own position and where it sits relative to its neighbors. To do this, the satellites will communicate via lasers.

The technology in space already exists, but most state-of-the-art systems are optimized for low bandwidth across large distances, whereas our lasers need to operate at very high bandwidth over extremely short distances. Maintaining the necessary connection requires extraordinary precision, similar to hitting a coin-size target from miles away while both points are in motion. We’ll test our work on this in 2027 when we put two satellites in orbit.

Exploring space as a viable location for scalable AI compute won’t happen all at once. It takes methodical engineering, starting with proving our hardware can handle the physical and unpredictable realities of operating in orbit. This first launch is about seeing what works, identifying points of failure, and applying those findings to future missions.

Every transformative technology we’ve built at Google began with an audacious goal — and the discipline of working backward to solve hard problems all along the way. As our latest moonshot heads to the launchpad, our team is excited to share what we learn and the science driving this work forward.

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