Abstract—In evolutionary high-level synthesis, design solutions have to be evaluated to extract information about some figures of merit (such as performance, area, etc.) and to allow the genetic algorithm to evolve and converge to Pareto-optimal solutions. Since the execution time of such evaluations increases with the complexity of the specification, this could lead to unacceptable execution time of the overall methodology. This paper presents a model to exploit fitness inheritance in a multiobjective optimization algorithm (i.e. NSGA-II [1]) by substituting the expensive real evaluations with an estimation based on neighbors in an hypothetical design space. The estimations are based on a measure of distance between individuals and a weighted average on fitnesses of closer ones. The results shows that the Pareto-optimal set obtained by applying the proposed model good approximates the set obtained without fitness inheritance and overall execution time is reduced more than 25% in average.

1. Fitness Inheritance in Evolutionary and Multi-Objective High-Level Synthesis Christian Pilato, Gianluca Palermo, Antonino Tumeo, Fabrizio Ferrandi, Donatella Sciuto, Pier Luca Lanzi

5. The Proposed Methodology Fitness is computed using a full synthesis flow Individuals encode information to perform an entire synthesis cycle

14. EWF (Pareto)‏

15. DCT (Pareto)‏

16. RGBtoYUVx4 (Pareto solutions with and without inheritance)‏

17. Overall execution time/chromosome size

18. Fitness evaluation time generation by generation (EWF)‏

19. Fitness evaluation time generation by generation (RGBtoYUVx4)‏