4 papers
PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud
Chenghua Wang, Daliang Xu, Dongqi Cai +24
Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment. Altho…
Quant.npu: Enabling Efficient Mobile NPU Inference for on-device LLMs via Fully Static Quantization
Jinghe Zhang, Daliang Xu, Chenghua Wang +5
Large language models (LLMs) are increasingly deployed on mobile devices, where Neural Processing Units (NPUs) necessitate fully static quantization for optimal inference efficienc…
DroidCall: A Dataset for LLM-powered Android Intent Invocation
Weikai Xie, Li Zhang, Shihe Wang +2
The growing capabilities of large language models in natural language understanding significantly strengthen existing agentic systems. To power performant on-device mobile agents f…
PhoneLM:an Efficient and Capable Small Language Model Family through Principled Pre-training
Rongjie Yi, Xiang Li, Weikai Xie +6
The interest in developing small language models (SLM) for on-device deployment is fast growing. However, the existing SLM design hardly considers the device hardware characteristi…