{"id":244439,"date":"2025-11-05T01:48:11","date_gmt":"2025-11-05T01:48:11","guid":{"rendered":"https:\/\/www.newsbeep.com\/uk\/244439\/"},"modified":"2025-11-05T01:48:11","modified_gmt":"2025-11-05T01:48:11","slug":"next-google-moonshot-puts-tpus-in-space-with-project-suncatcher","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/uk\/244439\/","title":{"rendered":"Next Google moonshot puts TPUs in space with Project Suncatcher"},"content":{"rendered":"

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Google is starting<\/a> a new research moonshot called Project Suncatcher to \u201cone day scale machine learning in space.\u201d<\/p>\n

This would involve Google Tensor Processing Unit (TPU) AI chips being placed onboard an interconnected network of satellites to \u201charness the full power of the Sun.\u201d<\/p>\n

Specifically, a \u201csolar panel can be up to 8 times more productive than on earth\u201d for near-continuous power using a \u201cdawn\u2013dusk sun-synchronous low earth orbit\u201d that reduces the need for batteries and other power generation.\u00a0<\/p>\n

In the future, space may be the best place to scale AI compute.<\/p>\n

These satellites would connect via free-space optical links, with large-scale ML workloads \u201cdistributing tasks across numerous accelerators with high-bandwidth, low-latency connections.\u201d To match data centers on Earth, the connection between satellites would have to be tens of terabits per second, and they\u2019d have to fly in \u201cvery close formation (kilometers or less).\u201d<\/p>\n

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\u2026with satellites positioned just hundreds of meters apart, we will likely only require modest station-keeping maneuvers to maintain stable constellations within our desired sun-synchronous orbit.<\/p>\n

Google has already conducted radiation testing on TPUs (Trillium, v6e), with \u201cpromising\u201d results:<\/p>\n

While the\u00a0High Bandwidth Memory<\/a>\u00a0(HBM) subsystems were the most sensitive component, they only began showing irregularities after a cumulative dose of 2\u00a0krad(Si)<\/a>\u00a0\u2014 nearly three times the expected (shielded) five year mission dose of 750 rad(Si). No hard failures were attributable to TID up to the maximum tested dose of 15 krad(Si) on a single chip, indicating that Trillium TPUs are surprisingly radiation-hard for space applications.<\/p>\n

Finally, Google believes that launch costs will \u201cfall to less than $200\/kg by the mid-2030s.\u201d At that point, the \u201ccost of launching and operating a space-based data center could become roughly comparable to the reported energy costs of an equivalent terrestrial data center on a per-kilowatt\/year basis.\u201d<\/p>\n

Our initial analysis shows that the core concepts of space-based ML compute are not precluded by fundamental physics or insurmountable economic barriers.<\/p>\n

Google still has to work through engineering challenges like thermal management, high-bandwidth ground communications, and on-orbit system reliability. It\u2019s partnering with Planet to launch two prototype satellites by early 2027 that test how \u201cmodels and TPU hardware operate in space and validate the use of optical inter-satellite links for distributed ML tasks.\u201d<\/p>\n

More details are available in \u201cTowards a future space-based, highly scalable AI infrastructure system design<\/a>.\u201d<\/p>\n

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