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HA-NPU is presented, the first system to enable efficient hybrid attention LLM inference on edge NPUs without modifying the underlying algorithms, and enhances execution efficiency by reorganizing the dataflow of the LA components across three levels.
PhyAI, a Physical AI inference engine with a single runtime that keeps architecture-specific conditioning, solver, cache, and output logic in model adapters while sharing graph execution, kernels, memory management, and parallel services, is built.
A systems vision for AI infrastructure in space is developed 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.
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