  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.
