Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/LM2018110" target="_blank" >LM2018110: Výzkumná infrastruktura CzechNanoLab</a><br>

Návaznosti

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

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

Advanced Intelligent Systems

ISSN

2640-4567

e-ISSN

—

Svazek periodika

4

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

2200001-220001

Kód UT WoS článku

000771007900001

EID výsledku v databázi Scopus

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