  There is a constant need for high-performing and computationally efficient
neural network models for image super-resolution: computationally efficient
models can be used via low-capacity devices and reduce carbon footprints. One
way to obtain such models is to compress models, e.g. quantization. Another way
is a neural architecture search that automatically discovers new, more
efficient solutions. We propose a novel quantization-aware procedure, the
QuantNAS that combines pros of these two approaches. To make QuantNAS work, the
procedure looks for quantization-friendly super-resolution models. The approach
utilizes entropy regularization, quantization noise, and Adaptive Deviation for
Quantization (ADQ) module to enhance the search procedure. The entropy
regularization technique prioritizes a single operation within each block of
the search space. Adding quantization noise to parameters and activations
approximates model degradation after quantization, resulting in a more
quantization-friendly architectures. ADQ helps to alleviate problems caused by
Batch Norm blocks in super-resolution models. Our experimental results show
that the proposed approximations are better for search procedure than direct
model quantization. QuantNAS discovers architectures with better PSNR/BitOps
trade-off than uniform or mixed precision quantization of fixed architectures.
We showcase the effectiveness of our method through its application to two
search spaces inspired by the state-of-the-art SR models and RFDN. Thus, anyone
can design a proper search space based on an existing architecture and apply
our method to obtain better quality and efficiency.
  The proposed procedure is 30\% faster than direct weight quantization and is
more stable.
