  Single-Image Super Resolution (SISR) is a classical computer vision problem
and it has been studied for over decades. With the recent success of deep
learning methods, recent work on SISR focuses solutions with deep learning
methodologies and achieves state-of-the-art results. However most of the
state-of-the-art SISR methods contain millions of parameters and layers, which
limits their practical applications. In this paper, we propose a hardware
(Synaptics Dolphin NPU) limitation aware, extremely lightweight quantization
robust real-time super resolution network (XLSR). The proposed model's building
block is inspired from root modules for Image classification. We successfully
applied root modules to SISR problem, further more to make the model uint8
quantization robust we used Clipped ReLU at the last layer of the network and
achieved great balance between reconstruction quality and runtime. Furthermore,
although the proposed network contains 30x fewer parameters than VDSR its
performance surpasses it on Div2K validation set. The network proved itself by
winning Mobile AI 2021 Real-Time Single Image Super Resolution Challenge.
