An Evaluation Method of the Number of Monte Carlo STA Trials for Statistical Path Delay Analysis

Masanori IMAI; Takashi SATO; Noriaki NAKAYAMA; Kazuya MASU

doi:10.1093/ietfec/e91-a.4.957

An Evaluation Method of the Number of Monte Carlo STA Trials for Statistical Path Delay Analysis

Masanori IMAI, Takashi SATO, Noriaki NAKAYAMA, Kazuya MASU

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We present an evaluation method for estimating the lower bound number of Monte Carlo STA trials required to obtain at least one sample which falls within top-k % of its parent population. The sample can be used to ensure that target designs are timing-error free with a predefined probability using the minimum computational cost. The lower bound number is represented as a closed-form formula which is general enough to be applied to other verifications. For validation, Monte Carlo STA was carried out on various benchmark data including ISCAS circuits. The minimum number of Monte Carlo runs determined using the proposed method successfully extracted one or more top-k % delay instances.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E91-A No.4 pp.957-964

Publication Date: 2008/04/01

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Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e91-a.4.957

Type of Manuscript: Special Section PAPER (Special Section on Selected Papers from the 20th Workshop on Circuits and Systems in Karuizawa)

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Masanori IMAI, Takashi SATO, Noriaki NAKAYAMA, Kazuya MASU, "An Evaluation Method of the Number of Monte Carlo STA Trials for Statistical Path Delay Analysis" in IEICE TRANSACTIONS on Fundamentals, vol. E91-A, no. 4, pp. 957-964, April 2008, doi: 10.1093/ietfec/e91-a.4.957.
Abstract: We present an evaluation method for estimating the lower bound number of Monte Carlo STA trials required to obtain at least one sample which falls within top-k % of its parent population. The sample can be used to ensure that target designs are timing-error free with a predefined probability using the minimum computational cost. The lower bound number is represented as a closed-form formula which is general enough to be applied to other verifications. For validation, Monte Carlo STA was carried out on various benchmark data including ISCAS circuits. The minimum number of Monte Carlo runs determined using the proposed method successfully extracted one or more top-k % delay instances.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.4.957/_p

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@ARTICLE{e91-a_4_957,
author={Masanori IMAI, Takashi SATO, Noriaki NAKAYAMA, Kazuya MASU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Evaluation Method of the Number of Monte Carlo STA Trials for Statistical Path Delay Analysis},
year={2008},
volume={E91-A},
number={4},
pages={957-964},
abstract={We present an evaluation method for estimating the lower bound number of Monte Carlo STA trials required to obtain at least one sample which falls within top-k % of its parent population. The sample can be used to ensure that target designs are timing-error free with a predefined probability using the minimum computational cost. The lower bound number is represented as a closed-form formula which is general enough to be applied to other verifications. For validation, Monte Carlo STA was carried out on various benchmark data including ISCAS circuits. The minimum number of Monte Carlo runs determined using the proposed method successfully extracted one or more top-k % delay instances.},
keywords={},
doi={10.1093/ietfec/e91-a.4.957},
ISSN={1745-1337},
month={April},}

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TY - JOUR
TI - An Evaluation Method of the Number of Monte Carlo STA Trials for Statistical Path Delay Analysis
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 957
EP - 964
AU - Masanori IMAI
AU - Takashi SATO
AU - Noriaki NAKAYAMA
AU - Kazuya MASU
PY - 2008
DO - 10.1093/ietfec/e91-a.4.957
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2008
AB - We present an evaluation method for estimating the lower bound number of Monte Carlo STA trials required to obtain at least one sample which falls within top-k % of its parent population. The sample can be used to ensure that target designs are timing-error free with a predefined probability using the minimum computational cost. The lower bound number is represented as a closed-form formula which is general enough to be applied to other verifications. For validation, Monte Carlo STA was carried out on various benchmark data including ISCAS circuits. The minimum number of Monte Carlo runs determined using the proposed method successfully extracted one or more top-k % delay instances.
ER -