1 citations · 1 across the 9 of their papers we have counts for
9 papers
Optimal Expert Selection for Distributed Mixture-of-Experts at the Wireless Edge
Shengling Qin, Hai Wu, Hongyang Du +1
The emergence of distributed Mixture-of-Experts (DMoE) systems, which deploy expert models at edge nodes, offers a pathway to achieving connected intelligence in sixth-generation (…
Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G
Zhiyan Liu, Xu Chen, Hai Wu +4
Sensing and edge artificial intelligence (AI) are envisioned as two essential and interconnected functions in sixth-generation (6G) mobile networks. On the one hand, sensing-empowe…
Knowledge-Based Ultra-Low-Latency Semantic Communications for Robotic Edge Intelligence
Qunsong Zeng, Zhanwei Wang, You Zhou +3
The 6G mobile networks will feature the widespread deployment of AI algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In s…
On-the-fly Communication-and-Computing to Enable Representation Learning for Distributed Point Clouds
Xu Chen, Hai Wu, Kaibin Huang
The advent of sixth-generation (6G) mobile networks introduces two groundbreaking capabilities: sensing and artificial intelligence (AI). Sensing leverages multi-modal sensors to c…
Resource Management for Low-latency Cooperative Fine-tuning of Foundation Models at the Network Edge
Hai Wu, Xu Chen, Kaibin Huang
The emergence of large-scale foundation models (FoMo's) that can perform human-like intelligence motivates their deployment at the network edge for devices to access state-of-the-a…
Device-Edge Cooperative Fine-Tuning of Foundation Models as a 6G Service
Hai Wu, Xu Chen, Kaibin Huang
Foundation models (FoMos), referring to large-scale AI models, possess human-like capabilities and are able to perform competitively in the domain of human intelligence. The breakt…
Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting
Hai Wu, Qunsong Zeng, Kaibin Huang
For the 6G mobile networks, in-situ model downloading has emerged as an important use case to enable real-time adaptive artificial intelligence on edge devices. However, the simult…
Vertical Layering of Quantized Neural Networks for Heterogeneous Inference
Hai Wu, Ruifei He, Haoru Tan +2
Although considerable progress has been obtained in neural network quantization for efficient inference, existing methods are not scalable to heterogeneous devices as one dedicated…
In-situ Model Downloading to Realize Versatile Edge AI in 6G Mobile Networks
Kaibin Huang, Hai Wu, Zhiyan Liu +1
The sixth-generation (6G) mobile networks are expected to feature the ubiquitous deployment of machine learning and AI algorithms at the network edge. With rapid advancements in ed…