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About this paper
Chenghua Wang, Daliang Xu, Dongqi Cai, Duojin Sun, Hao Zhang, Haoze Qian, Huaiyuan Zhang, Jinshuo Cui and 18 more
PhyAI introduces a unified inference runtime for physical AI policies, enabling consistent, high-performance execution across edge devices and cloud environments for diverse vision-language-action (VLA) and world-action models (WAMs). It achieves up to 4.65x speedups over official implementations and incorporates a "control-time Roofline" to guide performance optimization by distinguishing inference-bound from environment-bound control.
Key insights
- Decoupling model semantics from execution policies in a unified runtime simplifies the development and deployment of embodied AI models across their lifecycle.
- Combining micro-optimizations (kernel fusion) with macro-optimizations (CUDA Graphs) and dynamic operator selection significantly boosts real-time inference performance on diverse hardware.
- The "control-time Roofline" provides a practical framework to identify whether inference or environmental execution is the actual bottleneck, guiding efficient resource allocation and optimization efforts.
Summary by alphaXiv.
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