Detail publikace

Modeling and simulation of large memristive networks

Originální název

Modeling and simulation of large memristive networks

Anglický název

Modeling and simulation of large memristive networks

Jazyk

en

Originální abstrakt

The paper deals with the modeling of memristors operating in extremely large memristive networks such as crossbar structures for memory and computational circuits, memristor-based neural networks or circuits for massively parallel analog computations. Because the non-convergence and other numerical problems increase with increasing complexity of the simulated circuit, suitable models of the individual memristors need to be choicely developed and optimized. Three different models are considered, each representing a specific trade-off between speed and accuracy. Benchmark circuits for testing the applications of various complexities are used for the transient analysis in HSPICE. It is shown how the models can be modified to minimize the simulation time and improve the convergence.

Anglický abstrakt

The paper deals with the modeling of memristors operating in extremely large memristive networks such as crossbar structures for memory and computational circuits, memristor-based neural networks or circuits for massively parallel analog computations. Because the non-convergence and other numerical problems increase with increasing complexity of the simulated circuit, suitable models of the individual memristors need to be choicely developed and optimized. Three different models are considered, each representing a specific trade-off between speed and accuracy. Benchmark circuits for testing the applications of various complexities are used for the transient analysis in HSPICE. It is shown how the models can be modified to minimize the simulation time and improve the convergence.

BibTex


@article{BUT141094,
  author="Dalibor {Biolek} and Zdeněk {Kolka} and Viera {Biolková} and Zdeněk {Biolek} and Milka {Potrebic} and Dejan {Tosic}",
  title="Modeling and simulation of large memristive networks",
  annote="The paper deals with the modeling of memristors operating in extremely large memristive networks such as crossbar structures for memory and computational circuits, memristor-based neural networks or circuits for massively parallel analog computations. Because the non-convergence and other numerical problems increase with increasing complexity of the simulated circuit, suitable models of the individual memristors need to be choicely developed and optimized. Three different models are considered, each representing a specific trade-off between speed and accuracy. Benchmark circuits for testing the applications of various complexities are used for the transient analysis in HSPICE. It is shown how the models can be modified to minimize the simulation time and improve the convergence.",
  address="John Wiley & Sons, Ltd.",
  chapter="141094",
  doi="10.1002/cta.2327",
  howpublished="online",
  institution="John Wiley & Sons, Ltd.",
  number="1",
  volume="2017",
  year="2017",
  month="march",
  pages="50--65",
  publisher="John Wiley & Sons, Ltd.",
  type="journal article in Web of Science"
}