SRMC: A Multibit Memristor Crossbar for Self-Renewing Image Mask

Shang, Liuting; Duan, Shukai; Wang, Lidan; Huang, Tingwen

doi:10.1109/tvlsi.2018.2844463

Cited by 9 publications

(8 citation statements)

References 58 publications

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“…A study conducted by Yang et al [38] showed an improved memristive cell neural network as well as an adaptive thresholding algorithm based on spatial distribution, and they achieved the edge extraction of colour images. The paper [48] discussed a memristive mask circuit based on the computation-in-memory (CIM) architecture, which is shown in Figure 10. The core of this mask circuit was a multi-bit analogue adder based on the memristor crossbar array, which selects the memristive cells to be accessed through the row-column switches.…”

Section: Othersmentioning

confidence: 99%

Memristive System Based Image Processing Technology: A Review and Perspective

Dong

Zhou

et al. 2021

Electronics

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As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of image processing has become a key issue that has bothered scholars working on images for a long time. Memristors with non-volatile, synapse-like, as well as integrated storage-and-computation properties can be used to build intelligent processing systems that are closer to the structure and function of biological brains. They are also of great significance when constructing new intelligent image processing systems with non-Von Neumann architecture and for achieving the integrated storage and computation of image data. Based on this, this paper analyses the mathematical models of memristors and discusses their applications in conventional image processing based on memristive systems as well as image processing based on memristive neural networks, to investigate the potential of memristive systems in image processing. In addition, recent advances and implications of memristive system-based image processing are presented comprehensively, and its development opportunities and challenges in different major areas are explored as well. By establishing a complete spectrum of image processing technologies based on memristive systems, this review attempts to provide a reference for future studies in the field, and it is hoped that scholars can promote its development through interdisciplinary academic exchanges and cooperation.

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Section: Othersmentioning

confidence: 99%

Memristive System Based Image Processing Technology: A Review and Perspective

Dong

Zhou

et al. 2021

Electronics

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“…Figure 11 shows the convolution operator used in the weighted average filter, where the operator (a) [34] is implemented by two horizontal and vertical convolutions to store the convolution kernel operator in the memristor array in the training mode. Please note that the sum voltage is 1/12 times the read voltage, so the calculation is weighted, and no additional division is required.…”

Section: Color Image Denoising Based On Different Filter Operatorsmentioning

confidence: 99%

“…Moreover, [33] proposed a memristor cross array, which greatly reduces the power consumption of the cross array and reduces the area of the cross array, but no specific application is given. A self-renewing mask circuit is proposed in [34], which uses a multibit memristor cross array to achieve convolution operation and realizes mean filtering and edge extraction. However, a 3 × 3 convolution kernel is used to perform sliding convolution on the entire image.…”

Section: Introductionmentioning

confidence: 99%

Convolution Kernel Operations on a Two-Dimensional Spin Memristor Cross Array

Zhu

Wang

Dong

et al. 2020

Sensors

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In recent years, convolution operations often consume a lot of time and energy in deep learning algorithms, and convolution is usually used to remove noise or extract the edges of an image. However, under data-intensive conditions, frequent operations of the above algorithms will cause a significant memory/communication burden to the computing system. This paper proposes a circuit based on spin memristor cross array to solve the problems mentioned above. First, a logic switch based on spin memristors is proposed, which realizes the control of the memristor cross array. Secondly, a new type of spin memristor cross array and peripheral circuits is proposed, which realizes the multiplication and addition operation in the convolution operation and significantly alleviates the computational memory bottleneck. At last, the color image filtering and edge extraction simulation are carried out. By calculating the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the image result, the processing effects of different operators are compared, and the correctness of the circuit is verified.

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“…36,62,63 Spintronic memristors combine nonvolatility with the scalability of STT devices, both of which indicate the prospect of highperformance and high-density memory technology. 64 Besides, as presented in Figure 4, spintronic memristors are promising devices in the neuromorphic fields, 65,66 memory chips, 67,68 and temperature sensors. Traditional electronic devices are usually based on nonmagnetic semiconductors and realize some circuit functions by controlling the charge flow.…”

Section: Introductionmentioning

confidence: 99%

From Spintronic Memristors to Quantum Computing

Qin

Sun

Zhou

et al. 2023

ACS Materials Lett.

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The high-speed development of the Internet of Things and artificial intelligence is revolutionizing the world in terms of industrial production, environmental protection, medical treatment, education, daily life, and so on. The powerful and fast computing methods are crucial for the advanced computing technology toward the next generation artificial intelligence. Traditional computing systems have separated logical and storage units, which cause computation time delays and increase power consumption. Spintronic memristors combine the nonvolatile characteristics of memristors with the scalability of a spin-transfer torque device, which can meet the high-speed, low-power, and scalability requirements of quantum computing (QC) for quantitative information processing. This paper reviews the research progress of spintronic memristors based on magnetic tunnel junction (MTJ), domain wall (DW) motion, and spin wave (SW), respectively, focusing on the development and challenges of spintronic memristors for QC. Finally, some problems that need to be solved urgently in the current research are summarized, and the potential applications of spintronic memristors are discussed.

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SRMC: A Multibit Memristor Crossbar for Self-Renewing Image Mask

Cited by 9 publications

References 58 publications

Memristive System Based Image Processing Technology: A Review and Perspective

Memristive System Based Image Processing Technology: A Review and Perspective

Convolution Kernel Operations on a Two-Dimensional Spin Memristor Cross Array

From Spintronic Memristors to Quantum Computing

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