TY - JOUR
T1 - A Performance-Guided Graph Sparsification Approach to Scalable and Robust SPICE-Accurate Integrated Circuit Simulations
AU - Zhao, Xueqian
AU - Han, Lengfei
AU - Feng, Zhuo
N1 - Publisher Copyright:
© 1982-2012 IEEE.
PY - 2015/10/1
Y1 - 2015/10/1
N2 - To improve the efficiency of direct solution methods in SPICE-accurate integrated circuit (IC) simulations, preconditioned iterative solution techniques have been widely studied in the past decades. However, it is still an extremely challenging task to develop robust yet efficient general-purpose preconditioning methods that can deal with various types of large-scale IC problems. In this paper, based on recent graph sparsification research we propose circuit-oriented general-purpose support-circuit preconditioning (GPSCP) methods to dramatically improve the sparse matrix solution time and reduce the memory cost during SPICE-accurate IC simulations. By sparsifying the Laplacian matrix extracted from the original circuit network using graph sparsification techniques, general-purpose support circuits can be efficiently leveraged as preconditioners for solving large Jacobian matrices through Krylov-subspace iterations. Additionally, a performance model-guided graph sparsification framework is proposed to help automatically build nearly-optimal GPSCP solvers. Our experiment results for a variety of large-scale IC designs show that the proposed preconditioning techniques can achieve up to 18 {\times } runtime speedups and 7 {\times } memory reduction in DC and transient simulations when compared to state-of-The-art direct solution methods.
AB - To improve the efficiency of direct solution methods in SPICE-accurate integrated circuit (IC) simulations, preconditioned iterative solution techniques have been widely studied in the past decades. However, it is still an extremely challenging task to develop robust yet efficient general-purpose preconditioning methods that can deal with various types of large-scale IC problems. In this paper, based on recent graph sparsification research we propose circuit-oriented general-purpose support-circuit preconditioning (GPSCP) methods to dramatically improve the sparse matrix solution time and reduce the memory cost during SPICE-accurate IC simulations. By sparsifying the Laplacian matrix extracted from the original circuit network using graph sparsification techniques, general-purpose support circuits can be efficiently leveraged as preconditioners for solving large Jacobian matrices through Krylov-subspace iterations. Additionally, a performance model-guided graph sparsification framework is proposed to help automatically build nearly-optimal GPSCP solvers. Our experiment results for a variety of large-scale IC designs show that the proposed preconditioning techniques can achieve up to 18 {\times } runtime speedups and 7 {\times } memory reduction in DC and transient simulations when compared to state-of-The-art direct solution methods.
KW - SPICE simulation
KW - graph sparsification theory
KW - iterative solver
KW - post-layout integrated circuits
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U2 - 10.1109/TCAD.2015.2424958
DO - 10.1109/TCAD.2015.2424958
M3 - Article
AN - SCOPUS:84942412232
SN - 0278-0070
VL - 34
SP - 1639
EP - 1651
JO - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
JF - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IS - 10
M1 - 7090972
ER -