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Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F22%3APU144257" target="_blank" >RIV/00216305:26620/22:PU144257 - isvavai.cz</a>

  • Result on the web

    <a href="https://onlinelibrary.wiley.com/doi/10.1002/aisy.202200001" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1002/aisy.202200001</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1002/aisy.202200001" target="_blank" >10.1002/aisy.202200001</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

  • Original language description

    Memristive electronic synapses are attractive to construct artificial neural networks (ANNs) for neuromorphic computing systems, owing to their excellent electronic performance, high integration density, and low cost. However, the necessity of initializing their conductance through a forming process requires additional peripheral hardware and complex programming algorithms. Herein, the first fabrication of memristors that are initially in low-resistive state (LRS) is reported, which exhibit homogenous initial resistance and switching voltages. When used as electronic synapses in a neuromorphic system to classify images from the CIFAR-10 dataset (Canadian Institute For Advanced Research), the memristors offer x1.83 better throughput per area and consume x0.85 less energy than standard memristors (i.e., with the necessity of forming), which stems from approximate to 63% better density and approximate to 17% faster operation. It is demonstrated in the results that tuning the local properties of materials embedded in memristive electronic synapses is an attractive strategy that can lead to an improved neuromorphic performance at the system level.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20202 - Communication engineering and systems

Result continuities

  • Project

    <a href="/en/project/LM2018110" target="_blank" >LM2018110: CzechNanoLab research infrastructure</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2022

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Advanced Intelligent Systems

  • ISSN

    2640-4567

  • e-ISSN

  • Volume of the periodical

    4

  • Issue of the periodical within the volume

    3

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    9

  • Pages from-to

    2200001-220001

  • UT code for WoS article

    000771007900001

  • EID of the result in the Scopus database