  In recent years, convolutional networks have demonstrated unprecedented
performance in the image restoration task of super-resolution (SR). SR entails
the upscaling of a single low-resolution image in order to meet
application-specific image quality demands and plays a key role in mobile
devices. To comply with privacy regulations and reduce the overhead of cloud
computing, executing SR models locally on-device constitutes a key alternative
approach. Nevertheless, the excessive compute and memory requirements of SR
workloads pose a challenge in mapping SR networks on resource-constrained
mobile platforms. This work presents MobiSR, a novel framework for performing
efficient super-resolution on-device. Given a target mobile platform, the
proposed framework considers popular model compression techniques and traverses
the design space to reach the highest performing trade-off between image
quality and processing speed. At run time, a novel scheduler dispatches
incoming image patches to the appropriate model-engine pair based on the
patch's estimated upscaling difficulty in order to meet the required image
quality with minimum processing latency. Quantitative evaluation shows that the
proposed framework yields on-device SR designs that achieve an average speedup
of 2.13x over highly-optimized parallel difficulty-unaware mappings and 4.79x
over highly-optimized single compute engine implementations.
