100 citations · 150 across the 10 of their papers we have counts for
17 papers
iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud
Fei Xu, Jianian Xu, Jiabin Chen +4
GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs…
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…
Edge Robotics: Edge-Computing-Accelerated Multi-Robot Simultaneous Localization and Mapping
Peng Huang, Liekang Zeng, Xu Chen +3
With the wide penetration of smart robots in multifarious fields, Simultaneous Localization and Mapping (SLAM) technique in robotics has attracted growing attention in the communit…
Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control
Qiong Wu, Xu Chen, Zhi Zhou +2
To meet the ever increasing mobile traffic demand in 5G era, base stations (BSs) have been densely deployed in radio access networks (RANs) to increase the network coverage and cap…
FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring
Qiong Wu, Xu Chen, Zhi Zhou +1
In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and rec…
CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devices
Liekang Zeng, Xu Chen, Zhi Zhou +2
Recent advances in artificial intelligence have driven increasing intelligent applications at the network edge, such as smart home, smart factory, and smart city. To deploy computa…