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
기존 통계적 타이밍 분석의 성능을 향상시키기 위해서는 슬루 분포를 고려해야 하며 이를 신호 경로를 따라 지연 분포와 함께 전파하는 메커니즘이 필요합니다. 본 논문에서는 슬루 분포와 지연 분포를 표현하기 위한 가우스 혼합 모델을 소개하고 통계적 타이밍 분석을 위한 새로운 알고리즘을 제안합니다. 알고리즘은 주어진 회로 그래프에서 한 쌍의 지연과 슬루를 전파하고, 전파된 슬루에 따라 회로 요소의 지연 분포를 동적으로 변경합니다. 제안된 모델과 알고리즘을 몬테카를로 시뮬레이션과 비교하여 평가한다. 실험 결과는 현재의 가우스 분포를 이용한 통계적 타이밍 분석에 비해 최대 지연의 µ+3σ 값의 정확도 향상이 최대 4.5포인트임을 보여줍니다.
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Shingo TAKAHASHI, Shuji TSUKIYAMA, "A New Statistical Timing Analysis Using Gaussian Mixture Models for Delay and Slew Propagated Together" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 3, pp. 900-911, March 2009, doi: 10.1587/transfun.E92.A.900.
Abstract: In order to improve the performance of the existing statistical timing analysis, slew distributions must be taken into account and a mechanism to propagate them together with delay distributions along signal paths is necessary. This paper introduces Gaussian mixture models to represent the slew and delay distributions, and proposes a novel algorithm for statistical timing analysis. The algorithm propagates a pair of delay and slew in a given circuit graph, and changes the delay distributions of circuit elements dynamically by propagated slews. The proposed model and algorithm are evaluated by comparing with Monte Carlo simulation. The experimental results show that the accuracy improvement in µ+3σ value of maximum delay is up to 4.5 points from the current statistical timing analysis using Gaussian distributions.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.900/_p
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@ARTICLE{e92-a_3_900,
author={Shingo TAKAHASHI, Shuji TSUKIYAMA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A New Statistical Timing Analysis Using Gaussian Mixture Models for Delay and Slew Propagated Together},
year={2009},
volume={E92-A},
number={3},
pages={900-911},
abstract={In order to improve the performance of the existing statistical timing analysis, slew distributions must be taken into account and a mechanism to propagate them together with delay distributions along signal paths is necessary. This paper introduces Gaussian mixture models to represent the slew and delay distributions, and proposes a novel algorithm for statistical timing analysis. The algorithm propagates a pair of delay and slew in a given circuit graph, and changes the delay distributions of circuit elements dynamically by propagated slews. The proposed model and algorithm are evaluated by comparing with Monte Carlo simulation. The experimental results show that the accuracy improvement in µ+3σ value of maximum delay is up to 4.5 points from the current statistical timing analysis using Gaussian distributions.},
keywords={},
doi={10.1587/transfun.E92.A.900},
ISSN={1745-1337},
month={March},}
부
TY - JOUR
TI - A New Statistical Timing Analysis Using Gaussian Mixture Models for Delay and Slew Propagated Together
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 900
EP - 911
AU - Shingo TAKAHASHI
AU - Shuji TSUKIYAMA
PY - 2009
DO - 10.1587/transfun.E92.A.900
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2009
AB - In order to improve the performance of the existing statistical timing analysis, slew distributions must be taken into account and a mechanism to propagate them together with delay distributions along signal paths is necessary. This paper introduces Gaussian mixture models to represent the slew and delay distributions, and proposes a novel algorithm for statistical timing analysis. The algorithm propagates a pair of delay and slew in a given circuit graph, and changes the delay distributions of circuit elements dynamically by propagated slews. The proposed model and algorithm are evaluated by comparing with Monte Carlo simulation. The experimental results show that the accuracy improvement in µ+3σ value of maximum delay is up to 4.5 points from the current statistical timing analysis using Gaussian distributions.
ER -