Scenarios and policy aggregation in optimization under uncertainty
Mathematics of Operations Research
Stochastic decomposition: an algorithm for two-state linear programs with recourse
Mathematics of Operations Research
Parameter variations and impact on circuits and microarchitecture
Proceedings of the 40th annual Design Automation Conference
First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
Statistical Timing Analysis Considering Spatial Correlations using a Single Pert-Like Traversal
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Proceedings of the 42nd annual Design Automation Conference
A general framework for accurate statistical timing analysis considering correlations
Proceedings of the 42nd annual Design Automation Conference
An efficient algorithm for statistical minimization of total power under timing yield constraints
Proceedings of the 42nd annual Design Automation Conference
Robust gate sizing by geometric programming
Proceedings of the 42nd annual Design Automation Conference
Leakage control through fine-grained placement and sizing of sleep transistors
Proceedings of the 2004 IEEE/ACM International conference on Computer-aided design
Variability driven gate sizing for binning yield optimization
Proceedings of the 43rd annual Design Automation Conference
Variability-driven formulation for simultaneous gate sizing and post-silicon tunability allocation
Proceedings of the 2007 international symposium on Physical design
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Increasing effects of fabrication variability have inspired a growing interest in statistical techniques for design optimization. In this work, we propose a Monte-Carlo driven stochastic optimization framework that does not rely on the distribution of the varying parameters (unlike most other existing techniques). Stochastic techniques like Successive Sample Mean Optimization (SSMO) and Stochastic Decomposition present a strong framework for solving linear programming formulations in which the parameters behave as random variables. We consider Binning-Yield Loss (BYL) as the optimization objective and show that we can get a provably optimal solution under a convex BYL function. We apply this framework for the MTCMOS sizing problem [21] using SSMO and Stochastic Decomposition techniques. The experimental results show that the solution obtained from stochastic decomposition based framework had 0% yield-loss, while the deterministic solution [21] had a 48% yield-loss.