activity
20202022
most citedDynamic GPU Energy Optimization for Machine Learning Training Workloads

43 citations · 52 across the 3 of their papers we have counts for

collaborators

5 papers

cs.DC202243 cited

Dynamic GPU Energy Optimization for Machine Learning Training Workloads

Farui Wang, Weizhe Zhang, Shichao Lai +2

GPUs are widely used to accelerate the training of machine learning workloads. As modern machine learning models become increasingly larger, they require a longer time to train, le…

cs.LG20218 cited

Auction Based Clustered Federated Learning in Mobile Edge Computing System

Renhao Lu, Weizhe Zhang, Qiong Li +2

In recent years, mobile clients' computing ability and storage capacity have greatly improved, efficiently dealing with some applications locally. Federated learning is a promising…

cs.IR20201 cited

Double-Wing Mixture of Experts for Streaming Recommendations

Yan Zhao, Shoujin Wang, Yan Wang +2

Streaming Recommender Systems (SRSs) commonly train recommendation models on newly received data only to address user preference drift, i.e., the changing user preferences towards…

cs.NI2020

OODT: Obstacle Aware Opportunistic Data Transmission for Cognitive Radio Ad Hoc Networks

Xiaoxiong Zhong, Li Li, Yuanping Zhang +3

In recent years, a large number of smart devices will be connected in Internet of Things (IoT) using an ad hoc network, which needs more frequency spectra. The cognitive radio (CR)…

cs.NI2020

CL-ADMM: A Cooperative Learning Based Optimization Framework for Resource Management in MEC

Xiaoxiong Zhong, Xinghan Wang, Li Li +5

We consider the problem of intelligent and efficient resource management framework in mobile edge computing (MEC), which can reduce delay and energy consumption, featuring distribu…