Rethinking Sparsity in Performance Modeling for Analog and Mixed Circuits using Spike and Slab Models

As integrated circuit technologies continue to scale, efficient performance modeling becomes indispensable. Recently, several new learning paradigms have been proposed to reduce the computational cost associated with accurate performance modeling. A common attribute among most of these paradigms is the leverage of the sparsity feature to build efficient performance models. In this work, we propose a new perspective to incorporate sparsity in the modeling task by utilizing spike and slab feature selection techniques. Practically, our proposed method uses two different priors on the different model coefficients based on their importance. This is incorporated into a mixture model that can be built using a hierarchical Bayesian framework to select the important features and find the model coefficients. Our numerical experiments demonstrate that the proposed approach can achieve better results compared to traditional sparse modeling techniques while also providing valuable insight about the important features in the model.