Real-Time Quantized Image Super-Resolution on Mobile NPUs, Mobile AI 2021 Challenge: Report
METADATA ONLY
Loading...
Author / Producer
Date
2021
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
METADATA ONLY
Data
Rights / License
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. © 2021 IEEE
Permanent link
Publication status
published
External links
Editor
Book title
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Journal / series
Volume
Pages / Article No.
2525 - 2534
Publisher
IEEE
Event
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2021)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03514 - Van Gool, Luc (emeritus) / Van Gool, Luc (emeritus)