Abstract

In the domain of electronic design automation (EDA), the significance of design space exploration (DSE) tools is increasingly recognized for their adeptness in optimizing performance, power, and area (PPA) in integrated circuits (ICs) via advanced parameter configuration. Conventional EDA exploration approaches tend to focus primarily on identifying optimal result points, frequently disregarding the nuances of the parameter space's attributes. This oversight can significantly influence the quality of the sought-after optimal PPA results. Typically, it is presumed that an ideal parameter space usually manifests uniform distribution across each parameter. However, this uniformity in the parameter space does not necessarily assure the discovery of superior pareto points. To address this, our study introduces a novel method called Shuffled Iterative Hierarchical Minimixing (SIHM) that it can incorporate the reduced sampling within a uniformly constructed parameter space, supplemented by mixed superposition with noise. This strategy could notably enhance the quality of pareto points within the PPA results. Our experimental findings reveal that this approach has the ability to yield improved pareto points with a data sampling ratio as low as approximately 1:96 during each parameter, while maintaining exceptionally low noise levels. Additionally, our pareto points show a 3.9% decrease at least in the minimum value within one of the dimensional space in the PPA results, compared to pareto points derived from uniformly distributed parameter space.