Though recent years have witnessed remarkable progress in single image
super-resolution (SISR) tasks with the prosperous development of deep neural
networks (DNNs), the deep learning methods are confronted with the computation
and memory consumption issues in practice, especially for resource-limited
platforms such as mobile devices. To overcome the challenge and facilitate the
real-time deployment of SISR tasks on mobile, we combine neural architecture
search with pruning search and propose an automatic search framework that
derives sparse super-resolution (SR) models with high image quality while
satisfying the real-time inference requirement. To decrease the search cost, we
leverage the weight sharing strategy by introducing a supernet and decouple the
search problem into three stages, including supernet construction,
compiler-aware architecture and pruning search, and compiler-aware pruning
ratio search. With the proposed framework, we are the first to achieve
real-time SR inference (with only tens of milliseconds per frame) for
implementing 720p resolution with competitive image quality (in terms of PSNR
and SSIM) on mobile platforms (Samsung Galaxy S20)