A multi-objective genetic algorithm framework for design space exploration of reliable FPGA-based systems

This paper presents a framework for the design space exploration of reliable FPGA systems based on a multi-objective genetic algorithm (NSGA-II). The framework takes into account several design metrics and outputs a set of Pareto-optimal design solutions. The framework is compared to the multi-objective version of simulated annealing (AMOSA) and it is empirically studied in terms of scalability using three real-world circuits and a set of synthetic problems of different sizes. Our results show that the proposed approach generates a rich set of Pareto-optimal solutions whereas AMOSA tends to find suboptimal solutions. Our empirical scalability analysis shows that, while the problem space is exponential in the number n of functional units constituting the system, the number of evaluations required by our framework grows as O(n
^3.6
).