Multi-objective design space exploration using genetic algorithms

  • Authors:
  • Maurizio Palesi;Tony Givargis

  • Affiliations:
  • University of Catania, V.le Andrea Doria, 6, Catania, Italy;UC Irvine, Irvine, CA

  • Venue:
  • Proceedings of the tenth international symposium on Hardware/software codesign
  • Year:
  • 2002

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Abstract

In this work, we provide a technique for efficiently exploring a parameterized system-on-a-chip (SoC) architecture to find all Pareto-optimal configurations in a multi-objective design space. Globally, our approach uses a parameter dependency model of our target parameterized SoC architecture to extensively prune non-optimal sub-spaces. Locally, our approach applies Genetic Algorithms (GAs) to discover Pareto-optimal configurations within the remaining design points. The computed Pareto-optimal configurations will represent the range of performance (e.g., timing and power) tradeoffs that are obtainable by adjusting parameter values for a fixed application that is mapped on the parameterized SoC architecture. We have successfully applied our technique to explore Pareto-optimal configurations for a number of applications mapped on a parameterized SoC architecture.