论文标题
l^3u-net：平行CNN处理器的低延期轻量级U-NET图像分割模型
L^3U-net: Low-Latency Lightweight U-net Based Image Segmentation Model for Parallel CNN Processors
论文作者
论文摘要
在这项研究中，我们提出了一个微小的图像分割模型l^3U-net，该模型可实时可用于低资源边缘设备。我们引入了一种数据折叠技术，该技术通过利用CNN加速器的平行卷积层处理能力来降低推理潜伏期。我们还将提出的模型部署到此类设备Max78000上，结果表明，L^3U-NET在两个不同的分段数据集的10 fps上实现了90％以上的精度。
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.
