Xu, X.-X., Hu, X.-M., Chen, W.-N. and Li, Y. (2016) Set-based particle swarm optimization for mapping and scheduling tasks on heterogeneous embedded systems. In: 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI), Chiang Mai, Thailand, 14-16 Feb 2016, pp. 318-325. ISBN 9781467377805 (doi: 10.1109/ICACI.2016.7449845)
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118965.pdf - Accepted Version 958kB |
Abstract
Modern heterogeneous multiprocessor embedded platforms is important for the high volume markets that have strict performance. However, it presents many challenges that need to be addressed in order to be efficiently utilized for multitask applications. Since mapping and scheduling problems for multi processors belong to the classic of NP-Complete problems, common methods used to solve this kind of problem usually fail. In this paper, we present an algorithm based on the meta-heuristic optimization technique, set-based discrete particle swarm optimization (S-PSO), which efficiently solves scheduling and mapping problems on the target platform. This algorithm can simultaneously addressed the mapping and scheduling problems on a complex and heterogeneous MPSoC and it has better performance than other algorithms in dealing with large scale problems. This algorithm also reduces the execution time of the application by exploring various solutions for mapping and scheduling of tasks and communications. We compare our approach with other heuristics, Ant Colony Optimization (ACO), on the performance to reach the optimum value and on the potential to explore the target platform. The results show that our approach performs better than other heuristics.
| Item Type: | Conference Proceedings |
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| Additional Information: | This work was supported in part by the NSFC projects Nos. 61379061, 61332002, 61511130078, 61202130, in part by Natural Science Foundation of Gungdong for Distinguished Young Scholars No. 2015A030306024, in part by the “Guangdong Special Support Program” No. 2014TQ01X550, and in part by the Guangzhou Pearl River New Star of Science and Technology No. 201506010002. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Li, Professor Yun |
| Authors: | Xu, X.-X., Hu, X.-M., Chen, W.-N., and Li, Y. |
| College/School: | College of Science and Engineering > School of Engineering > Systems Power and Energy |
| ISBN: | 9781467377805 |
| Copyright Holders: | Copyright © 2016 IEEE |
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