Publication detail

Evaluation of Memristor Models for Large Crossbar Structures

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

Original Title

Evaluation of Memristor Models for Large Crossbar Structures

English Title

Evaluation of Memristor Models for Large Crossbar Structures

Type

conference paper

Language

en

Original Abstract

This paper is focused on comparing selected SPICE models of TiO2 memristors with respect to time- and memory requirements in the simulation of very large artificial neural networks, which are most likely the first real-world applications of memristors as analog memories. All models were implemented as HSPICE macros and simulated in a Multilayer Perceptron artificial neural network with variable configuration. The results show that after applying modifications to the models in order to prevent numerical overflows it is possible to simulate networks with tens of thousands of memristors.

English abstract

This paper is focused on comparing selected SPICE models of TiO2 memristors with respect to time- and memory requirements in the simulation of very large artificial neural networks, which are most likely the first real-world applications of memristors as analog memories. All models were implemented as HSPICE macros and simulated in a Multilayer Perceptron artificial neural network with variable configuration. The results show that after applying modifications to the models in order to prevent numerical overflows it is possible to simulate networks with tens of thousands of memristors.

Keywords

Memristor, model, SPICE, neural networks

Released

19.04.2016

Publisher

IEEE

Location

Košice

ISBN

978-1-5090-1674-7

Book

Proceedings of the 26th International Conference RADIOELEKTRONIKA 2016

Pages from

91

Pages to

94

Pages count

4

URL

BibTex


@inproceedings{BUT128502,
  author="Zdeněk {Kolka} and Dalibor {Biolek} and Viera {Biolková} and Zdeněk {Biolek}",
  title="Evaluation of Memristor Models for Large Crossbar Structures",
  annote="This paper is focused on comparing selected SPICE models of TiO2 memristors with respect to time- and memory requirements in the simulation of very large artificial neural networks, which are most likely the first real-world applications of memristors as analog memories. All models were implemented as HSPICE macros and simulated in a Multilayer Perceptron artificial neural network with variable configuration. The results show that after applying modifications to the models in order to prevent numerical overflows it is possible to simulate networks with tens of thousands of memristors.",
  address="IEEE",
  booktitle="Proceedings of the 26th International Conference RADIOELEKTRONIKA 2016",
  chapter="128502",
  doi="10.1109/RADIOELEK.2016.7477423",
  howpublished="electronic, physical medium",
  institution="IEEE",
  year="2016",
  month="april",
  pages="91--94",
  publisher="IEEE",
  type="conference paper"
}