Paper Info
Title
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors
Abstract
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.
Year
DOI
Venue
2019
10.1145/3300061.3345455
MobiCom '19: The 25th Annual International Conference on Mobile Computing and Networking Los Cabos Mexico October, 2019
Keywords
DocType
ISBN
Super-resolution,deep neural networks,mobile computing,heterogeneous computing,scheduling
Conference
978-1-4503-6169-9
Citations 
PageRank 
References 
8
0.48
13
Authors
5
Name
Order
Citations
PageRank
Royson Lee182.85
Stylianos I. Venieris210612.98
Łukasz Dudziak3174.37
Sourav Bhattacharya462452.45
Nicholas D. Lane54247248.15