Fugu-MT 論文翻訳(概要): Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

論文の概要: Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

arxiv url: http://arxiv.org/abs/2211.05910v1
Date: Mon, 7 Nov 2022 22:27:58 GMT
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システム内更新日: 2022-11-20 13:32:49.701107
Title: Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
Title（参考訳）: モバイルnpus, mobile ai & aim 2022 チャレンジ : 効率的・高精度量子化画像超解像
Authors: Andrey Ignatov and Radu Timofte and Maurizio Denna and Abdel Younes and Ganzorig Gankhuyag and Jingang Huh and Myeong Kyun Kim and Kihwan Yoon and Hyeon-Cheol Moon and Seungho Lee and Yoonsik Choe and Jinwoo Jeong and Sungjei Kim and Maciej Smyl and Tomasz Latkowski and Pawel Kubik and Michal Sokolski and Yujie Ma and Jiahao Chao and Zhou Zhou and Hongfan Gao and Zhengfeng Yang and Zhenbing Zeng and Zhengyang Zhuge and Chenghua Li and Dan Zhu and Mengdi Sun and Ran Duan and Yan Gao and Lingshun Kong and Long Sun and Xiang Li and Xingdong Zhang and Jiawei Zhang and Yaqi Wu and Jinshan Pan and Gaocheng Yu and Jin Zhang and Feng Zhang and Zhe Ma and Hongbin Wang and Hojin Cho and Steve Kim and Huaen Li and Yanbo Ma and Ziwei Luo and Youwei Li and Lei Yu and Zhihong Wen and Qi Wu and Haoqiang Fan and Shuaicheng Liu and Lize Zhang and Zhikai Zong and Jeremy Kwon and Junxi Zhang and Mengyuan Li and Nianxiang Fu and Guanchen Ding and Han Zhu and Zhenzhong Chen and Gen Li and Yuanfan Zhang and Lei Sun and Dafeng Zhang and Neo Yang and Fitz Liu and Jerry Zhao and Mustafa Ayazoglu and Bahri Batuhan Bilecen and Shota Hirose and Kasidis Arunruangsirilert and Luo Ao and Ho Chun Leung and Andrew Wei and Jie Liu and Qiang Liu and Dahai Yu and Ao Li and Lei Luo and Ce Zhu and Seongmin Hong and Dongwon Park and Joonhee Lee and Byeong Hyun Lee and Seunggyu Lee and Se Young Chun and Ruiyuan He and Xuhao Jiang and Haihang Ruan and Xinjian Zhang and Jing Liu and Garas Gendy and Nabil Sabor and Jingchao Hou and Guanghui He
Abstract要約: 本稿では,高効率な量子化画像超解法を設計するための参加者を提案する。このソリューションはモバイルNPU上でリアルタイムのパフォーマンスを示すことができる。全てのモデルのランタイムは、専用のエッジNPUを備えたSynaptics VS680 Smart Homeボードで評価された。
参考スコア（独自算出の注目度）: 144.41960648643564
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints. In this Mobile AI challenge, we address this problem and propose the participants to design an efficient quantized image super-resolution solution that can demonstrate a real-time performance on mobile NPUs. The participants were provided with the DIV2K dataset and trained INT8 models to do a high-quality 3X image upscaling. The runtime of all models was evaluated on the Synaptics VS680 Smart Home board with a dedicated edge NPU capable of accelerating quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 60 FPS rate when reconstructing Full HD resolution images. A detailed description of all models developed in the challenge is provided in this paper.
Abstract（参考訳）: 画像の超解像度は、モバイルデバイスやIoTデバイスで一般的なタスクであり、低解像度の画像やビデオフレームをスケールアップして拡張する必要があることが多い。この問題に対する多くの解決策が提案されてきたが、通常は計算やメモリの制約の多い低消費電力モバイルNPUと互換性がない。このモバイルAIチャレンジでは、この問題に対処し、モバイルNPU上でリアルタイムのパフォーマンスを実演できる効率的な量子化画像超解ソリューションを設計する参加者を提案する。参加者はDIV2KデータセットとINT8モデルをトレーニングし、高品質な3Xイメージアップスケーリングを実現した。全てのモデルのランタイムをSynaptics VS680 Smart Homeボード上で評価し、量子化されたニューラルネットワークを高速化する専用エッジNPUを使用した。提案手法はすべて上記のNPUと完全に互換性があり、フルHD解像度画像の再構成時に最大60FPSの速度を示す。本論文では,本課題で開発されたモデルについて詳述する。

論文の概要: Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

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