Multi-objective DSE algorithms' evaluations on processor optimization

Very complex micro-architectures, like complex superscalar/SMT or multicore systems, have lots of configurations. Exploring this huge design space and trying to optimize multiple objectives, like performance, power consumption and hardware complexity is a real challenge. In this paper, using the multi-objective design space exploration tool FADSE, we tried to optimize the hardware parameters of the complex superscalar Grid ALU Processor. We compared how different heuristic algorithms handle the DSE optimization. Three of these algorithms are taken from the jMetal library (NSGAII, SPEA2 and SMPSO) while the other two, CNSGAII and MOHC were implemented by us. We show that in this huge design space the differences between the best found individuals by every algorithm are very small, only the time in which they got to these solutions differs. In order to accelerate the DSE process we also did a feature selection through machine learning techniques and ran all DSE algorithms again with a smaller number of input parameters.