Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Extended Krylov subspace method for reduced order analysis of linear circuits with multiple sources
Proceedings of the 37th Annual Design Automation Conference
Efficient large-scale power grid analysis based on preconditioned krylov-subspace iterative methods
Proceedings of the 38th annual Design Automation Conference
Proceedings of the 39th annual Design Automation Conference
Random walks in a supply network
Proceedings of the 40th annual Design Automation Conference
Fast flip-chip power grid analysis via locality and grid shells
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Power grid physics and implications for CAD
Proceedings of the 43rd annual Design Automation Conference
Advanced Model Order Reduction Techniques in VLSI Design
Advanced Model Order Reduction Techniques in VLSI Design
ETBR: extended truncated balanced realization method for on-chip power grid network analysis
Proceedings of the conference on Design, automation and test in Europe
PRIMA: passive reduced-order interconnect macromodeling algorithm
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Hierarchical analysis of power distribution networks
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A multigrid-like technique for power grid analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Efficient linear circuit analysis by Pade approximation via the Lanczos process
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Efficient algorithms for fast IR drop analysis exploiting locality
Integration, the VLSI Journal
Decentralized and passive model order reduction of linear networks with massive ports
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Fast analysis of power grid networks has been a challenging problem for many years. The huge size renders circuit simulation inefficient and the large number of inputs further limits the application of existing Krylov-subspace macromodeling algorithms. However, strong locality has been observed that two nodes geometrically far have very small electrical impact on each other because of the exponential attenuation. However, no systematic approaches have been proposed to exploit such locality. In this paper, we propose a novel modeling and simulation scheme, which can automatically identify the dominant inputs for a given observed node in a power grid network. This enables us to build extremely compact models by projecting the system onto the locally dominant Krylov subspace corresponding to those dominant inputs only. The resulting simulation can be very fast with the compact models if we only need to view the responses of a few nodes under many different inputs. Experimental results show that the proposed method can have at least 100X speedup over SPICE-like simulations on a number of large power grid networks up to 1M nodes.