Multi-objective design space exploration using genetic algorithms

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 subspaces. 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.