Associative memory realized by a reconfigurable memristive Hopfield neural network

Hu, Shaoxiang; Liu, Y.; Liu, Z; Chen, T. P.; Wang, J.J.; Yu, Qin; Deng, Longjiang; Yin, You; Hosaka, Sumio

doi:10.1038/ncomms8522

Cited by 218 publications

(139 citation statements)

References 38 publications

(40 reference statements)

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“…During the past decade, many scientists have shown a variety of methods of memristor application in hardware design of ANN systems. For instance, in [26] the hybrid CMOS-memristor Hopfield network-based associative memory is demonstrated. While in the work conducted by Guo et al [27], the CMOS-memristor hybrid architecture is applied in the design of 4-bit Hopfield neural ADC.…”

Section: Cmos/memristor Hybrid Network-based Adcmentioning

confidence: 99%

Neural Network-Based Analog-to-Digital Converters

Tankimanova¹,

James²

2018

Memristor and Memristive Neural Networks

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In this chapter, we present an overview of the recent advances in analog-to-digital converter (ADC) neural networks. Biological neural networks consist of natural binarization reflected by the neurosynaptic processes. This natural analog-to-binary conversion ability of neurons can be modeled to emulate analog-to-digital conversion using a set of nonlinear circuit elements and existing artificial neural network models. Since one neuron during processing consumes on average only about half nanowatts of power, neurons can perform highly energy-efficient operations, including pattern recognition. Analog-to-digital conversion itself is an example of simple pattern recognition where input analog signal can be presented in one of the 2 N different patterns for N bits. The classical configuration of neural network-based ADC is Hopfield neural network ADC. Improved designs, such as modified Hopfield network ADC, T-model neural ADC, and multilevel neurons-based neural ADC, will be discussed. In addition, the latest architecture designs of neural ADC such as hybrid complementary metal-oxide semiconductor (CMOS)-memristor Hopfield ADC are covered at the end of this chapter.

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Section: Cmos/memristor Hybrid Network-based Adcmentioning

confidence: 99%

Neural Network-Based Analog-to-Digital Converters

Tankimanova¹,

James²

2018

Memristor and Memristive Neural Networks

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“…And the research on the application of memristors with the common synaptic plasticity in some kind of neural networks has also been conducted. For instance, HfO 2 -based memristors were used in a Hopfield neural network to implement associative memory [9]. The relationship between the resistance of the memristor and the synaptic weight was defined.…”

Section: Introductionmentioning

confidence: 99%

“…The relationship between the resistance of the memristor and the synaptic weight was defined. And the resistances of the memristors were tuned to the target resistances through the application of the voltage pulses on the memristors as the training process [9]. Prezioso et al realized pattern classification by using the neural network based on memristors with synaptic plasticity [10].…”

Section: Introductionmentioning

confidence: 99%

Spike‐Timing‐Dependent Plasticity in Memristors

Shuai¹,

Pan²,

Sun³

2018

Memristor and Memristive Neural Networks

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The spike-timing-dependent plasticity (STDP) characteristic of the memristor plays an important role in the development of neuromorphic network computing in the future. The STDP characteristics were observed in different memristors based on different kinds of materials. The investigation regarding the influences of device hysteresis characteristic, the initial conductance of the memristors, and the waveform of the voltage pulses applied to the memristor as preneuron voltage spike and postneuron voltage spike on the STDP behavior of memristors are reviewed.

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“…The memristance of the memristor were used to calculate the synaptic weights in some cases. In 2015, Wang et al [5] Pavlov experiments showed relations between the memristors and the associative memory, also with the MHN [6] single and multi associative memories have been realised. Tarkov [7] in 2016 conducted experiments on the binary image convergence in associative memory.…”

Section: Introductionmentioning

confidence: 99%

Design of Memristive Hopfield Neural Network using Memristor Bridges

Monurajan¹,

Ruhanbevi²,

Manjula³

2018

IJET

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Artificial Neural Networks are interconnection of neurons inspired from the biological neural network of the brain. ANN is claimed to rule the future, spreads its wings to various areas of interest to name a few such as optimization, information technology, cryptography, image processing and even in medical diagnosis. There are devices which possess synaptic behaviour, one such device is memristor. Bridge circuit of memristors can be combined together to form neurons. Neurons can be made into a network with appropriate parameters to store data or images. Hopfield neural networks are chosen to store the data in associative memory. Hopfield neural networks are a significant feature in ANN which are recurrent in nature and in general are used as associative memory and in solving optimization problems such as the Travelling Salesman Problem. The paper deals on the construction of memristive Hopfield neural network using memristor bridging circuit and its application in the associative memory. This paper also illustrates the experiment with mathematical equations and the associative memory concept of the network using Matlab.

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Associative memory realized by a reconfigurable memristive Hopfield neural network

Cited by 218 publications

References 38 publications

Neural Network-Based Analog-to-Digital Converters

Neural Network-Based Analog-to-Digital Converters

Spike‐Timing‐Dependent Plasticity in Memristors

Design of Memristive Hopfield Neural Network using Memristor Bridges

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