Death, taxes and failing chips
Proceedings of the 40th annual Design Automation Conference
First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
Block-based Static Timing Analysis with Uncertainty
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Statistical Timing Analysis Considering Spatial Correlations using a Single Pert-Like Traversal
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Statistical timing yield optimization by gate sizing
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Advances in Computation of the Maximum of a Set of Gaussian Random Variables
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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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Statistical static timing analysis (SSTA) involves computation of maximum (max) and minimum (min) of Gaussian random variables. Typically, the max or min of a set of Gaussians is performed iteratively in a pair-wise fashion, wherein the result of each pair-wise max or min operation is approximated to a Gaussian by matching moments of the true result obtained using Clark's approach [1]. The approximation error in the final result is thus a function of the order in which the pair-wise operations are performed. In this paper, we analyze known "run-time expensive" ordering techniques that attempt to reduce this error in the context of SSTA and SSTA driven optimization. We propose new techniques to speeding up the computation of the max/min of a set of Gaussians by special handling of prevalent "zero error" cases. Two new methods are presented using these techniques that provide more than 60% run-time savings (3X speed-up) in max/min operations. This translates to an overall run-time improvement of 2--17% for a single SSTA run and an improvement of up to 8 hours (55%) in an SSTA driven optimization run.