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. 2023 Jun 27;13:10391. doi: 10.1038/s41598-023-34436-w

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© The Author(s) 2023

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Inference latency results for (a) SimpleCNN and (b) UNet running on various edge devices connected to an NVIDIA Jetson Nano. We compare models accelerated in the Myriad-X chip, in the Coral EdgeTPU and using CUDA on the NVIDIA Jetson Nano (NVIDIA Maxwell with 128 CUDA cores). Results in Jetson Nano ARM CPU are ommited for UNet as they are an order of magnitude slower. Results are reported as tiles processed per second. While larger tiles may take longer to process, fewer tiles are required for a given image size. Where results for a particular framework and/or device are missing, a model was not available at that tile size due to memory limitations.