32 citations · 47 across the 11 of their papers we have counts for
5 papers · 1 filter
Empowering Hybrid Attention Models on NPUs
Yinyuan Zhang, Daliang Xu, Xiaolong Huang +4
Hybrid attention models have emerged as a crucial architecture for Large Language Models (LLMs) (e.g., the Qwen3.5 and Kimi series). Their memory and computational efficiency make…
AI Infrastructure in Space: How Far Can We Go?
Qing Li, Qiyang Zhang, Daliang Xu +10
Satellites are becoming programmable computing platforms capable of running increasingly demanding AI workloads. This shift raises a systems problem: how can AI services remain dep…
PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud
Chenghua Wang, Daliang Xu, Dongqi Cai +23
Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment. Altho…
NanoSpec: Accelerating Speculative Decoding using Minimalist In-Context Vocabularies
Zhiyang Chen, Daliang Xu, Yinyuan Zhang +3
The massive vocabulary sizes of large language models, often exceeding 100k tokens, impose a computational bottleneck on the final linear projection layer during speculative decodi…
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…