The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cet article, nous proposons une méthode de collocation stochastique adaptative pour l'analyse statistique du timing statique (SSTA) basée sur des blocs. Une nouvelle méthode adaptative est proposée pour réaliser SSTA avec des retards de portes et d'interconnexions modélisés par des polynômes quadratiques basés sur l'expansion du chaos homogène. Afin d'approcher l'opérateur atomique clé MAX dans l'espace aléatoire complet lors de l'analyse temporelle, la méthode proposée choisit de manière adaptative l'algorithme optimal parmi un ensemble de méthodes de collocation stochastique en considérant différentes conditions d'entrée. Par rapport aux méthodes de collocation stochastique existantes, y compris celle utilisant la technique de réduction de dimension et celle utilisant la technique Sparse Grid, la méthode proposée présente une précision 10 fois supérieure tout en utilisant le même ordre de temps de calcul. L'algorithme proposé montre également une grande amélioration en termes de précision par rapport à une méthode d'appariement de moments. Par rapport aux 10,000 85 simulations de Monte Carlo sur des circuits de référence ISCAS1, les résultats de la méthode proposée montrent une erreur de moins de 100 % sur la moyenne et la variance, et des vitesses près de XNUMX fois supérieures.
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Yi WANG, Xuan ZENG, Jun TAO, Hengliang ZHU, Wei CAI, "Adaptive Stochastic Collocation Method for Parameterized Statistical Timing Analysis with Quadratic Delay Model" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 12, pp. 3465-3473, December 2008, doi: 10.1093/ietfec/e91-a.12.3465.
Abstract: In this paper, we propose an Adaptive Stochastic Collocation Method for block-based Statistical Static Timing Analysis (SSTA). A novel adaptive method is proposed to perform SSTA with delays of gates and interconnects modeled by quadratic polynomials based on Homogeneous Chaos expansion. In order to approximate the key atomic operator MAX in the full random space during timing analysis, the proposed method adaptively chooses the optimal algorithm from a set of stochastic collocation methods by considering different input conditions. Compared with the existing stochastic collocation methods, including the one using dimension reduction technique and the one using Sparse Grid technique, the proposed method has 10x improvements in the accuracy while using the same order of computation time. The proposed algorithm also shows great improvement in accuracy compared with a moment matching method. Compared with the 10,000 Monte Carlo simulations on ISCAS85 benchmark circuits, the results of the proposed method show less than 1% error in the mean and variance, and nearly 100x speeds up.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.12.3465/_p
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@ARTICLE{e91-a_12_3465,
author={Yi WANG, Xuan ZENG, Jun TAO, Hengliang ZHU, Wei CAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Adaptive Stochastic Collocation Method for Parameterized Statistical Timing Analysis with Quadratic Delay Model},
year={2008},
volume={E91-A},
number={12},
pages={3465-3473},
abstract={In this paper, we propose an Adaptive Stochastic Collocation Method for block-based Statistical Static Timing Analysis (SSTA). A novel adaptive method is proposed to perform SSTA with delays of gates and interconnects modeled by quadratic polynomials based on Homogeneous Chaos expansion. In order to approximate the key atomic operator MAX in the full random space during timing analysis, the proposed method adaptively chooses the optimal algorithm from a set of stochastic collocation methods by considering different input conditions. Compared with the existing stochastic collocation methods, including the one using dimension reduction technique and the one using Sparse Grid technique, the proposed method has 10x improvements in the accuracy while using the same order of computation time. The proposed algorithm also shows great improvement in accuracy compared with a moment matching method. Compared with the 10,000 Monte Carlo simulations on ISCAS85 benchmark circuits, the results of the proposed method show less than 1% error in the mean and variance, and nearly 100x speeds up.},
keywords={},
doi={10.1093/ietfec/e91-a.12.3465},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - Adaptive Stochastic Collocation Method for Parameterized Statistical Timing Analysis with Quadratic Delay Model
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3465
EP - 3473
AU - Yi WANG
AU - Xuan ZENG
AU - Jun TAO
AU - Hengliang ZHU
AU - Wei CAI
PY - 2008
DO - 10.1093/ietfec/e91-a.12.3465
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2008
AB - In this paper, we propose an Adaptive Stochastic Collocation Method for block-based Statistical Static Timing Analysis (SSTA). A novel adaptive method is proposed to perform SSTA with delays of gates and interconnects modeled by quadratic polynomials based on Homogeneous Chaos expansion. In order to approximate the key atomic operator MAX in the full random space during timing analysis, the proposed method adaptively chooses the optimal algorithm from a set of stochastic collocation methods by considering different input conditions. Compared with the existing stochastic collocation methods, including the one using dimension reduction technique and the one using Sparse Grid technique, the proposed method has 10x improvements in the accuracy while using the same order of computation time. The proposed algorithm also shows great improvement in accuracy compared with a moment matching method. Compared with the 10,000 Monte Carlo simulations on ISCAS85 benchmark circuits, the results of the proposed method show less than 1% error in the mean and variance, and nearly 100x speeds up.
ER -