Paper Info
Title
Multi-objective optimization of multimedia embedded systems using genetic algorithms and stochastic simulation.
Abstract
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.
Year
DOI
Venue
2017
10.1007/s00500-016-2061-x
Soft Comput.
Keywords
Field
DocType
Multimedia embedded systems, Simulation, Architecture exploration, Genetic algorithms
Stochastic simulation,Multimedia embedded systems,Mathematical optimization,Architecture,Computer science,MULTICUBE,Theoretical computer science,Multi-objective optimization,System requirements specification,Design space exploration,Genetic algorithm
Journal
Volume
Issue
ISSN
21
14
1433-7479
Citations 
PageRank 
References 
2
0.37
28
Authors
5
Name
Order
Citations
PageRank
Bruno Nogueira1687.67
Paulo Romero Martins Maciel236359.24
Eduardo Tavares316125.22
Ricardo M. A. Silva4659.02
Ermeson Andrade59712.29