Overhead-aware energy optimization for real-time streaming applications on multiprocessor System-on-Chip

  • Authors:
  • Yi Wang;Hui Liu;Duo Liu;Zhiwei Qin;Zili Shao;Edwin H.-M. Sha

  • Affiliations:
  • Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Xidian University, XI'AN, China;Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Hunan University and The University of Texas at Dallas, Richardson, TX

  • Venue:
  • ACM Transactions on Design Automation of Electronic Systems (TODAES)
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

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.