Set-based particle swarm optimization for mapping and scheduling tasks on heterogeneous embedded systems

Xiao Xiao Xu, Xiao Min Hu*, Wei Neng Chen, Yun Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016
Pages318-325
Number of pages8
DOIs
Publication statusPublished - 7 Apr 2016
Event8th International Conference on Advanced Computational Intelligence, ICACI 2016 - Chiang Mai, Thailand
Duration: 14 Feb 201616 Feb 2016

Conference

Conference8th International Conference on Advanced Computational Intelligence, ICACI 2016
Country/TerritoryThailand
CityChiang Mai
Period14/02/1616/02/16

Keywords

  • communications
  • mapping
  • scheduling
  • set-based discrete particle swarm optimization (DPSO)

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