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Beyond Memristors: Neuromorphic Computing Using Meminductors

Wang, Frank Zhigang (2023) Beyond Memristors: Neuromorphic Computing Using Meminductors. Micromachines, 14 (2). Article Number 486. ISSN 2072-666X. (doi:10.3390/mi14020486) (KAR id:100164)

Abstract

Resistors with memory (memristors), inductors with memory (meminductors) and capacitors with memory (memcapacitors) play different roles in novel computing architectures. We found that a coil with a magnetic core is an inductor with memory (meminductor) in terms of its inductance L(q) being a function of charge q. The history of the current passing through the coil is remembered by the magnetization inside the magnetic core. Such a meminductor can play a unique role (that cannot be played by a memristor) in neuromorphic computing, deep learning and brain-inspired computers since the time constant of a neuromorphic RLC circuit is jointly determined by the inductance L and capacitance C, rather than the resistance R. As an experimental verification, this newly invented meminductor was used to reproduce the observed biological behavior of amoebae (the memorizing, timing and anticipating mechanisms). In conclusion, a beyond-memristor

computing paradigm is theoretically sensible and experimentally practical.

Item Type: Article
DOI/Identification number: 10.3390/mi14020486
Uncontrolled keywords: memristor; meminductor; novel computing architectures; non-Turing machine; neuromorphic computing; deep learning; brain-inspired computers
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Funders: European Commission (https://ror.org/00k4n6c32)
Depositing User: Frank Wang
Date Deposited: 21 Feb 2023 21:24 UTC
Last Modified: 04 Mar 2024 17:27 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/100164 (The current URI for this page, for reference purposes)

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