Efficient large-scale power grid analysis based on preconditioned krylov-subspace iterative methods
Proceedings of the 38th annual Design Automation Conference
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
A fast on-chip decoupling capacitance budgeting algorithm using macromodeling and linear programming
Proceedings of the 43rd annual Design Automation Conference
Application of the cross-entropy method to clustering and vector quantization
Journal of Global Optimization
Multigrid on GPU: tackling power grid analysis on parallel SIMT platforms
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Electronic Circuit & System Simulation Methods (SRE)
Electronic Circuit & System Simulation Methods (SRE)
Optimal decoupling capacitor sizing and placement for standard-cell layout designs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
On-chip power-supply network optimization using multigrid-based technique
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Partitioning-Based Approach to Fast On-Chip Decoupling Capacitor Budgeting and Minimization
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Fast and scalable parallel layout decomposition in double patterning lithography
Integration, the VLSI Journal
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Decoupling capacitor (decap) placement has been widely adopted as an effective way to suppress dynamic power supply noise. Traditional decap budgeting algorithms usually explore the sensitivity-based nonlinear optimizations or conjugate gradient methods, which can be prohibitively expensive for large-scale decap budgeting problems. We present a hierarchical cross entropy (CE) optimization technique for solving the decap budgeting problem. CE is an advanced optimization framework which explores the power of rare-event probability theory and importance sampling. To achieve high efficiency, a sensitivity-guided cross entropy (SCE) algorithm is proposed which integrates CE with a partitioning-based sampling strategy to effectively reduce the dimensionality in solving the large scale decap budgeting problems. Extensive experiments on industrial power grid benchmarks show that the proposed SCE method converges 2X faster than the prior methods and 10X faster than the standard CE method, while gaining up to 25% improvement on power grid supply noise. Importantly, the proposed SCE algorithm is parallel-friendly since the simulation samples of each SCE iteration can be independently obtained in parallel. We obtain up to 1.9X speedup when running the SCE decap budgeting algorithm on a dual-core-dual-GPU system.