Detail publikace

System-Theoretic Methods for Designing Bio-Inspired Mem-Computing Memristor Cellular Nonlinear Networks

ASCOLI, A. TETZLAFF, R. KANG, SM. S. CHUA, L.

Originální název

System-Theoretic Methods for Designing Bio-Inspired Mem-Computing Memristor Cellular Nonlinear Networks

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

The introduction of nano-memristors in electronics may allow to boost the performance of integrated circuits beyond the Moore era, especially in view of their extraordinary capability to process and store data in the very same physical volume. However, recurring to nonlinear system theory is absolutely necessary for the development of a systematic approach to memristive circuit design. In fact, the application of linear system-theoretic techniques is not suitable to explore thoroughly the rich dynamics of resistance switching memories, and designing circuits without a comprehensive picture of the nonlinear behaviour of these devices may lead to the realization of technical systems failing to operate as desired. Converting traditional circuits to memristive equivalents may require the adaptation of classical methods from nonlinear system theory. This paper extends the theory of time- and space-invariant standard cellular nonlinear networks with first-order processing elements for the case where a single non-volatile memristor is inserted in parallel to the capacitor in each cell. A novel nonlinear system-theoretic method allows to draw a comprehensive picture of the dynamical phenomena emerging in the memristive mem-computing array, beautifully illustrated in the so-called Primary Mosaic for the class of uncoupled memristor cellular nonlinear networks. Employing this new analysis tool it is possible to elucidate, with the support of illustrative examples, how to design variability-tolerant bio-inspired cellular nonlinear networks with second-order memristive cells for the execution of computing tasks or of memory operations. The capability of the class of memristor cellular nonlinear networks under focus to store and process information locally, without the need to insert additional memory units in each cell, may allow to increase considerably the spatial resolution of state-of-the-art purely CMOS sensor-processor arrays. This is of great appeal for edge computing applications, especially since the Internet-of-Things industry is currently calling for the realization of miniaturized, lightweight, low-power, and high-speed mem-computers with sensing capability on board.

Klíčová slova

bio-inspired mem-computing machines; cellular nonlinear networks; memristor; nonlinear circuit theory; nonlinear system theory

Autoři

ASCOLI, A.; TETZLAFF, R.; KANG, SM. S.; CHUA, L.

Vydáno

12. 5. 2021

Nakladatel

Frontiers Media S.A.

ISSN

2673-3013

Periodikum

Frontiers in Nanotechnology

Ročník

3

Číslo

2021

Stát

Švýcarská konfederace

Strany od

1

Strany do

33

Strany počet

33

URL

https://www.frontiersin.org/articles/10.3389/fnano.2021.633026/full

BibTex

@article{BUT183091,
  author="ASCOLI, A. and TETZLAFF, R. and KANG, SM. S. and CHUA, L.",
  title="System-Theoretic Methods for Designing Bio-Inspired Mem-Computing Memristor Cellular Nonlinear Networks",
  journal="Frontiers in Nanotechnology",
  year="2021",
  volume="3",
  number="2021",
  pages="1--33",
  doi="10.3389/fnano.2021.633026",
  issn="2673-3013",
  url="https://www.frontiersin.org/articles/10.3389/fnano.2021.633026/full"
}