Selective Inversion of Inductance Matrix for Large-Scale Sparse RLC Simulation

Apostolopoulou, Ifigeneia; Daloukas, Konstantis; Evmorfopoulos, Nestor; Stamoulis, George

doi:10.1145/2593069.2593213

Cited by 3 publications

(1 citation statement)

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“…However, this requires prior inversion of a large dense matrix which is a very expensive computational procedure, making the reluctancebased approaches feasible only for problems of moderate sizes. An approach that avoids the expensive inversion of the inductance matrix, by computing selectively only some of the entries of the reluctance matrix up to a given sparsity ratio, has been proposed in [5]. However, this approach introduces error that is difficult to quantify in advance, thus limiting its application in practical settings.…”

Section: Introductionmentioning

confidence: 99%

Efficient Linear System Solution Techniques in the Simulation of Large Dense Mutually Inductive Circuits

Antoniadis

Mihajlović

Evmorfopoulos

et al. 2019

2019 IEEE 37th International Conference on Computer Design (ICCD)

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The verification of integrated Circuits (ICs) in deep submicron technologies requires that all mutual inductive effects are taken into account to properly validate the performance and reliable operation of the chip. However, the inclusion of all mutual inductive couplings results in a fully dense inductance matrix that renders the circuit simulation computationally prohibitive. In this paper, we present efficient techniques for the solution of the linear systems arising in transient analysis of large mutually inductive circuits. These techniques involve the compression of the dense inductance matrix block by low-rank products in hierarchical matrix format, as well as the development of a Schur-complement preconditioner for the iterative solution of the transient linear system (which comprises sparse blocks alongside the dense inductance block). Experimental results indicate that substantial compression rates of the inductance matrix can be achieved without compromising accuracy, along with considerable reduction in iteration counts and execution time of iterative solution methods.

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Section: Introductionmentioning

confidence: 99%