32 citations · 47 across the 11 of their papers we have counts for
12 papers
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…
Accelerating Mobile Language Model via Speculative Decoding and NPU-Coordinated Execution
Zhiyang Chen, Daliang Xu, Haiyang Shen +5
Performing Retrieval-Augmented Generation (RAG) directly on mobile devices is promising for data privacy and responsiveness but is hindered by the architectural constraints of mobi…
ShadowNPU: System and Algorithm Co-design for NPU-Centric On-Device LLM Inference
Wangsong Yin, Daliang Xu, Mengwei Xu +2
On-device running Large Language Models (LLMs) is nowadays a critical enabler towards preserving user privacy. We observe that the attention operator falls back from the special-pu…
MobiEdit: Resource-efficient Knowledge Editing for Personalized On-device LLMs
Zhenyan Lu, Daliang Xu, Dongqi Cai +5
Large language models (LLMs) are deployed on mobile devices to power killer applications such as intelligent assistants. LLMs pre-trained on general corpora often hallucinate when…
Elastic On-Device LLM Service
Wangsong Yin, Rongjie Yi, Daliang Xu +3
On-device Large Language Models (LLMs) are transforming mobile AI, catalyzing applications like UI automation without privacy concerns. Nowadays the common practice is to deploy a…
Fast On-device LLM Inference with NPUs
Daliang Xu, Hao Zhang, Liming Yang +4
On-device inference for Large Language Models (LLMs), driven by increasing privacy concerns and advancements of mobile-sized models, has gained significant interest. However, even…
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Mengwei Xu, Wangsong Yin, Dongqi Cai +15
Large foundation models, including large language models (LLMs), vision transformers (ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine lear…
LLMCad: Fast and Scalable On-device Large Language Model Inference
Daliang Xu, Wangsong Yin, Xin Jin +4
Generative tasks, such as text generation and question answering, hold a crucial position in the realm of mobile applications. Due to their sensitivity to privacy concerns, there i…