Journal ArticleDOI
Synaptic Characteristics of Ag/AgInSbTe/Ta-Based Memristor for Pattern Recognition Applications
TLDR
The synaptic behavior of a Ag/AgInSbTe/Ta (AIST)-based memristor is experimentally demonstrated, and a neural architecture using one AIST Memristor as a synapse is proposed, where both the plus and minus weights of the neural synapses are realized in a single memristive array.Abstract:
The memristor, a promising candidate for synaptic interconnections in artificial neural network, has gained significant attention for application to neuromorphic systems. One common method is using two memristors as one synapse to represent the positive and negative weights. In this paper, the synaptic behavior of a Ag/AgInSbTe/Ta (AIST)-based memristor is experimentally demonstrated. In addition, a neural architecture using one AIST memristor as a synapse is proposed, where both the plus and minus weights of the neural synapses are realized in a single memristive array. Moreover, the memristor-based neural network is extended to a multilayer architecture, and modified memristor-based backpropagation learning rules are implemented on-chip to achieve pattern recognition. The effects of device variations and input noise on the performance of a memristor-based multilayer neural network (MNN) are also described. The proposed MNN is capable of pattern recognition with high success rates and exhibits several advantages, such as good accuracy, high robustness, and noise immunity.read more
Citations
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Journal ArticleDOI
Neuro-Inspired Computing With Emerging Nonvolatile Memorys
TL;DR: This comprehensive review summarizes state of the art, challenges, and prospects of the neuro-inspired computing with emerging nonvolatile memory devices and presents a device-circuit-algorithm codesign methodology to evaluate the impact of nonideal device effects on the system-level performance.
Journal ArticleDOI
Neuromemristive Circuits for Edge Computing: A Review
TL;DR: This paper provides the review of neuromorphic CMOS-memristive architectures that can be integrated into edge computing devices and discusses why the neuromorphic architectures are useful for edge devices and shows the advantages, drawbacks, and open problems in the field of neuromemristive circuits for edge computing.
Journal ArticleDOI
Brain-inspired computing with memristors: Challenges in devices, circuits, and systems
Yang Zhang,Yang Zhang,Zhongrui Wang,Jiadi Zhu,Yuchao Yang,Mingyi Rao,Wenhao Song,Ye Zhuo,Xumeng Zhang,Xumeng Zhang,Menglin Cui,Linlin Shen,Ru Huang,Jianhua Yang +13 more
TL;DR: This article provides a review of current development and challenges in brain-inspired computing with memristors and survey the progress of memristive spiking and artificial neural networks.
Journal ArticleDOI
Memristor-Based Circuit Design for Multilayer Neural Networks
TL;DR: A novel circuit for Memristor-based multilayer neural networks is presented, which can use a single memristor array to realize both the plus and minus weight of the neural synapses.
Journal ArticleDOI
Memristor-Based Neural Network Circuit of Full-Function Pavlov Associative Memory With Time Delay and Variable Learning Rate
TL;DR: The time delay is considered, in order to form associative memory when the food stimulus lags behind the ring stimulus for a certain period of time, and provides a reference for further development of the brain-like systems.
References
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Journal ArticleDOI
Training and operation of an integrated neuromorphic network based on metal-oxide memristors
Mirko Prezioso,Farnood Merrikh-Bayat,Brian D. Hoskins,Gina C. Adam,Konstantin K. Likharev,Dmitri B. Strukov +5 more
TL;DR: The experimental implementation of transistor-free metal-oxide memristor crossbars, with device variability sufficiently low to allow operation of integrated neural networks, in a simple network: a single-layer perceptron (an algorithm for linear classification).
Journal ArticleDOI
An Electronic Synapse Device Based on Metal Oxide Resistive Switching Memory for Neuromorphic Computation
TL;DR: In this article, the multilevel capability of metal oxide resistive switching memory was explored for the potential use as a single-element electronic synapse device for the emerging neuromorphic computation system.
Journal ArticleDOI
Artificial synapse network on inorganic proton conductor for neuromorphic systems
TL;DR: In-plane lateral-coupled oxide-based artificial synapse network coupled by proton neurotransmitters are self-assembled on glass substrates at room-temperature and a strong lateral modulation is observed due to the proton-related electrical-double-layer effect.
Journal ArticleDOI
Pattern classification by memristive crossbar circuits using ex situ and in situ training
TL;DR: In this article, a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods is presented.
Journal ArticleDOI
A correlated nickelate synaptic transistor.
TL;DR: The demonstration of a synaptic transistor with SmNiO₃, a correlated electron system with insulator-metal transition temperature at 130°C in bulk form, and synaptic spike-timing-dependent plasticity learning behaviour is realized.