Overhead-aware energy optimization for real-time streaming applications on multiprocessor system-on-chip

Yi Wang, Hui Liu, Duo Liu, Zhiwei Qin, Zili Shao, Edwin H.M. Sha

Research output: Journal article publicationJournal articleAcademic researchpeer-review

53 Citations (Scopus)

Abstract

In this article, we focus on solving the energy optimization problem for real-time streaming applications on multiprocessor System-on-Chip by combining task-level coarse-grained software pipelining with DVS (Dynamic Voltage Scaling) and DPM (Dynamic Power Management) considering transition overhead, inter-core communication and discrete voltage levels. We propose a two-phase approach to solve the problem. In the first phase, we propose a coarse-grained task parallelization algorithm called RDAG to transform a periodic dependent task graph into a set of independent tasks by exploiting the periodic feature of streaming applications. In the second phase, we propose a scheduling algorithm, GeneS, to optimize energy consumption. GeneS is a genetic algorithm that can search and find the best schedule within the solution space generated by gene evolution. We conduct experiments with a set of benchmarks from E3S and TGFF. The experimental results show that our approach can achieve a 24.4% reduction in energy consumption on average compared with the previous work.
Original languageEnglish
Article number14
JournalACM Transactions on Design Automation of Electronic Systems
Volume16
Issue number2
DOIs
Publication statusPublished - 1 Mar 2011

Keywords

  • Energy optimization
  • MPSoC
  • Overhead-aware
  • Real-time
  • Streaming applications
  • Task scheduling

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering

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