Name
Abstract | ||
|---|---|---|
Current mobile devices are unable to execute complex vision applications in a timely and power efficient manner without offloading some of the computation. This paper examines the tradeoffs that arise from executing some of the workload onboard and some remotely. Feature extraction and matching play an essential role in image classification and have the potential to be executed locally. Along with advances in mobile hardware, understanding the computation requirements of these applications is essential to realize their full potential in mobile environments. We analyze the ability of a mobile platform to execute feature extraction and matching, and prediction workloads under various scenarios. The best configuration for optimal runtime (11% faster) executes feature extraction with a GPU onboard and offloads the rest of the pipeline. Alternatively, compressing and sending the image over the network achieves lowest data transferred (2.5× better) and lowest energy usage (3.7× better) than the next best option. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6855235 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
feature extraction,image classification,image matching,mobile computing,GPU onboard,feature extraction,image matching,mobile devices,mobile environments,offloading mobile image classification,energy management,image classification,mobile computing,offloading | Mobile computing,Energy management,Power efficient,Computer science,Workload,Real-time computing,Feature extraction,Mobile device,Contextual image classification,Computation | Conference |
ISSN | Citations | PageRank |
1520-6149 | 12 | 0.69 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hauswald, J. | 1 | 12 | 0.69 |
| Manville, T. | 2 | 12 | 0.69 |
| Zheng, Q. | 3 | 12 | 0.69 |
| Dreslinski, R. | 4 | 43 | 2.37 |