• DocumentCode
    2649056
  • Title

    Framework for statistical analysis of homogeneous multicore power grid networks

  • Author

    Liu, Guanglei ; Fan, Jeffrey

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Florida Int. Univ., Miami, FL, USA
  • fYear
    2009
  • fDate
    20-23 Oct. 2009
  • Firstpage
    423
  • Lastpage
    426
  • Abstract
    In this paper, we propose a framework to analyze the large-scaled multicore power grid network statistically by first building a simplified multicore power supply distribution model. We then apply the Modified Nodal Analysis (MNA) method on a simplified power gird circuit. Under such a framework, most statistical approaches, including Monte Carlo (MC), Importance Sampling, and Stochastic Spectrum Analysis, can be applied to analyze the process-induced variation of homogeneous multicore power grid networks. In the experiment, we focus on the subthreshold leakage current variations, which are modeled as lognormal distribution random variables, by using MC approach as an example to demonstrate the feasibility of such a framework.
  • Keywords
    importance sampling; log normal distribution; power grids; statistical analysis; Monte Carlo method; homogeneous multicore power grid networks; importance sampling; large-scaled multicore power grid networks; lognormal distribution random variables; modified nodal analysis; multicore power supply distribution model; simplified power gird circuit; statistical analysis; stochastic spectrum analysis; subthreshold leakage current variations; Frequency; Leakage current; Monte Carlo methods; Multicore processing; Power grids; Power supplies; Power system modeling; Random variables; Statistical analysis; Stochastic processes; Multicore; Power Grid Network; Process Variation; Statistical Analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    ASIC, 2009. ASICON '09. IEEE 8th International Conference on
  • Conference_Location
    Changsha, Hunan
  • Print_ISBN
    978-1-4244-3868-6
  • Electronic_ISBN
    978-1-4244-3870-9
  • Type

    conf

  • DOI
    10.1109/ASICON.2009.5351259
  • Filename
    5351259