Name
Abstract | ||
|---|---|---|
Leveraging multithreading on embedded multicore platforms has been proven effective on handling the increasing resolutions of target stimuli of object detection. However, complex tradeoffs and correlated design impacts between a parallel application and the underlying multicore platform necessitate an effective and adaptable multithreaded design. This paper introduces a hybrid multithreaded object detection with high parallelism and extensive data reuse. A self adaptable flow is proposed to adjust the multithreaded object detection to fully exploit various embedded multicore architectures. The ARM-based cycle accurate simulations of multicore systems have shown the superior performance returned by the proposed design. Comprehensive design exploration for a multithreaded object detection algorithm.A Multi-Staged Classifier Grouping scheme to improve data reuse on the local cache.A self adaptable design flow to auto-tune design parameters for a multicore system.In-depth performance evaluation with an ARM-based cycle accurate simulator. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.jpdc.2015.01.005 | J. Parallel Distrib. Comput. |
Keywords | Field | DocType |
cache memories,face and gesture recognition,multiprocessor systems | Multithreading,Object detection,Computer architecture,Computer science,Parallel computing,Design flow,Exploit,Classifier (linguistics),Multi-core processor,Design exploration,Multicore systems | Journal |
Volume | Issue | ISSN |
78 | C | 0743-7315 |
Citations | PageRank | References |
3 | 0.39 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo-Cheng Charles Lai | 1 | 177 | 19.25 |
| Kun-Chun Li | 2 | 3 | 0.73 |
| Guan-Ru Li | 3 | 7 | 0.83 |
| Chih-Hsuan Chiang | 4 | 3 | 0.39 |