Toward efficient large-scale performance modeling of integrated circuits via multi-mode/multi-corner sparse regression

In this paper, we propose a novel multi-mode/multi-corner sparse regression (MSR) algorithm to build large-scale performance models of integrated circuits at multiple working modes and environmental corners. Our goal is to efficiently extract multiple performance models to cover different modes/corners with a small number of simulation samples. To this end, an efficient Bayesian inference with shared prior distribution (i.e., model template) is developed to explore the strong performance correlation among different modes/corners in order to achieve high modeling accuracy with low computational cost. Several industrial circuit examples demonstrate that the proposed MSR achieves up to 185× speedup over least-squares regression [14] and 6.7× speedup over least-angle regression [7] without surrendering any accuracy.