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Statistically inspired multi-shift Arnoldi projection for on-chip interconnects

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Listed:
  • Malik, Rahila
  • Alam, Mehboob
  • Muhammad, Shah
  • Hussain, Rashida
  • Ali, Asghar
  • Akram, Namra
  • Duraihem, Faisal Zaid
  • Haq, Anwar Ul

Abstract

Model order reduction of electronic devices and on-chip interconnects plays an important role in determining the performance of very large scale IC (Integrated Circuit) designs. The ever shrinking process technology continues to increase complexity of these systems with the current Intel device technology node standing at 10 nm. In the analysis of high-speed IC interconnects, the reduced model is often required to approximate the original system in the desired frequency range. In this paper, an efficient Statistically Inspired Multi-shift Arnoldi (SIMA) Method is proposed, which dynamically selects normally distributed shifts to determine the projection matrix. The SIMA is implemented by developing an iterative algorithm for approximation of large-scale systems as an eigenvalue problem using statistically generated interpolation points. The motivation of selecting these points weighted with the Gaussian kernel is derived from the fact that there exist many natural phenomenon, which follow normal distribution. Simulation results have shown better accuracy for the proposed method as compared to other existing model order reduction techniques.

Suggested Citation

  • Malik, Rahila & Alam, Mehboob & Muhammad, Shah & Hussain, Rashida & Ali, Asghar & Akram, Namra & Duraihem, Faisal Zaid & Haq, Anwar Ul, 2021. "Statistically inspired multi-shift Arnoldi projection for on-chip interconnects," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 418-428.
    • Handle: RePEc:eee:matcom:v:190:y:2021:i:c:p:418-428
    DOI: 10.1016/j.matcom.2021.05.025
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    References listed on IDEAS

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    1. Xiao-Long Wang & Yao-Lin Jiang, 2013. "Two-sided projection methods for model reduction of MIMO bilinear systems," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 19(6), pages 575-592, December.
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