Computer Science > Computer Vision and Pattern Recognition
arXiv:2208.11666 (cs)
[Submitted on 24 Aug 2022]
Title:Efficient Heterogeneous Video Segmentation at the Edge
Authors:Jamie Menjay Lin,Siargey Pisarchyk,Juhyun Lee,David Tian,Tingbo Hou,Karthik Raveendran,Raman Sarokin,George Sung,Trent Tolley,Matthias Grundmann
View a PDF of the paper titled Efficient Heterogeneous Video Segmentation at the Edge, by Jamie Menjay Lin and 9 other authors
View PDFAbstract:We introduce an efficient video segmentation system for resource-limited edge devices leveraging heterogeneous compute. Specifically, we design network models by searching across multiple dimensions of specifications for the neural architectures and operations on top of already light-weight backbones, targeting commercially available edge inference engines. We further analyze and optimize the heterogeneous data flows in our systems across the CPU, the GPU and the NPU. Our approach has empirically factored well into our real-time AR system, enabling remarkably higher accuracy with quadrupled effective resolutions, yet at much shorter end-to-end latency, much higher frame rate, and even lower power consumption on edge platforms.
| Comments: | Published as a workshop paper at CVPRW CV4ARVR 2022 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2208.11666 [cs.CV] |
| (orarXiv:2208.11666v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2208.11666 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Efficient Heterogeneous Video Segmentation at the Edge, by Jamie Menjay Lin and 9 other authors
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