Brain-inspired computing with memristors: Challenges in devices, circuits, and systems

被引：249

作者：

Zhang, Yang ^{[1
,2
]}

Wang, Zhongrui ^{[2
]}

Zhu, Jiadi ^{[3
]}

Yang, Yuchao ^{[3
]}

Rao, Mingyi ^{[2
]}

Song, Wenhao ^{[2
]}

Zhuo, Ye ^{[2
]}

Zhang, Xumeng ^{[2
,4
,5
]}

Cui, Menglin ^{[6
]}

Shen, Linlin ^{[1
]}

Huang, Ru ^{[3
]}

Joshua Yang, J. ^{[2
]}

机构：

[1] Shenzhen Univ, Sch Comp Sci & Software Engn, Shenzhen 518060, Guangdong, Peoples R China

[2] Univ Massachusetts, Dept Elect & Comp Engn, Amherst, MA 01003 USA

[3] Peking Univ, Inst Microelect, Key Lab Microelect Devices & Circuits MOE, Beijing 100871, Peoples R China

[4] Chinese Acad Sci, Inst Microelect, Key Lab Microelect Device & Integrated Technol, Beijing 100029, Peoples R China

[5] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[6] Univ Nottingham, Sch Comp Sci, Ningbo 315100, Zhejiang, Peoples R China

来源：

APPLIED PHYSICS REVIEWS | 2020年 / 7卷 / 01期

基金：

中国国家自然科学基金;

关键词：

RESISTIVE-SWITCHING MEMORY; PHASE-CHANGE MEMORY; SPIKING NEURAL-NETWORK; RANDOM-ACCESS MEMORY; SYNAPSE DEVICE; CONDUCTANCE LINEARITY; FEATURE-EXTRACTION; CROSSBAR ARRAYS; COMPACT MODEL; MECHANISMS;

D O I：

10.1063/1.5124027

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

This article provides a review of current development and challenges in brain-inspired computing with memristors. We review the mechanisms of various memristive devices that can mimic synaptic and neuronal functionalities and survey the progress of memristive spiking and artificial neural networks. Different architectures are compared, including spiking neural networks, fully connected artificial neural networks, convolutional neural networks, and Hopfield recurrent neural networks. Challenges and strategies for nanoelectronic brain-inspired computing systems, including device variations, training, and testing algorithms, are also discussed.

引用

页数：24

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