NDLI: Statistical timing analysis with correlated non-gaussian parameters using independent component analysis

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Timing driven power gating

Statistical timing analysis with correlated non-gaussian parameters using independent component analysis

Content Provider	ACM Digital Library
Author	Sapatnekar, Sachin Singh, Jaskirat
Abstract	We propose a scalable and efficient parameterized block-based statistical static timing analysis algorithm incorporating both Gaussian and non-Gaussian parameter distributions, capturing spatial correlations using a grid-based model. As a preprocessing step, we employ independent component analysis to transform the set of correlated non-Gaussian parameters to a basis set of parameters that are statistically independent, and principal components analysis to orthogonalize the Gaussian parameters. The procedure requires minimal input information: given the moments of the variational parameters, we use a Padé approximation-based moment matching scheme to generate the distributions of the random variables representing the signal arrival times, and preserve correlation information by propagating arrival times in a canonical form. For the ISCAS89 benchmark circuits, as compared to Monte Carlo simulations, we obtain average errors of 0.99% and 2.05%, respectively, in the mean and standard deviation of the circuit delay. For a circuit with G gates and a layout with g spatial correlation grids,the complexity of our approach is O(g G).
Starting Page	155
Ending Page	160
Page Count	6
File Format	PDF
ISBN	1595933816
DOI	10.1145/1146909.1146953
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-07-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Statistical timing Independent component analysis Moment matching Non-gaussian
Content Type	Text
Resource Type	Article

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