L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors
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Abstract (arXiv)
In this research, we propose a tiny image segmentation model, L^3U-net, that works on low-resource edge devices in real-time. We introduce a data folding technique that reduces inference latency by leveraging the parallel convolutional layer processing capability of the CNN accelerators. We also deploy the proposed model to such a device, MAX78000, and the results show that L^3U-net achieves more than 90% accuracy over two different segmentation datasets with 10 fps.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 23 of its 34 references that are in the map. First posted on arXiv 30 Mar 2022.
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23 of 34 references.
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11 more
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