Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

Kaichen Zhu; Mohammad Reza Mahmoodi; Zahra Fahimi; Yiping Xiao; Tao Wang; Kristýna Bukvišová; Miroslav Kolíbal; Juan B. Roldan; David Perez; Fernando Aguirre; Mario Lanza

doi:10.1002/aisy.202200001

Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

Kaichen Zhu, Mohammad Reza Mahmoodi, Zahra Fahimi, Yiping Xiao, Tao Wang, Kristýna Bukvišová, Miroslav Kolíbal, Juan B. Roldan, David Perez, Fernando Aguirre, Mario Lanza

Research output: Contribution to journal › Article › peer-review

Abstract

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 ×1.83 better throughput per area and consume ×0.85 less energy than standard memristors (i.e., with the necessity of forming), which stems from ≈63% better density and ≈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.

Original language	English (US)
Pages (from-to)	2200001
Journal	Advanced Intelligent Systems
DOIs	https://doi.org/10.1002/aisy.202200001
State	Published - Mar 21 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-04-20
Acknowledgements: Supported by the Ministry of Science and Technology of China (grant no. 2018YFE0100800), the National Natural Science Foundation of China (grants no. 11661131002, 61874075), and the Ministry of Finance of China (grant no. SX21400213), the 111 Project from the State Administration of Foreign Experts Affairs of China, the Collaborative Innovation Centre of Suzhou Nano Science &Technology, the Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, and the Priority Academic Program Development of Jiangsu Higher Education Institutions. M.L. acknowledges generous support from the Baseline funding program of the King Abdullah University of Science and Technology. Czech Nano Lab projectLM2018110 funded by MEYS CR is gratefully acknowledged for the financial support of the measurements at CEITEC Nano Research Infrastructure.

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https://repository.kaust.edu.sa/bitstream/10754/676322/1/Advanced%20Intelligent%20Systems%20-%202022%20-%20Zhu%20-%20Memristors%20with%20Initial%20Low%e2%80%90Resistive%20State%20for%20Efficient%20Neuromorphic%20Systems.pdf

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title = "Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems",

abstract = "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 ×1.83 better throughput per area and consume ×0.85 less energy than standard memristors (i.e., with the necessity of forming), which stems from ≈63% better density and ≈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.",

author = "Kaichen Zhu and Mahmoodi, {Mohammad Reza} and Zahra Fahimi and Yiping Xiao and Tao Wang and Krist{\'y}na Bukvi{\v s}ov{\'a} and Miroslav Kol{\'i}bal and Roldan, {Juan B.} and David Perez and Fernando Aguirre and Mario Lanza",

note = "KAUST Repository Item: Exported on 2022-04-20 Acknowledgements: Supported by the Ministry of Science and Technology of China (grant no. 2018YFE0100800), the National Natural Science Foundation of China (grants no. 11661131002, 61874075), and the Ministry of Finance of China (grant no. SX21400213), the 111 Project from the State Administration of Foreign Experts Affairs of China, the Collaborative Innovation Centre of Suzhou Nano Science &Technology, the Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, and the Priority Academic Program Development of Jiangsu Higher Education Institutions. M.L. acknowledges generous support from the Baseline funding program of the King Abdullah University of Science and Technology. Czech Nano Lab projectLM2018110 funded by MEYS CR is gratefully acknowledged for the financial support of the measurements at CEITEC Nano Research Infrastructure.",

year = "2022",

month = mar,

day = "21",

doi = "10.1002/aisy.202200001",

language = "English (US)",

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AU - Zhu, Kaichen

AU - Mahmoodi, Mohammad Reza

AU - Fahimi, Zahra

AU - Xiao, Yiping

AU - Wang, Tao

AU - Bukvišová, Kristýna

AU - Kolíbal, Miroslav

AU - Roldan, Juan B.

AU - Perez, David

AU - Aguirre, Fernando

AU - Lanza, Mario

N1 - KAUST Repository Item: Exported on 2022-04-20 Acknowledgements: Supported by the Ministry of Science and Technology of China (grant no. 2018YFE0100800), the National Natural Science Foundation of China (grants no. 11661131002, 61874075), and the Ministry of Finance of China (grant no. SX21400213), the 111 Project from the State Administration of Foreign Experts Affairs of China, the Collaborative Innovation Centre of Suzhou Nano Science &Technology, the Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, and the Priority Academic Program Development of Jiangsu Higher Education Institutions. M.L. acknowledges generous support from the Baseline funding program of the King Abdullah University of Science and Technology. Czech Nano Lab projectLM2018110 funded by MEYS CR is gratefully acknowledged for the financial support of the measurements at CEITEC Nano Research Infrastructure.

PY - 2022/3/21

Y1 - 2022/3/21

N2 - 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 ×1.83 better throughput per area and consume ×0.85 less energy than standard memristors (i.e., with the necessity of forming), which stems from ≈63% better density and ≈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.

AB - 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 ×1.83 better throughput per area and consume ×0.85 less energy than standard memristors (i.e., with the necessity of forming), which stems from ≈63% better density and ≈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.

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DO - 10.1002/aisy.202200001

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SN - 2640-4567

SP - 2200001

JO - Advanced Intelligent Systems

JF - Advanced Intelligent Systems

ER -

Memristors with Initial Low-Resistive State for Efficient Neuromorphic Systems

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