  Deep learning-based single image super-resolution (SISR) approaches have
drawn much attention and achieved remarkable success on modern advanced GPUs.
However, most state-of-the-art methods require a huge number of parameters,
memories, and computational resources, which usually show inferior inference
times when applying them to current mobile device CPUs/NPUs. In this paper, we
propose a simple plain convolution network with a fast nearest convolution
module (NCNet), which is NPU-friendly and can perform a reliable
super-resolution in real-time. The proposed nearest convolution has the same
performance as the nearest upsampling but is much faster and more suitable for
Android NNAPI. Our model can be easily deployed on mobile devices with 8-bit
quantization and is fully compatible with all major mobile AI accelerators.
Moreover, we conduct comprehensive experiments on different tensor operations
on a mobile device to illustrate the efficiency of our network architecture.
Our NCNet is trained and validated on the DIV2K 3x dataset, and the comparison
with other efficient SR methods demonstrated that the NCNet can achieve high
fidelity SR results while using fewer inference times. Our codes and pretrained
models are publicly available at \url{https://github.com/Algolzw/NCNet}.
