Physical AI aims to enable intelligent systems to perceive, reason, act, and learn through interaction with the physical world. Unlike purely digital intelligence, physical agents must operate under partial observability, uncertain dynamics, embodiment constraints, and real-time requirements, while their actions contin...
Bin Gao, Anuj Kumar Gautam, Aritra Bagchi et al.· 0 citations
Vision-language-action (VLA) policies repeatedly invoke inference to control robots, making graphics processing unit (GPU) energy a recurring cost of task execution. Reducing energy per inference call, however, may not reduce energy per successful task if numerical errors increase failures or slower inference prolongs...
Qing-Yun Zou, Bin Gao, Wen-Ju Zhao et al.· 0 citations
The key-value (KV) cache has become the dominant consumer of memory in large language model (LLM) serving systems as context lengths, concurrency, and request lifetimes grow. High-bandwidth memory (HBM) provides the bandwidth attention decode needs but limited capacity, while off-package memory and storage add capacity...
Experimental results indicate that such complex designs exceed the capabilities of existing approaches, whereas the proposed LLM-based hardware development framework is the only one capable of producing functional end-to-end design.
SAEM is proposed, a stage-aware MoE inference runtime that detects reasoning stage boundaries and exploits stage-level activation coherence to guide expert placement and achieves an average 1.33x throughput improvement over the strongest state-of-the-art caching and offloading baselines under constrained GPU memory.
Yujie Zhang, Bin Gao, Tulika Mitra· 0 citations
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