Hierarchical random-walk algorithms for power grid analysis
Proceedings of the 2004 Asia and South Pacific Design Automation Conference
Power grid analysis benchmarks
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Power grid analysis using random walks
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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This paper proposes an importance sampling (IS) technique based on quasi-zero-variance estimation for accelerating convergence of random-walk-based power grid analysis. In our approach, the alternative probability for IS is incrementally updated after every Mr samples of random walk so that more recent and thus more accurate node voltages are utilized to asymptotically achieve ideal zero-variance estimation. We also propose a method to determine efficient Mr for the r-th probability update; although smaller Mr results more aggressive update of alternative probability, the alternative probability becomes inaccurate if Mr is too small. The estimation error of the proposed method decreases O((M/r)-r/2), which breaks O(M-1/2), the slow convergence-rate barrier of normal Monte Carlo analysis. Our trial implementation achieved 790x speedup compared with a conventional random-walk-based circuit analysis for analyzing IBM power grid benchmark circuits at 1mV accuracy.