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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: 30 June 2024 | Viewed by 5016

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
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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

Related Special Issue

Published Papers (4 papers)

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Research

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 681
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
Viewed by 1489
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
Viewed by 1161
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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12 pages, 4346 KiB  
Article
Design and Application of Memristive Balanced Ternary Univariate Logic Circuit
by Xiaoyuan Wang, Xinrui Zhang, Chuantao Dong, Shimul Kanti Nath and Herbert Ho-Ching Iu
Micromachines 2023, 14(10), 1895; https://doi.org/10.3390/mi14101895 - 30 Sep 2023
Cited by 2 | Viewed by 984
Abstract
This paper proposes a unique memristor-based design scheme for a balanced ternary digital logic circuit. First, a design method of a single-variable logic function circuit is proposed. Then, by combining with a balanced ternary multiplexer, some common application-type combinational logic circuits are proposed, [...] Read more.
This paper proposes a unique memristor-based design scheme for a balanced ternary digital logic circuit. First, a design method of a single-variable logic function circuit is proposed. Then, by combining with a balanced ternary multiplexer, some common application-type combinational logic circuits are proposed, including a balanced ternary half adder, multiplier and numerical comparator. The above circuits are all simulated and verified in LTSpice, which demonstrate the feasibility of the proposed scheme. 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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