Sparse and efficient reduced order modeling of linear subcircuits with large number of terminals
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Noise driven in-package decoupling capacitor optimization for power integrity
Proceedings of the 2006 international symposium on Physical design
An efficient method for terminal reduction of interconnect circuits considering delay variations
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Power grid physics and implications for CAD
Proceedings of the 43rd annual Design Automation Conference
Proceedings of the 43rd annual Design Automation Conference
A fast block structure preserving model order reduction for inverse inductance circuits
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Allocating power ground vias in 3D ICs for simultaneous power and thermal integrity
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Reducing peak power with a table-driven adaptive processor core
Proceedings of the 42nd Annual IEEE/ACM International Symposium on Microarchitecture
Fast analysis of a large-scale inductive interconnect by block-structure-preserved macromodeling
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Modeling and design for beyond-the-die power integrity
Proceedings of the International Conference on Computer-Aided Design
System-in-Package: Electrical and Layout Perspectives
Foundations and Trends in Electronic Design Automation
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Off-chip decoupling capacitor (decap) allocation is a demanding task during package and chip codesign. Existing approaches can not handle large numbers of I/O counts and large numbers of legal decap positions. In this paper, we propose a fast decoupling capacitor allocation method. By applying a spectral clustering, a small amount of principal I/Os can be found. Accordingly, the large power supply network is partitioned into several blocks each with only one principal I/O. This enables a localized macromodeling for each block by a triangular-structured reduction. In addition, to systemically consider a large legal position map in a manageable fashion, the map of legal positions is decomposed into multiple rings, which are further parameterized in each block. The decaps are then allocated according to the sensitivity obtained from the parameterized macro-model for each block. Compared to the PRIMA-based macromodeling, experiments show that our method (TBS2) is 25X faster and has 3.04X smaller error. Moreover, our decap allocation reduces the optimization time by 97X, and reduces decap cost by up to 16% to meet the same power-integrty target.