Implementation and tests of low-discrepancy sequences
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the conference on Design, automation and test in Europe
Analysis of dynamic voltage/frequency scaling in chip-multiprocessors
ISLPED '07 Proceedings of the 2007 international symposium on Low power electronics and design
VLSID '08 Proceedings of the 21st International Conference on VLSI Design
Efficient Monte Carlo based incremental statistical timing analysis
Proceedings of the 45th annual Design Automation Conference
Optimal margin computation for at-speed test
Proceedings of the conference on Design, automation and test in Europe
Practical, fast Monte Carlo statistical static timing analysis: why and how
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
On efficient Monte Carlo-based statistical static timing analysis of digital circuits
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Breaking the simulation barrier: SRAM evaluation through norm minimization
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Statistical static timing analysis: A survey
Integration, the VLSI Journal
Statistical Timing Analysis: From Basic Principles to State of the Art
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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We present a new technique for statistical static timing analysis (SSTA) based on Markov chain Monte Carlo (MCMC), that allows fast and accurate estimation of the right-hand tail of the delay distribution. A "naive" MCMC approach is inadequate for SSTA. Several modifications and enhancements, presented in this paper, enable application of MCMC to SSTA. Moreover, such an approach overcomes inherent limitations of techniques such as importance sampling and Quasi-Monte Carlo. Our results on open source designs, with an independent delay variation model, demonstrate that our technique can obtain more than an order of magnitude improvement in computation time over simple Monte Carlo, given an estimation accuracy target at a point in the tail. Our approach works by providing a large number of samples in the region of interest. Open problems include extension of algorithm applicability to a broader class of synthesis conditions, and handling of correlated delay variations. In a broader context, this work aims to show that MCMC and associated techniques can be useful in rare event analyses related to circuits, particularly for high-dimensional problems.