Multi-objective optimization of multimedia embedded systems using genetic algorithms and stochastic simulation

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.