9 citations · 10 across the 4 of their papers we have counts for
4 papers
Minimizing End-to-End Latency for Joint Source-Channel Coding Systems
Kaiyi Chi, Qianqian Yang, Yuanchao Shu +2
While existing studies have highlighted the advantages of deep learning (DL)-based joint source-channel coding (JSCC) schemes in enhancing transmission efficiency, they often overl…
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
Yuhao Chen, Yuxuan Yan, Qianqian Yang +4
It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applica…
Turbo: Opportunistic Enhancement for Edge Video Analytics
Yan Lu, Shiqi Jiang, Ting Cao +1
Edge computing is being widely used for video analytics. To alleviate the inherent tension between accuracy and cost, various video analytics pipelines have been proposed to optimi…
GEMEL: Model Merging for Memory-Efficient, Real-Time Video Analytics at the Edge
Arthi Padmanabhan, Neil Agarwal, Anand Iyer +5
Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth overheads and privacy violations, but in doing so, face an ever-growing resource tension. Mo…