L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors

Osman Erman Okman; Mehmet Gorkem Ulkar; Gulnur Selda Uyanik

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arXiv · 2203.16528

L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors

Osman Erman Okman·Mehmet Gorkem Ulkar·Gulnur Selda Uyanik

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Abstract

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.

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BibTeX RIS

Osman Erman Okman, Mehmet Gorkem Ulkar, Gulnur Selda Uyanik. 2022-03-30. L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors. https://arxiv.org/abs/2203.16528

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