Journal
ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS
Volume 16, Issue 2, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/1929943.1929946
Keywords
Design; Performance; Algorithms; Real-time; task scheduling; energy optimization; streaming applications; MPSoC; overhead-aware
Funding
- Research Grants Council of the Hong Kong Special Administrative Region, China [GRF PolyU 5260/07E, GRF PolyU 5269/08E]
- HK PolyU [A-PJ17]
- National Science Foundation of China [608031520]
- Ministry of Education, China [2009-144]
- Fundamental Research Fund for the Central Universities, China
- Direct For Computer & Info Scie & Enginr
- Division Of Computer and Network Systems [1015802] Funding Source: National Science Foundation
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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.
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