Overview of Memristor-Based Neural Network Design and Applications

Ye, Longcheng; Gao, Zhixuan; Fu, Jinke; Wang, Ren; Yang, Cihui; Wen, Jing; Wan, Xiang; Ren, Qingying; Gu, Shi‐Pu; Liu, Xiaoyan; Lian, Xiaojuan; Wang, Lei

doi:10.3389/fphy.2022.839243

Cited by 17 publications

(13 citation statements)

References 143 publications

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“…Memristor, with a simple form of a sandwich-like, two-terminal structure but a dynamically reconfigured storage layer, has become the popular principle of artificial neuron [40]. Not only because the simple and scalable structure, but also for the resemblance of ions dynamics to biological neuron, in which ions will migrate inside the interlayer under the electrical inputs, imitating the ions diffusion through the neuron membrane [41].…”

Section: Implementation Of Neuron In Single Devicementioning

confidence: 99%

Emerging functions of two-dimensional materials in memristive neurons

Hong,

Liu,

et al. 2024

J. Phys. Mater.

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Neuromorphic computing (NC), considered as a promising candidate for future computer architecture, can facilitate more biomimetic intelligence while reducing energy consumption. Neuron is one of the critical building blocks of NC systems. Researchers have been engaged in promoting neuron devices with better electrical properties and more biomimetic functions. Two-dimensional (2D) materials, with ultrathin layers, diverse band structures, featuring excellent electronic properties and various sensing abilities, are promised to realize these requirements. Here, the progress of artificial neurons brought by 2D materials is reviewed, from the perspective of electrical performance of neuron devices, from stability, tunability to power consumption and on/off ratio. Rose up to system-level applications, algorithms and hardware implementation of spiking neural network, stochastic neural network and artificial perception system based on 2D materials are reviewed. 2D materials not only facilitate the realization of NC systems but also increase the integration density. Finally, current challenges and perspectives on developing 2D material-based neurons and NC systems are systematically analyzed, from the bottom 2D materials fabrication to novel neural devices, more brain-like computational algorithms and systems.

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Section: Implementation Of Neuron In Single Devicementioning

confidence: 99%

Emerging functions of two-dimensional materials in memristive neurons

Hong,

Liu,

et al. 2024

J. Phys. Mater.

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“…[27][28][29][30][31] Upon the application of an external electrical stimulus, the MM system discloses programmable conductance states. [32][33][34] The prototypical In-memory computing and in-sensor computation using MM systems. a) In a traditional computational platform, the data D are transferred to a processing element, when a function f is implemented on the D, resulting in substantial costs in power and time.…”

Section: Introductionmentioning

confidence: 99%

“…[ 27–31 ] Upon the application of an external electrical stimulus, the MM system discloses programmable conductance states. [ 32–34 ] The prototypical MM operations, enabled by the phase‐change, i.e., thermally induced crystalline–amorphous transitions, tunnel magnetoresistance, viz., spin‐dependent tunnel conductance, and electrochemical reaction, e.g., redox and ion migration, are based on the switching of a dielectric layer in a two‐terminal metal–dielectric–metal configuration. [ 35–37 ] The MM systems, which have a small footprint, low programming energy, high reliability, and short switching time, exhibit a marked contrast in the electrical conductance as a result of the dependence of conductance states on the history of electrical stimuli.…”

Section: Introductionmentioning

confidence: 99%

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

Go,

Lim,

Lee

et al. 2024

Small Science

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Separate memory and processing units are utilized in conventional von Neumann computational architectures. However, regarding the energy and the time, it is costly to shuffle data between the memory and the processing entity, and for data‐intensive applications associated with artificial intelligence, the demand is ever increasing. A paradigm shift in traditional architectures is required, and in‐memory computing is one of the non‐von‐Neumann computing strategies. By harnessing physical signatures of the memory, computing workloads are administered in the same memory element. For in‐memory computing, a wide range of memristive material (MM) systems have been examined. Moreover, developing computing schemes that perform in the same sensory network and that minimize the data shuffle between the processing unit and the sensing element is a requirement, to process large volumes of data efficiently and decrease the energy consumption. In this review, an overview of the switching character and system signature harnessed in three archetypal MM systems is rendered, along with an integrated application survey for developing in‐sensor and in‐memory computing, viz., brain‐inspired or analogue computing, physical unclonable functions, and random number generators. The recent progress in theoretical studies that reveal the structural origin of the fast‐switching ability of the MM system is further summarized.

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“…Memristor has a remarkable contribution in synthesizing neuronal activities in a dynamical model. Memristive systems have a wide range of real-life applications in memristive neural networks [14], secure communication [15], neural computations [16], memory devices [17], etc.…”

Section: Introductionmentioning

confidence: 99%

A physical memristor-based chaotic system and its application in colour image encryption scheme

Das,

Mandal

2023

Phys. Scr.

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This work proposes a physical memristor (TaOx) based new 4D chaotic system with 3D multi-scroll, no equilibrium point, spiking behaviour, coexistence bursting oscillation and multistability. Using this physical memristor-based chaotic system, a novel and efficient colour image encryption algorithm has been developed using a unique box scrambling method and bit-wise XOR operations. Many interesting and new dynamics of a material-based memristive chaotic system are reported here, like 3D multi-scroll chaotic attractors, bursting characteristics, multistability, a neuronal system like spiking behaviours etc using Lyapunov spectrum and bifurcation plots. It is observed that the number of scrolls is changed with the total simulation time. This novel memristive chaotic system has limit cycles with controllable spikes and bursting oscillation. In addition, the system shows chaotic bursting oscillation under a different set of parameters and initial conditions. The coexistence of the bursting phenomena is studied here. The bursting and spiking characteristic is important for material-based memristors in neuromorphic applications. 3D Chaotic multi-scroll and multistability properties make the image encryption method more efficient and secure. Such characteristics are rare in physical memristor-based chaotic systems and using this, the image encryption algorithm is also rare in recent findings. Therefore, a new secure image encryption algorithm for colour images is proposed here, based on the unique box scrambling method, bitwise XOR operation and pseudo-random number generation using the proposed memristive chaotic system. Various tests like NPCR, UACI, histogram analysis, correlation study, information entropy analysis, robustness against external noise, etc have been performed to check the algorithm’s robustness and efficiency and test the capability to resist statistical and differential attacks.

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Overview of Memristor-Based Neural Network Design and Applications

Cited by 17 publications

References 143 publications

Emerging functions of two-dimensional materials in memristive neurons

Emerging functions of two-dimensional materials in memristive neurons

Nonvolatile Memristive Materials and Physical Modeling for In‐Memory and In‐Sensor Computing

A physical memristor-based chaotic system and its application in colour image encryption scheme

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