.png){kind=icon}
.png){kind=icon}
Return
Multi-objective optimization of multimedia embedded systems using genetic algorithms and stochastic simulation
.png){kind=icon}
PRE
AI
DOI：10.1007/s00500-016-2061-x.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
To meet the ever shrinking time-to-market for multimedia embedded systems, designers need effective system-level optimization techniques to support their design decisions. Despite multimedia embedded systems' highly variable execution times and soft real-time constraints, most previous work has adopted a constant execution time (worst-case) approach to evaluate if a candidate architecture satisfies the timing constraints. Such an approach is too pessimistic and might result in unnecessary costly architectures. In this work, we propose a new method for design space exploration of multimedia embedded systems. Given a system specification, the proposed method automatically explores the design space to quickly identify Pareto-optimal solutions (or an approximation) that optimize conflicting design metrics, such as price and power consumption. Our approach combines (i) a fast and formal strategy for performance evaluation that captures the varying runtime behavior of multimedia systems and (ii) a new multi-objective genetic algorithm for architecture exploration. The experiments on well-known benchmarks show the efficiency of our method in comparison to similar ones.
Keywords:
Multimedia embedded systems
Simulation
Architecture exploration
Genetic algorithms
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.5
Papers:
1.0W
Citations:
2.1W