100 citations · 148 across the 20 of their papers we have counts for
11 papers · 1 filter
Venus: An Efficient Edge Memory-and-Retrieval System for VLM-based Online Video Understanding
Shengyuan Ye, Bei Ouyang, Tianyi Qian +5
Vision-language models (VLMs) have demonstrated impressive multimodal comprehension capabilities and are being deployed in an increasing number of online video understanding applic…
Jupiter: Fast and Resource-Efficient Collaborative Inference of Generative LLMs on Edge Devices
Shengyuan Ye, Bei Ouyang, Liekang Zeng +4
Generative large language models (LLMs) have garnered significant attention due to their exceptional capabilities in various AI tasks. Traditionally deployed in cloud datacenters,…
Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices
Shengyuan Ye, Liekang Zeng, Xiaowen Chu +2
On-device Deep Neural Network (DNN) training has been recognized as crucial for privacy-preserving machine learning at the edge. However, the intensive training workload and limite…
Resource-Efficient Personal Large Language Models Fine-Tuning with Collaborative Edge Computing
Shengyuan Ye, Bei Ouyang, Tianyi Qian +6
Large language models (LLMs) have unlocked a plethora of powerful applications at the network edge, such as intelligent personal assistants. Data privacy and security concerns have…
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence
Liekang Zeng, Shengyuan Ye, Xu Chen +6
Recent years have witnessed a thriving growth of computing facilities connected at the network edge, cultivating edge networks as a fundamental infrastructure for supporting miscel…
Online Optimization of DNN Inference Network Utility in Collaborative Edge Computing
Rui Li, Tao Ouyang, Liekang Zeng +3
Collaborative Edge Computing (CEC) is an emerging paradigm that collaborates heterogeneous edge devices as a resource pool to compute DNN inference tasks in proximity such as edge…
Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference
Shengyuan Ye, Jiangsu Du, Liekang Zeng +4
Transformer-based models have unlocked a plethora of powerful intelligent applications at the edge, such as voice assistant in smart home. Traditional deployment approaches offload…
Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services
Liekang Zeng, Xu Chen, Peng Huang +3
Graph Neural Networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures.…
GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers
Liekang Zeng, Chongyu Yang, Peng Huang +3
Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has ext…
Joint Multi-User DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence
Xin Tang, Xu Chen, Liekang Zeng +2
Mobile Edge Computing (MEC) has emerged as a promising supporting architecture providing a variety of resources to the network edge, thus acting as an enabler for edge intelligence…
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
Zhi Zhou, Xu Chen, En Li +3
With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to re…