A practical Bayesian framework for backpropagation networks
Neural Computation
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Statistical Timing Analysis Considering Spatial Correlations using a Single Pert-Like Traversal
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
New Generation of Predictive Technology Model for Sub-45nm Design Exploration
ISQED '06 Proceedings of the 7th International Symposium on Quality Electronic Design
Projection-based performance modeling for inter/intra-die variations
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Worst-case analysis and optimization of VLSI circuit performances
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 45th annual Design Automation Conference
Proceedings of the 46th Annual Design Automation Conference
Efficient design-specific worst-case corner extraction for integrated circuits
Proceedings of the 46th Annual Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 2009 International Conference on Computer-Aided Design
Proceedings of the 47th Design Automation Conference
Generation of yield-embedded Pareto-front for simultaneous optimization of yield and performances
Proceedings of the 47th Design Automation Conference
Two fast methods for estimating the minimum standby supply voltage for large SRAMs
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 50th Annual Design Automation Conference
Proceedings of the International Conference on Computer-Aided Design
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The number and magnitude of process variation sources are increasing as we scale further into the nano regime. Today's most successful response surface methods limit us to low-order forms -- linear, quadratic -- to make the fitting tractable. Unfortunately, not all variation-al scenarios are well modeled with low-order surfaces. We show how to exploit latent variable regression ideas to support efficient extraction of arbitrarily nonlinear statistical response surfaces. An implementation of these ideas called SiLVR, applied to a range of analog and digital circuits, in technologies from 90 to 45nm, shows significant improvements in prediction, with errors reduced by up to 21X, with very reasonable runtime costs.