论文标题
具有深度学习，移动AI和AIM 2022挑战的移动NPU的功率高效视频超分辨率：报告
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
论文作者
论文摘要
视频超分辨率是移动设备上最受欢迎的任务之一，被广泛用于自动改进低焦点和低分辨率视频流。尽管已经针对此问题提出了许多解决方案，但它们通常在计算方面要求很高，表明FPS速率较低和移动设备上的功率效率。在此移动AI挑战中，我们解决了此问题，并建议参与者设计一种端到端的实时视频超分辨率解决方案，以优化用于低能消耗的移动NPU。为参与者提供了REDS培训数据集，其中包含4倍视频升级任务的视频序列。在功能强大的Mediatek Dimenty 9000平台上评估了所有模型的运行时和功率效率，该平台具有专用的AI处理单元，能够加速浮点和量化神经网络。所有提出的解决方案都与上述NPU完全兼容，表明高达500 fps的速率和0.2 [WATT / 30 fps]功耗。本文提供了挑战中所有模型的详细描述。
Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While numerous solutions have been proposed for this problem, they are usually quite computationally demanding, demonstrating low FPS rates and power efficiency on mobile devices. In this Mobile AI challenge, we address this problem and propose the participants to design an end-to-end real-time video super-resolution solution for mobile NPUs optimized for low energy consumption. The participants were provided with the REDS training dataset containing video sequences for a 4X video upscaling task. The runtime and power efficiency of all models was evaluated on the powerful MediaTek Dimensity 9000 platform with a dedicated AI processing unit capable of accelerating floating-point and quantized neural networks. All proposed solutions are fully compatible with the above NPU, demonstrating an up to 500 FPS rate and 0.2 [Watt / 30 FPS] power consumption. A detailed description of all models developed in the challenge is provided in this paper.
