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".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Neste artigo, propomos um Método de Colocação Estocástica Adaptativa para Análise Estatística de Temporização Estática (SSTA) baseada em blocos. Um novo método adaptativo é proposto para realizar SSTA com atrasos de portas e interconexões modeladas por polinômios quadráticos baseados na expansão do Caos Homogêneo. A fim de aproximar o operador atômico chave MAX no espaço aleatório completo durante a análise temporal, o método proposto escolhe adaptativamente o algoritmo ideal de um conjunto de métodos de colocação estocástica, considerando diferentes condições de entrada. Comparado com os métodos de colocação estocástica existentes, incluindo aquele que utiliza a técnica de redução de dimensão e aquele que utiliza a técnica Sparse Grid, o método proposto tem melhorias de 10x na precisão enquanto usa a mesma ordem de tempo de cálculo. O algoritmo proposto também mostra grande melhoria na precisão em comparação com um método de casamento de momentos. Em comparação com as 10,000 simulações de Monte Carlo nos circuitos de benchmark ISCAS85, os resultados do método proposto mostram menos de 1% de erro na média e na variância, e aceleram quase 100x.
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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 -