Multilevel hypergraph partitioning: applications in VLSI domain
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Modifying a Sparse Cholesky Factorization
SIAM Journal on Matrix Analysis and Applications
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
Multiple-Rank Modifications of a Sparse Cholesky Factorization
SIAM Journal on Matrix Analysis and Applications
A static pattern-independent technique for power grid voltage integrity verification
Proceedings of the 40th annual Design Automation Conference
Power network analysis using an adaptive algebraic multigrid approach
Proceedings of the 40th annual Design Automation Conference
Row Modifications of a Sparse Cholesky Factorization
SIAM Journal on Matrix Analysis and Applications
Power grid physics and implications for CAD
Proceedings of the 43rd annual Design Automation Conference
A multigrid-like technique for power grid analysis
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Sparse implicit projection (SIP) for reduction of general many-terminal networks
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Electromigration study of power-gated grids
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
Reliability-Driven Power/Ground Routing for Analog ICs
ACM Transactions on Design Automation of Electronic Systems (TODAES)
| Hi-index | 0.00 |
Modern deep sub-micron ULSI designs with hundreds of millions of devices require huge grids for power distribution. Such grids, operating with increasingly low-power voltages, are a design limiting factor and accurate analysis of their behavior is of paramount importance as any voltage drops can seriously impact performance or functionality. As power grid models have millions of unknowns, highly optimized special purpose simulation tools are required to handle the time and memory complexity of solving for their dynamic behavior. In this work, we propose a hierarchical matrix representation of the power grid model that is both space and time efficient. With this representation, reduced storage matrix factors are efficiently computed and applied in the analysis at every time-step of the simulation. Results show an almost linear complexity growth, namely O(nloga(n)), for some small constant a, in both space and time, when using this matrix representation. Comparisons of our academic implementation with production-quality code proves this method to be very efficient when dealing with the simulation of large power grid models