37 citations · 98 across the 11 of their papers we have counts for
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cs.DC2022★ 9 cited
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…
cs.DC2020★ 21 cited
Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers
Romil Bhardwaj, Zhengxu Xia, Ganesh Ananthanarayanan +6
Video analytics applications use edge compute servers for the analytics of the videos (for bandwidth and privacy). Compressed models that are deployed on the edge servers for infer…