32 citations · 85 across the 16 of their papers we have counts for
17 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…
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…
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…
LoRASuite: Efficient LoRA Adaptation Across Large Language Model Upgrades
Yanan Li, Fanxu Meng, Muhan Zhang +3
As Large Language Models (LLMs) are frequently updated, LoRA weights trained on earlier versions quickly become obsolete. The conventional practice of retraining LoRA weights from…
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…
Recall: Empowering Multimodal Embedding for Edge Devices
Dongqi Cai, Shangguang Wang, Chen Peng +2
Human memory is inherently prone to forgetting. To address this, multimodal embedding models have been introduced, which transform diverse real-world data into a unified embedding…
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…
FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts
Hanzi Mei, Dongqi Cai, Ao Zhou +2
As Large Language Models (LLMs) push the boundaries of AI capabilities, their demand for data is growing. Much of this data is private and distributed across edge devices, making F…
The CAP Principle for LLM Serving: A Survey of Long-Context Large Language Model Serving
Pai Zeng, Zhenyu Ning, Jieru Zhao +5
We survey the large language model (LLM) serving area to understand the intricate dynamics between cost-efficiency and accuracy, which is magnified by the growing need for longer c…
FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission
Zeling Zhang, Dongqi Cai, Yiran Zhang +3
Communication overhead is a significant bottleneck in federated learning (FL), which has been exaggerated with the increasing size of AI models. In this paper, we propose FedRDMA,…
Resource-efficient In-orbit Detection of Earth Objects
Qiyang Zhang, Xin Yuan, Ruolin Xing +6
With the rapid proliferation of large Low Earth Orbit (LEO) satellite constellations, a huge amount of in-orbit data is generated and needs to be transmitted to the ground for proc…
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…
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
Yuanchun Li, Hao Wen, Weijun Wang +22
Since the advent of personal computing devices, intelligent personal assistants (IPAs) have been one of the key technologies that researchers and engineers have focused on, aiming…
Deciphering the Enigma of Satellite Computing with COTS Devices: Measurement and Analysis
Ruolin Xing, Mengwei Xu, Ao Zhou +4
In the wake of the rapid deployment of large-scale low-Earth orbit satellite constellations, exploiting the full computing potential of Commercial Off-The-Shelf (COTS) devices in t…
The First Verification Test of Space-Ground Collaborative Intelligence via Cloud-Native Satellites
Shangguang Wang, Qiyang Zhang, Ruolin Xing +2
Recent advancements in satellite technologies and the declining cost of access to space have led to the emergence of large satellite constellations in Low Earth Orbit. However, the…