Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report

• URL: http://arxiv.org/abs/2105.07825v1
• Date: Mon, 17 May 2021 13:34:15 GMT
• Title: Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
• Authors: Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Andrew Lek, Mustafa Ayazoglu, Jie Liu, Zongcai Du, Jiaming Guo, Xueyi Zhou, Hao Jia, Youliang Yan, Zexin Zhang, Yixin Chen, Yunbo Peng, Yue Lin, Xindong Zhang, Hui Zeng, Kun Zeng, Peirong Li, Zhihuang Liu, Shiqi Xue, Shengpeng Wang
• Abstract summary: We introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image super-resolution solution. The proposed solutions are fully compatible with all major mobile AI accelerators and are capable of reconstructing Full HD images under 40-60 ms.
• Score: 67.86837649834636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image super-resolution is one of the most popular computer vision problems with many important applications to mobile devices. While many solutions have been proposed for this task, they are usually not optimized even for common smartphone AI hardware, not to mention more constrained smart TV platforms that are often supporting INT8 inference only. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop an end-to-end deep learning-based image super-resolution solutions that can demonstrate a real-time performance on mobile or edge NPUs. For this, the participants were provided with the DIV2K dataset and trained quantized models to do an efficient 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated NPU capable of accelerating quantized neural networks. The proposed solutions are fully compatible with all major mobile AI accelerators and are capable of reconstructing Full HD images under 40-60 ms while achieving high fidelity results. A detailed description of all models developed in the challenge is provided in this paper.

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