TY - GEN
T1 - Statistical timing analysis with correlated non-gaussian parameters using independent component analysis
AU - Singh, Jaskirat
AU - Sapatnekar, Sachin S
PY - 2006
Y1 - 2006
N2 - 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).
AB - 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).
KW - Independent component analysis
KW - Moment matching
KW - Non-Gaussian
KW - Statistical timing
UR - http://www.scopus.com/inward/record.url?scp=34547223772&partnerID=8YFLogxK
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U2 - 10.1145/1146909.1146953
DO - 10.1145/1146909.1146953
M3 - Conference contribution
AN - SCOPUS:34547223772
SN - 1595933816
SN - 1595933816
SN - 9781595933812
T3 - Proceedings - Design Automation Conference
SP - 155
EP - 160
BT - 2006 43rd ACM/IEEE Design Automation Conference, DAC'06
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual Design Automation Conference, DAC 2006
Y2 - 24 July 2006 through 28 July 2006
ER -