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
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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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
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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
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