Efficient design space exploration of high performance embedded out-of-order processors

Previous work on efficient customized processor design primarily focused on in-order architectures. However, with the recent introduction of out-of-order processors for high-end high-performance embedded applications, researchers and designers need to address how to automate the design process of customized out-of-order processors. Because of the parallel execution of independent instructions in out-of-order processors, in-order processor design methodologies which subdivide the search space in independent components are unlikely to be effective in terms of accuracy for designing out-of-order processors. In this paper we propose and evaluate various automated single- and multi-objective optimizations for exploring out-of-order processor designs. We conclude that the newly proposed genetic local search algorithm outperforms all other search algorithms in terms of accuracy. In addition, we propose two-phase simulation in which the first phase explores the design space through statistical simulation; a region of interest is then simulated through detailed simulation in the second phase. We show that simulation time speedups can be obtained of a factor 2.2X to 7.3X using two-phase simulation.