Characterizing Statistical Cells Timing Metrics with Semi-Monte-Carlo Method

Abstract : Monte Carlo (MC) method is the standard method to characterize statistical moments of cell delays and slopes. However, this method suffers from very high computational cost. In this paper, we propose a technique to quickly and accurately estimate Standard Deviation (SD) of standard cell delays and slopes. The proposed technique is based on the identification, performed with a reduced set of MC simulations, of delay and output slope SD functions that take input slope, output load and supply voltage as input arguments. These identified functions are then used to estimate SDs of delays and slopes at different operating conditions (input slope, output load, supply voltage). This proposed method provides at least ퟕퟔ% of CPU gains, with respect to MC, while keeping high accuracy.
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Communication dans un congrès
VLSI-Soc'11 : IFIP/IEEE International Conference on Very Large Scale Integration, Oct 2011, Hong-Kong, China. pp.6, 2011
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00617606
Contributeur : Nadine Azemard <>
Soumis le : lundi 29 août 2011 - 17:19:02
Dernière modification le : lundi 23 juillet 2018 - 14:20:01

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  • HAL Id : lirmm-00617606, version 1

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Nadine Azemard, Zeqin Wu, Philippe Maurine, Gille Ducharme. Characterizing Statistical Cells Timing Metrics with Semi-Monte-Carlo Method. VLSI-Soc'11 : IFIP/IEEE International Conference on Very Large Scale Integration, Oct 2011, Hong-Kong, China. pp.6, 2011. 〈lirmm-00617606〉

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