100 citations · 148 across the 14 of their papers we have counts for
19 papers
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…