Stochastic finite elements: a spectral approach
Stochastic finite elements: a spectral approach
Technology and design challenges for low power and high performance
ISLPED '99 Proceedings of the 1999 international symposium on Low power electronics and design
Statistical analysis of subthreshold leakage current for VLSI circuits
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Full-chip analysis of leakage power under process variations, including spatial correlations
Proceedings of the 42nd annual Design Automation Conference
Projection-based statistical analysis of full-chip leakage power with non-log-normal distributions
Proceedings of the 43rd annual Design Automation Conference
Modeling and estimation of full-chip leakage current considering within-die correlation
Proceedings of the 44th annual Design Automation Conference
Static timing: back to our roots
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Statistical modeling and analysis of chip-level leakage power by spectral stochastic method
Proceedings of the 2009 Asia and South Pacific Design Automation Conference
Statistical modeling and analysis of chip-level leakage power by spectral stochastic method
Integration, the VLSI Journal
Proceedings of the 2009 International Conference on Computer-Aided Design
Robust Extraction of Spatial Correlation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Statistical full-chip total power estimation considering spatially correlated process variations
Integration, the VLSI Journal
An efficient method for analyzing on-chip thermal reliability considering process variations
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Variation-aware leakage power model extraction for system-level hierarchical power analysis
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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Full-chip statistical leakage power analysis typically requires quadratic time complexity in the presence of spatial correlation. When spatial correlation are strong (with large spatial correlation length), efficient linear time complexity analysis can be attained as the number of variational variables can be significantly reduced. However this is not the case for circuits where gate leakage currents are weakly correlated. In this paper, we present a linear time algorithm for statistical leakage power analysis in the presence of weak spatial correlation. The new algorithm exploits the fact that gate leakage current can be efficiently computed locally when correlation is weak. We adopt a newly proposed spatial correlation model where a new set of location-dependent uncorrelated variables are defined over virtual grids to represent the original physical random variables via fitting. To compute the leakage current of a gate on the new set of variables, the new method uses the orthogonal polynomials based collocation method, which can be applied to any gate leakage models. The total leakage currents are then computed by simply summing up the resulting orthogonal polynomials (their coefficients) on the new set of variables for all gates. Experimental results show that the proposed method is about two orders of magnitude faster than the recently proposed grid-based method [3] with similar accuracy and many orders of magnitude times over the Monte Carlo method.