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Micromachines
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Novel Computing Architectures and Digital Circuit Designs Using Memristors and...
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Novel Computing Architectures and Digital Circuit Designs Using Memristors and Memristive Systems, 2nd Edition

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "E：Engineering and Technology".

Deadline for manuscript submissions: closed (30 December 2024) | Viewed by 11485

Special Issue Editors

Prof. Dr. Xiaoyuan Wang

E-Mail Website
Guest Editor
School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
Interests: memristor; logic circuits design; chaos; nonlinear circuits; encryption algorithm; neural network
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Herbert Ho-Ching Iu

E-Mail Website
Guest Editor
School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Perth, WA 6009, Australia
Interests: power electronics; chaos; smart grid; renewable energy; nonlinear dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Memristors have shown much promise as a solution for processing-in-memory architectures due to their non-volatile memory retention, high density, low power, nanoscale geometry, and multi-level memory capacity. Novel computing architectures and systems based on memristors are breaking the barriers of traditional von Neumann computing architectures, which are bottlenecked by data movement constraints. With ongoing advances in material science and device physics, physically derived and empirically based memristor models have broadened the ways in which we may design, simulate, and test exotic computing systems and architectures. Integrating memristors with modern CMOS processes technology continued to be explored and has recently led to the commercial availability of several memristor-CMOS VLSI workflows. This has expanded the spectrum of research on memristive crossbar arrays, digital logic circuits, and in-memory processors, which play an important role in neuromorphic computing systems, novel computing architectures, and dynamical memristive networks.

The purpose of this Special Issue on “Novel computing architectures and digital circuit designs using memristors and memristive systems” is to provide a comprehensive overview of memristor fabrication, characterization, and modeling; memristor crossbar arrays, memristor logic circuit designs, and processing-in-memory architectures; and other circuit or system-level applications that harness the dynamical properties of memristors.

Prof. Dr. Xiaoyuan Wang
Prof. Dr. Herbert Ho-Ching Iu
Guest Editors

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Keywords

  • Memristor
  • Memristive systems
  • Memristor crossbar arrays
  • Memristor logic circuits design
  • Modeling and simulation of memristive devices
  • Logic circuits based on memristor and memristive devices
  • Memristive nonlinear circuit design
  • Neuromorphic computing based on memristors and memristive devices

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Related Special Issue

Published Papers (5 papers)

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Research

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15 pages, 5329 KiB  
Article
Dynamics Research of the Hopfield Neural Network Based on Hyperbolic Tangent Memristor with Absolute Value
by Huiyan Gao and Hongmei Xu
Micromachines 2025, 16(2), 228; https://doi.org/10.3390/mi16020228 - 17 Feb 2025
Viewed by 655
Abstract
Neurons in the brain are interconnected through synapses. Local active memristors can both simulate the synaptic behavior of neurons and the action potentials of neurons. Currently, the hyperbolic tangent function-type memristors used for coupling neural networks do not belong to local active memristors. [...] Read more.
Neurons in the brain are interconnected through synapses. Local active memristors can both simulate the synaptic behavior of neurons and the action potentials of neurons. Currently, the hyperbolic tangent function-type memristors used for coupling neural networks do not belong to local active memristors. To take advantage of local active memristors and consider the multi-equilibrium point problem, a cosine function is introduced into the state equation, resulting in the design of an absolute value hyperbolic tangent-type double local active memristor, and it is used as a coupling synapse to replace a synaptic weight in a 3-neuron HNN. Then, basic dynamical analysis methods are used to study the effects of different memristor synapse coupling strengths and different initial conditions on the dynamics of the neural network. The research results indicate that dynamical behavior of memristor Hopfield neural network is closely related to the synaptic coupling strengths and the initial conditions, and this neural network exhibits rich dynamical behaviors, including the coexistence of chaotic and periodic attractors, super-multistability phenomena, etc. Finally, the neural network was implemented using an FPGA development board, verifying the hardware feasibility of this system. Full article
(This article belongs to the Special Issue Novel Computing Architectures and Digital Circuit Designs Using Memristors and Memristive Systems, 2nd Edition)
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18 pages, 4576 KiB  
Article
MARR-GAN: Memristive Attention Recurrent Residual Generative Adversarial Network for Raindrop Removal
by Qiuyue Chai and Yue Liu
Micromachines 2024, 15(2), 217; https://doi.org/10.3390/mi15020217 - 31 Jan 2024
Viewed by 1519
Abstract
Since machine learning techniques for raindrop removal have not been capable of completely removing raindrops and have failed to take into account the constraints of edge devices with limited resources, a novel software-hardware co-designed method with a memristor for raindrop removal, named memristive [...] Read more.
Since machine learning techniques for raindrop removal have not been capable of completely removing raindrops and have failed to take into account the constraints of edge devices with limited resources, a novel software-hardware co-designed method with a memristor for raindrop removal, named memristive attention recurrent residual generative adversarial network (MARR-GAN), is introduced in this research. A novel raindrop-removal network is specifically designed based on attention gate connections and recurrent residual convolutional blocks. By replacing the basic convolution unit with recurrent residual convolution unit, improved capturing of the changes in raindrop appearance over time is achieved, while preserving the position and shape information in the image. Additionally, an attention gate is utilized instead of the original skip connection to enhance the overall structural understanding and local detail preservation, facilitating a more comprehensive removal of raindrops across various areas of the image. Furthermore, a hardware implementation scheme for MARR-GAN is presented in this paper, where deep learning algorithms are seamlessly integrated with neuro inspired computing chips, utilizing memristor crossbar arrays for accelerated real-time image-data processing. Compelling evidence of the efficacy and superiority of MARR-GAN in raindrop removal and image restoration is provided by the results of the empirical study. Full article
(This article belongs to the Special Issue Novel Computing Architectures and Digital Circuit Designs Using Memristors and Memristive Systems, 2nd Edition)
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13 pages, 3034 KiB  
Article
Coexisting Firing Patterns in an Improved Memristive Hindmarsh–Rose Neuron Model with Multi-Frequency Alternating Current Injection
by Mengjiao Wang, Jie Ding, Bingqing Deng, Shaobo He and Herbert Ho-Ching Iu
Micromachines 2023, 14(12), 2233; https://doi.org/10.3390/mi14122233 - 12 Dec 2023
Cited by 3 | Viewed by 2593
Abstract
With the development of memristor theory, the application of memristor in the field of the nervous system has achieved remarkable results and has bright development prospects. Flux-controlled memristor can be used to describe the magnetic induction effect of the neuron. Based on the [...] Read more.
With the development of memristor theory, the application of memristor in the field of the nervous system has achieved remarkable results and has bright development prospects. Flux-controlled memristor can be used to describe the magnetic induction effect of the neuron. Based on the Hindmarsh–Rose (HR) neuron model, a new HR neuron model is proposed by introducing a flux-controlled memristor and a multi-frequency excitation with high–low frequency current superimposed. Various firing patterns under single and multiple stimuli are investigated. The model can exhibit different coexisting firing patterns. In addition, when the memristor coupling strength changes, the multiple stability of the model is eliminated, which is a rare phenomenon. Moreover, an analog circuit is built to verify the numerical simulation results. Full article
(This article belongs to the Special Issue Novel Computing Architectures and Digital Circuit Designs Using Memristors and Memristive Systems, 2nd Edition)
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14 pages, 7610 KiB  
Article
Infrared UAV Target Detection Based on Continuous-Coupled Neural Network
by Zhuoran Yang, Jing Lian and Jizhao Liu
Micromachines 2023, 14(11), 2113; https://doi.org/10.3390/mi14112113 - 18 Nov 2023
Cited by 14 | Viewed by 2752
Abstract
The task of the detection of unmanned aerial vehicles (UAVs) is of great significance to social communication security. Infrared detection technology has the advantage of not being interfered with by environmental and other factors and can detect UAVs in complex environments. Since infrared [...] Read more.
The task of the detection of unmanned aerial vehicles (UAVs) is of great significance to social communication security. Infrared detection technology has the advantage of not being interfered with by environmental and other factors and can detect UAVs in complex environments. Since infrared detection equipment is expensive and data collection is difficult, there are few existing UAV-based infrared images, making it difficult to train deep neural networks; in addition, there are background clutter and noise in infrared images, such as heavy clouds, buildings, etc. The signal-to-clutter ratio is low, and the signal-to-noise ratio is low. Therefore, it is difficult to achieve the UAV detection task using traditional methods. The above challenges make infrared UAV detection a difficult task. In order to solve the above problems, this work drew upon the visual processing mechanism of the human brain to propose an effective framework for UAV detection in infrared images. The framework first determines the relevant parameters of the continuous-coupled neural network (CCNN) through the image’s standard deviation, mean, etc. Then, it inputs the image into the CCNN, groups the pixels through iteration, then obtains the segmentation result through expansion and erosion, and finally, obtains the final result through the minimum circumscribed rectangle. The experimental results showed that, compared with the existing most-advanced brain-inspired image-understanding methods, this framework has the best intersection over union (IoU) (the intersection over union is the overlapping area between the predicted segmentation and the label divided by the joint area between the predicted segmentation and the label) in UAV infrared images, with an average of 74.79% (up to 97.01%), and can effectively realize the task of UAV detection. Full article
(This article belongs to the Special Issue Novel Computing Architectures and Digital Circuit Designs Using Memristors and Memristive Systems, 2nd Edition)
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Review

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22 pages, 6873 KiB  
Review
Recent Advancements in 2D Material-Based Memristor Technology Toward Neuromorphic Computing
by Sungmin Park, Muhammad Naqi, Namgyu Lee, Suyoung Park, Seongin Hong and Byeong Hyeon Lee
Micromachines 2024, 15(12), 1451; https://doi.org/10.3390/mi15121451 - 29 Nov 2024
Viewed by 2751
Abstract
Two-dimensional (2D) layered materials have recently gained significant attention and have been extensively studied for their potential applications in neuromorphic computing, where they are used to mimic the functions of the human brain. Their unique properties, including atomic-level thickness, exceptional mechanical stability, and [...] Read more.
Two-dimensional (2D) layered materials have recently gained significant attention and have been extensively studied for their potential applications in neuromorphic computing, where they are used to mimic the functions of the human brain. Their unique properties, including atomic-level thickness, exceptional mechanical stability, and tunable optical and electrical characteristics, make them highly versatile for a wide range of applications. In this review, we offer a comprehensive analysis of 2D material-based memristors. Furthermore, we examine the ability of 2D material-based memristors to successfully mimic the human brain by referencing their neuromorphic applications. Full article
(This article belongs to the Special Issue Novel Computing Architectures and Digital Circuit Designs Using Memristors and Memristive Systems, 2nd Edition)
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