A hybrid task mapping algorithm for heterogeneous MPSoCs

Wei Quan*, Andy D. Pimentel

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


The application workloads in modern MPSoC-based embedded systems are becoming increasingly dynamic. Different applications concurrently execute and contend for resources in such systems, which could cause serious changes in the intensity and nature of the workload demands over time. To cope with the dynamism of application workloads at runtime and improve the efficiency of the underlying system architecture, this article presents a hybrid task mapping algorithm that combines a static mapping exploration and a dynamic mapping optimization to achieve an overall improvement of system efficiency. We evaluate our algorithm using a heterogeneous MPSoC system with three real applications. Experimental results reveal the effectiveness of our proposed algorithm by comparing derived solutions to the ones obtained from several other runtime mapping algorithms. In test cases with three simultaneously active applications, the mapping solutions derived by our approach have average performance improvements ranging from 45.9% to 105.9% and average energy savings ranging from 14.6% to 23.5%.

Original languageEnglish
Article number14
JournalACM Transactions on Embedded Computing Systems
Issue number1
Publication statusPublished - 1 Jan 2015
Externally publishedYes


  • Embedded systems
  • KPN
  • MPSoC
  • Simulation
  • Task mapping


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