32 citations · 70 across the 8 of their papers we have counts for
8 papers
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…
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…
Small Language Models: Survey, Measurements, and Insights
Zhenyan Lu, Xiang Li, Dongqi Cai +5
Small language models (SLMs), despite their widespread adoption in modern smart devices, have received significantly less academic attention compared to their large language model…
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…
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…
EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices
Rongjie Yi, Liwei Guo, Shiyun Wei +3
Large language models (LLMs) such as GPTs and Mixtral-8x7B have revolutionized machine intelligence due to their exceptional abilities in generic ML tasks. Transiting LLMs from dat…
Boosting DNN Cold Inference on Edge Devices
Rongjie Yi, Ting Cao, Ao Zhou +3
DNNs are ubiquitous on edge devices nowadays. With its increasing importance and use cases, it's not likely to pack all DNNs into device memory and expect that each inference has b…