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Memristive Behavior
Published in Rupendra Kumar Pachauri, Jitendra Kumar Pandey, Abhishek Sharma, Om Prakash Nautiyal, Mangey Ram, Applied Soft Computing and Embedded System Applications in Solar Energy, 2021
Manish Bilgaye, M. Gurunadha Babu, Y. David Solomon Raju, Adesh Kumar
System performance improvement is a natural never-ending desire instrumental in driving the technological efforts for the development of next-generation high-performance systems. Currently, memristor-based ReRAM (Resistive Random Access Memory) has proven itself powerful through its potential as a leading candidate to mitigate issues on multiple technological fronts. Devices based on memristive ReRAM when coupled with a complementary metal oxide semiconductor (CMOS) transistors have numerous openings related to furtherance of next-generation high-performance systems [17,18]. Basically, memristor is a variable resistance device based on the relationship between charge ‘q’ and the flux ‘ϕ’ with the capacity to retain the previous resistive value based on amount of charge ‘q’ that has flown through it, in the process providing it the most important memory effect, that of nonvolatility in nature. The resistance value/resistive memory can be programmed to attain a particular value, which can be understood as logical binary bits ‘0’ and ‘1’, respectively. The second characteristic behavior of memristor is akin to the synapse of a brain [19].
Evaluation of Nanoscale Memristor Device for Analog and Digital Application
Published in Brajesh Kumar Kaushik, Nanoscale Devices, 2018
Jeetendra Singh, Balwinder Raj
A memristor is basically a resistor accompanied by memory. It is a propitious nonlinear device that became the fourth fundamental electrical circuit component after resistor, capacitor, and inductor. Its nanoscale size and nonvolatile memorizing capability provide more potential to replace conventional data storage devices. In conventional memories such as dynamic random access memory (DRAM), static random access memory (SRAM), and flash memory, data are stored as charge on a capacitor that dissipates with time—and data is eventually lost. In memristor-based memory, the high resistance and low resistance states are stored unlike charge in conventional memory. Since it stores resistance value indefinitely, the memristor can be used as a nonvolatile memory. The brain can be developed with the help of implementing a memristor in an analog circuit since the memristor can also be used as synapses of neurons.
How to Untangle Complex Systems?
Published in Pier Luigi Gentili, Untangling Complex Systems, 2018
The hierarchical structure of the memory can be revolutionized by introducing cells of memristors. A memristor (Chua 1971) is the fourth fundamental circuit element along with the resistor, capacitor, and inductor. A memristor—short for memory resistor—is an electronic component whose resistance is not constant but depends on how much electric charge has flowed in what direction through it in the past. In other words, the memristor remembers its history. When the electric power supply is turned off, the memristor remembers its most recent resistance until it is turned on again (Yang et al. 2013). Cells of memristors can be exploited to devise a RAM that is not anymore volatile. To achieve performances similar to the SRAM, it is necessary that memristors cells are close to CPU. This requirement is not easy to be accomplished with the available technology. However, photons may be used to connect the memory based on memristors with the traditional SRAM working closely to CPU.
Global exponential stability of memristor based uncertain neural networks with time-varying delays via Lagrange sense
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
R. Suresh, M. Syed Ali, Sumit Saroha
The concept of memristor was proposed theoretically by Chua (1971), and the first memristor was created in 2008 in HP Lab (2008). Subsequently, the research works on memristors are gradually increasing, and have become a new hot spot. Memristor is a fourth fundamental circuit element the other three being resistor, capacitor and inductor respectively. Unlike the other three elements, memristor also has an added advantage of variable resistance in addition to memory capability. It is noted that the main benefit of memristors is closer to the synaptic characteristics of the biological nervous system. Memristors behave as artificial synapses which enables the construction of artificial NNs in a better manner. Many Researchers have been analysing on simulation of biological NNs more accurately by constructing MNNs (Chen et al., 2017; Gu et al., 2019). Further, memristor has been successfully adopted in many scientific areas like image and signal processing (Duan et al., 2015), non-volatile memory (Ho et al., 2011) and associative memory (Yang et al., 2016).
H ∞ state estimation for memristive neural networks with randomly occurring DoS attacks
Published in Systems Science & Control Engineering, 2022
Huimin Tao, Hailong Tan, Qiwen Chen, Hongjian Liu, Jun Hu
In 1971, the existence of a two-port component is described in Chua (1971) and named memristor. In 2008, HP Lab completed the physical realization of memristor for the first time. Since then, memristor has attracted the attention of many scholars because of its memory function, extremely low power consumption, nano-scale size and many other advantages. The memory characteristics and learning function of memristors make the neural networks (NNs), composed of memristors, more similar to human brain, which brings new dawn for the development of NNs. Recurrent neural networks (RNNs) using memristors instead of resistors can achieve higher integration, the so-called memristive neural networks (MNNs) are widely valued in information processing, pattern recognition, brain-like research and other fields, and a number of numerous research on MNNs have emerged (Cao et al., 2020; Guo et al., 2015; Liu et al., 2018; Strukov et al., 2008; Wang et al., 2017; Yang et al., 2015; Zhang & Shen, 2013). However, it should be pointed out that a large amount of existing literatures are about continuous-time MNNs (CMNNs). Although the study of discrete-time MNNs (DMNNs) has more practical significance in engineering practice, DMNNs are still in a relatively neglected state compared with the CMNNs because of the failure to find a appropriate method to deal with state-related parameters.
Finite-time stability of fractional-order delayed Cohen–Grossberg memristive neural networks: a novel fractional-order delayed Gronwall inequality approach
Published in International Journal of General Systems, 2022
The memristor is a two-terminal electrical component relating magnetic flux and electric charge. It was initially envisioned by Chua (1971) and realized by HP labs (Strukov et al. 2008). The memristor has the advantages such as small size, low power consumption and large-scale integration. Since the migration of ions in the memristor is very similar to the diffusion process of neurotransmitter in the nerve synapse, the memristor is introduced to simulate the synapse in the neural network to form memristive neural networks (MNNs). Memristors are the best choice to simulate synapses in artificial neural networks due to their advantages such as the low power consumption, passivity, nanometer size and memory characteristics (Wang et al. 2020). Compared with the traditional neural networks, MNNs are more effective. As reported in Yao et al. (2020), the memristor-based convolutional neural network neuromorphic system has an energy efficiency more than 2 orders of magnitude greater than that of state-of-the-art graphics-processing units. Recently, MNNs have been a hot topic and some important contributions (Jia et al. 2020; Rajchakit, Pharunyou, Michal et al. 2020; Rajchakit, Pharunyou, Pramet et al. 2020; Sun et al. 2020; Wen et al. 2020) on MNNs have been made. In Yao et al. (2020), the memristive convolutional neural network neuromorphic system has developed. In Shi et al. (2019), a silk memristor has been developed, which has significant improvement in the switching speed in comparison with other organic memristors.