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

• URL: http://arxiv.org/abs/2203.16528v1
• Date: Wed, 30 Mar 2022 17:58:51 GMT
• Title: L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors
• Abstract summary: 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 L3U-net achieves more than 90% accuracy over two different segmentation datasets with 10 fps.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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