GPSCP: A general-purpose support-circuit preconditioning approach to large-scale SPICE-accurate nonlinear circuit simulations

To improve the efficiency of direct solution methods in SPICE-accurate nonlinear circuit simulations, preconditioned iterative solution techniques have been widely studied in the past decades. However, it still has been an extremely challenging task to develop general-purpose preconditioning methods that can deal with various large-scale nonlinear circuit simulations. In this work, a novel circuit-oriented, generalpurpose support-circuit preconditioning technique (GPSCP) is proposed to significantly improve the matrix solving time and reduce the memory consumption during large-scale nonlinear circuit simulations. We show that by decomposing the system Jacobian matrix at a given solution point into a graph Laplacian matrix as well as a matrix including all voltage and controlled sources, and subsequently sparsifying the graph Laplacian matrix based on support graph theory, the general-purpose support-circuit preconditioning matrix can be efficiently obtained, thereby serving as a very effective and efficient preconditioner in solving the original Jacobian matrix through Krylov-subspace iterations. Additionally, a novel critical node selection method and an energy-based spanning-graph scaling method have been proposed to further improve the quality of ultra-sparsifier support graph. To gain higher computational efficiency during transient circuit analysis, a dynamic support-circuit preconditioner updating approach has also been investigated. Our experimental results for a variety of large-scale nonlinear circuit designs show that the proposed technique can achieve up to 14.0X runtime speedups and 6.7X memory reduction in DC and transient simulations.