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

被引：276

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

共 50 条

[21] A Brain-Inspired Cognitive System that Mimics the Dynamics of Human Thought
Ji, Yuehu
Gamez, David
Huyck, Christian
ARTIFICIAL INTELLIGENCE XXXV (AI 2018), 2018, 11311 : 50 - 62
[22] Brain-Inspired Affective Empathy Computational Model and Its Application on Altruistic Rescue Task
Feng, Hui
Zeng, Yi
Lu, Enmeng
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 2022, 16
[23] Natural Organic Materials Based Memristors and Transistors for Artificial Synaptic Devices in Sustainable Neuromorphic Computing Systems
Tanim, Md Mehedi Hasan
Templin, Zoe
Zhao, Feng
MICROMACHINES, 2023, 14 (02)
[24] A brain-inspired spiking neural network model with temporal encoding and learning
Yu, Qiang
Tang, Huajin
Tan, Kay Chen
Yu, Haoyong
NEUROCOMPUTING, 2014, 138 : 3 - 13
[25] Compact Model of Memristors and Its Application in Computing Systems
Li, Hai
Hu, Miao
2010 DESIGN, AUTOMATION & TEST IN EUROPE (DATE 2010), 2010, : 673 - 678
[26] Reliable Brain-inspired AI Accelerators using Classical and Emerging Memories
Yayla, Mikail
Thomann, Simon
Islam, Md Mazharul
Wei, Ming-Liang
Ho, Shu-Yin
Aziz, Ahmedullah
Yang, Chia-Lin
Chen, Jian-Jia
Amrouch, Hussam
2023 IEEE 41ST VLSI TEST SYMPOSIUM, VTS, 2023,
[27] A Hybrid Loop Closure Detection Method Based on Brain-Inspired Models
Li, Jiaxin
Tang, Huajin
Yan, Rui
IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS, 2022, 14 (04) : 1532 - 1543
[28] Stylistic Composition of Melodies Based on a Brain-Inspired Spiking Neural Network
Liang, Qian
Zeng, Yi
FRONTIERS IN SYSTEMS NEUROSCIENCE, 2021, 15
[29] A brain-inspired robot pain model based on a spiking neural network
Feng, Hui
Zeng, Yi
FRONTIERS IN NEUROROBOTICS, 2022, 16
[30] A Brain-Inspired Causal Reasoning Model Based on Spiking Neural Networks
Fang, Hongjian
Zeng, Yi
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,

← 1 2 3 4 5 →