High-level synthesis with multi-objective genetic algorithm: A comparative encoding analysis

— The high-level synthesis process involves three interdependent and NP-complete optimization problems: (i) the operation scheduling, (ii) the resource allocation, and (iii) the controller synthesis. Evolutionary algorithms have been effectively applied to high level synthesis in presence conflicting design objectives for finding good tradeoffs in the design space. However, so far the design space exploration has been performed using single-objective evolutionary algorithms with an ad hoc fitness function to achieve the desired tradeoff between the objectives. Recently we proposed a framework based on multi-objective genetic algorithms to perform a fully automated design space exploration. In this paper we focus on the choice of the solution representations that can be used to perform the design space exploration with multi-objective genetic algorithms. In particular we consider two specific representations and compare them on a set of benchmark problems. Our results suggest that...

• Type: Conference
• Year: 2008
• Where: CEC
• Authors: Christian Pilato, Daniele Loiacono, Fabrizio Ferrandi, Pier Luca Lanzi, Donatella Sciuto
• Keywords: Artificial Intelligence, CEC 2008, Design Space Exploration, Evolutionary Algorithms, Multi-objective Genetic Algorithms