Detail publikace

(V)TEAM for SPICE Simulation of Memristive Devices With Improved Numerical Performance

BIOLEK, D. KOLKA, Z. BIOLKOVÁ, V. BIOLEK, Z. KVATINSKY, S.

Originální název

(V)TEAM for SPICE Simulation of Memristive Devices With Improved Numerical Performance

Anglický název

(V)TEAM for SPICE Simulation of Memristive Devices With Improved Numerical Performance

Jazyk

en

Originální abstrakt

The paper introduces a set of models of memristive devices for a reliable, accurate and fast analysis of large networks in the SPICE (Simulation Program with Integrated Circuit Emphasis) environment. The modeling starts from the recently introduced TEAM (ThrEshold Adaptive Memristor Model) and VTEAM (Voltage ThrEshold Adaptive Memristor Model). A number of improvements are made towards the stick effect elimination and other numerical renements to make the analysis of large networks fast and accurate. A set of models are proposed that utilize the synergy of several techniques such as window asymmetrization, integration with saturation, state equation preprocessing, scaling, and smoothing. The performance of models is tested in Cadence PSPICE 17.2 and particularly in HSPICE v2017, the latter on a large-scale CNN (Cellular Nonlinear Network) for detecting edges of binary images. The simulations manifest the usability of developed models for fast and reliable operation in networks containing more than one million nodes.

Anglický abstrakt

The paper introduces a set of models of memristive devices for a reliable, accurate and fast analysis of large networks in the SPICE (Simulation Program with Integrated Circuit Emphasis) environment. The modeling starts from the recently introduced TEAM (ThrEshold Adaptive Memristor Model) and VTEAM (Voltage ThrEshold Adaptive Memristor Model). A number of improvements are made towards the stick effect elimination and other numerical renements to make the analysis of large networks fast and accurate. A set of models are proposed that utilize the synergy of several techniques such as window asymmetrization, integration with saturation, state equation preprocessing, scaling, and smoothing. The performance of models is tested in Cadence PSPICE 17.2 and particularly in HSPICE v2017, the latter on a large-scale CNN (Cellular Nonlinear Network) for detecting edges of binary images. The simulations manifest the usability of developed models for fast and reliable operation in networks containing more than one million nodes.

Plný text v Digitální knihovně

Dokumenty

BibTex


@article{BUT170268,
  author="Dalibor {Biolek} and Zdeněk {Kolka} and Viera {Biolková} and Zdeněk {Biolek} and Shahar {Kvatinsky}",
  title="(V)TEAM for SPICE Simulation of Memristive Devices With Improved Numerical Performance",
  annote="The paper introduces a set of models of memristive devices for a reliable, accurate and fast analysis of large networks in the SPICE (Simulation Program with Integrated Circuit Emphasis) environment. The modeling starts from the recently introduced TEAM (ThrEshold Adaptive Memristor Model) and VTEAM (Voltage ThrEshold Adaptive Memristor Model). A number of improvements are made towards the stick effect elimination and other numerical renements to make the analysis of large networks fast and accurate. A set of models are proposed that utilize the synergy of several techniques such as window asymmetrization, integration with saturation, state equation preprocessing, scaling, and smoothing. The performance of models is tested in Cadence PSPICE 17.2 and particularly in HSPICE v2017, the latter on a large-scale CNN (Cellular Nonlinear Network) for detecting edges of binary images. The simulations manifest the usability of developed models for fast and reliable operation in networks containing more than one million nodes.",
  address="IEEE",
  chapter="170268",
  doi="10.1109/ACCESS.2021.3059241",
  howpublished="online",
  institution="IEEE",
  number="2",
  volume="9",
  year="2021",
  month="february",
  pages="30242--30255",
  publisher="IEEE",
  type="journal article in Web of Science"
}