Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/32189
Title: Overhead-aware energy optimization for real-time streaming applications on multiprocessor system-on-chip
Authors: Wang, Y
Liu, H
Liu, D
Qin, Z
Shao, Z 
Sha, EH
Keywords: Energy optimization
MPSoC
Overhead-aware
Real-time
Streaming applications
Task scheduling
Issue Date: 2011
Publisher: Association for Computing Machinary
Source: ACM transactions on design automation of electronic systems, 2011, v. 16, no. 2, 14 How to cite?
Journal: ACM transactions on design automation of electronic systems 
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.
URI: http://hdl.handle.net/10397/32189
ISSN: 1084-4309
DOI: 10.1145/1929943.1929946
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