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As an emerging in-memory element, memristor has been widely used in various neural network circuits to represent the weights and accelerate the calculation. However, the Transformer Network (TN), one of the most important models for machine vision and natural language processing in recent years, has not yet been full-circuit implemented using memristors due to the complex calculation process and data storage. In order to carry out the computation of the TN more efficiently, this work proposes a memristor-based full-circuit implementation of the TN capable of: 1) a memristor crossbar module to preserve the weights of the TN and perform the vector-matrix multiplications; 2) an analog signal memory module to store the analog signal directly in near-memory mode; 3) function circuit modules to achieve five transformations, namely Softmax, Layer Normalization, ReLU, Multiply-add and Residual; 4) a timing signal generation module to schedule operations of the circuit. The proposed TN circuit can complete all calculations directly based on the analog signal without using any analog-digital converter (ADC), digital-analog converter (DAC) and digital memory. In addition, character image recognition experiments are carried out in PSPICE to verify the functional correctness of the designed circuit. The corresponding signal retention rates of the analog memory, the performance of the whole circuit, and the non-idealities of the memristors are also analyzed. The results indicate that the circuit has advantages in terms of area overhead, energy efficiency and anti-noise. [ABSTRACT FROM AUTHOR]

Memristors are widely used to construct multi-scroll/wing chaotic systems with complex dynamics. However, the generation of a multi-scroll/wing attractor is typically not induced by the memristor but depends on other nonlinear functions in the system, which does not take advantage of the unique features of the memristor for chaos-based applications. To address this issue, the present paper introduces a memristor coupling (MC) method to construct a novel memristive Sprott A system (MSAS) through coupling a flux-controlled memristor with multi-piecewise linear memductance into the chaotic Sprott A system. From theoretical analysis and numerical simulations, the MSAS is shown to be able to generate any number of multi-type hidden attractors, including multi-one-scroll, multi-double-scroll and multi-double-wing hidden attractors. In addition, it has two kinds of multistabilities, that is, heterogeneous multistability and homogeneous multistability. Based on these unique properties, different numbers of coexisting heterogeneous hidden attractors and coexisting homogeneous hidden attractors are derived respectively by switching the memristor initial states. These interesting dynamical properties are comprehensively investigated using nonlinear analysis tools. Furthermore, hardware experiments are implemented to demonstrate the feasibility of the MSAS and the effectiveness of the MC method. Finally, a new pseudo-random number generator (PRNG) is proposed to explore the practical applications of the MSAS. Performance evaluation results verify the high-quality randomness of the designed PRNG. [ABSTRACT FROM AUTHOR]

This paper is aimed at optimizing the synchronizability of a complex network when the total of its edge weights is given and fixed. We try to allocate edge weights on a complex network to optimize the network’s synchronizability from the perspective of spectral graph theory. Most of the existing analysis on multilayer networks assumes the weights of intralayer or interlayer edges to be identical. Such a restrictive assumption is not made in this work. Using the graph comparison based method, different edge weights are allocated according to topological features of networks, which is more reasonable and consistent with most physical complex networks. Furthermore, in order to find out the best edge-weight allocation scheme, we carried out numerical simulations on typical duplex networks and real-world networks. The simulation results show that our proposed edge-weight allocation schemes outperform the average, degree-based, and edge betweenness centrality allocations. [ABSTRACT FROM AUTHOR]

In this paper, implementation of memristive neural network with full-function Pavlov associative memory is designed based on a proposed associative memory rule. The designed neural network can well perform the Pavlov associative memory in the network with at least three interconnected neurons. This neural network and the associative memory rule that is partly based on spike-rate-dependent plasticity (SRDP) protocol are inspired by the famous Pavlov’s dog-experiment that demonstrated the interrelation between the “sight of food” and the “ringing.” Besides the learning activity, the proposed network can also perform two kinds of forgetting activities after the learning process is completed: on one hand, when the salivation neuron is stimulated by the food neuron alone, after a period of time, the ring neuron can no longer trigger the salivation neuron; on the other hand, when the salivation neuron is stimulated by the ring neuron alone, at first the salivation neuron can be triggered but after the salivation neuron realizes that the “ringing” is not associated with “food,” the salivation neuron will not be triggered any longer. How to integrate the proposed network into large scale memristive neural network with multiple associations is also introduced. Simulations results demonstrate the correctness of the designs. [ABSTRACT FROM AUTHOR]

In 1971, Dr. Leon Chua, known as the father of nonlinear circuits and cellular neural networks, postulated the existence of memristor, a portmanteau of memory resistor, in his seminal paper: “Memristor—The missing circuit element” published in IEEE Transactions on Circuit Theory, the predecessor of IEEE Transactions on Circuits and Systems—I: Regular Papers. In 2008, Hewlett-Packard researchers made nanomemristor devices for the first time, setting off an upsurge of memristor research. The emergence of nanomemristor devices is expected to realize nonvolatile RAM. Moreover, the integration, power consumption, and read–write speed of the RAM based on memristor are superior to those of traditional RAMs. The hardware network based on memristor synaptic devices is an important development direction of neuromorphic computing. It is a powerful technical candidate to break through the traditional von Neumann computing architecture in the post-Moore era, which will provide a feasible scheme about a technological breakthrough for surpassing Moore’s law. [ABSTRACT FROM AUTHOR]