  Along with the rapid development of real-world applications, higher
requirements on the accuracy and efficiency of image super-resolution (SR) are
brought forward. Though existing methods have achieved remarkable success, the
majority of them demand plenty of computational resources and large amount of
RAM, and thus they can not be well applied to mobile device. In this paper, we
aim at designing efficient architecture for 8-bit quantization and deploy it on
mobile device. First, we conduct an experiment about meta-node latency by
decomposing lightweight SR architectures, which determines the portable
operations we can utilize. Then, we dig deeper into what kind of architecture
is beneficial to 8-bit quantization and propose anchor-based plain net (ABPN).
Finally, we adopt quantization-aware training strategy to further boost the
performance. Our model can outperform 8-bit quantized FSRCNN by nearly 2dB in
terms of PSNR, while satisfying realistic needs at the same time. Code is
avaliable at https://github.com/NJU- Jet/SR_Mobile_Quantization.
