A Multi-Value 3D Crossbar Array Nonvolatile Memory Based on Pure Memristors

© 2022, The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1140/epjs/s11734-022-00576-9How to improve the storage density and solve the sneak path current problem has become the key to the design of nonvolatile memristive memory. In this paper, a high storage density and high reading/writing speed 3D crossbar array non-volatile memory based on pure memristors is proposed. The main works are as follows: (1) an extensible memristive cluster is proposed, (2) a memristive switch is designed, and (3) a 3D crossbar array non-volatile memory is constructed. The memory cell of the 3D crossbar array non-volatile memory is constructed by pure memristors and can be extended by adding memristor in a memristive cluster or adding memristive clusters in a memory cell to realize multi-value storage. The memristive switch can effectively reduce the sneak path current effect. The pure memristive memory cell solves the conflict between the storage density and sneak path current effect and greatly improves the storage density of memory cells. Furthermore, the 3D cross-array structure allows different memory cells on the same layer or different layers to be read and written in parallel, which greatly improves the speed of reading and writing. Simulations with PSpice verifies that the proposed memristive cluster can realize stable multi-value storage, has higher storage density, faster reading and writing speed, fewer input ports and output ports, better stability, and lower power consumption. Moreover, the structure proposed in this paper can also be used in the circuit design of the neuromorphic network, logic circuit, and other memristive circuits.Peer reviewe

HMIAN: a Hierarchical Mapping and Interactive Attention Data Fusion Network for Traffic Forecasting

© 2022 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/JIOT.2022.3196461With the development of intelligent transportation system (ITS), the vital technology of ITS, short-term traffic forecasting, gains increasing attention. However, the existing prediction models ignore the impact of urban functional zones on traffic data, resulting in inaccurate extractions of dynamic spatial relationships from network. Furthermore, how to calculate the influence of external factors such as weather and holidays on traffic is an unsolved problem. This paper proposes a spatio-temporal hierarchical mapping and interactive attention network (HMIAN), which extracts the spatial features from traffic network by constructing functional zones, and designs an effective external factors fusion method. HMIAN uses the hierarchical mapping structure to aggregate the roads into functional zones, calculate the interaction between functional zones and feed this information back to the spatial features. And the interactive attention mechanism is utilized to fuse the traffic data with external factors effectively, and extracts temporal features. In addition, some experiments were carried out on three real traffic data sets. First, experiment results show that the proposed model better prediction performance compared with other existing approaches in more complex traffic network. Second, the longitudinal comparison experiment verifies that the hierarchical mapping structure is effective in extracting spatial features in complex road network. Finally, the influence of different external factors and fusion methods on traffic prediction are compared, which provides a consult for subsequent research on the influence of external factors.Peer reviewe

A multi-stable memristor and its application in a neural network

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Nowadays, there is a lot of study on memristorbased systems with multistability. However, there is no study on memristor with multistability. This brief constructs a mathematical memristor model with multistability. The origin of the multi-stable dynamics is revealed using standard nonlinear theory as well as circuit and system theory. Moreover, the multi-stable memristor is applied to simulate a synaptic connection in a Hopfield neural network. The memristive neural network successfully generates infinitely many coexisting chaotic attractors unobserved in previous Hopfield-type neural networks. The results are also confirmed in analog circuits based on commercially available electronic elements.Peer reviewe

Design of Artificial Neurons of Memristive Neuromorphic Networks Based on Biological Neural Dynamics and Structures

© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/TCSI.2023.3332496Memristive neuromorphic networks have great potentialand advantage in both technology and computationalprotocols for artificial intelligence. Efficient hardware design ofbiological neuron models forms the core of research problems inneuromorphic networks. However, most of the existing researchhas been based on logic or integrated circuit principles, limitedto replicating simple integrate-and-fire behaviors, while morecomplex firing characteristics have relied on the inherent propertiesof the devices themselves, without support from biologicalprinciples. This paper proposes a memristor-based neuron circuitsystem (MNCS) according to the microdynamics of neuronsand complex neural cell structures. It leverages the nonlinearityand non-volatile characteristics of memristors to simulate thebiological functions of various ion channels. It is designed basedon the Hodgkin-Huxley (HH) model circuit, and the parametersare adjusted according to each neuronal firing mechanism. BothPSpice simulations and practical experiments have demonstratedthat MNCS can replicate 24 types of repeating biological neuronalbehaviors. Furthermore, the results from the Joint Inter-spikeInterval(JISI) experiment indicate that as the background noiseincreases, MNCS exhibits pulse emission characteristics similarto those of biological neurons.Peer reviewe

A Triple-Memristor Hopfield Neural Network With Space Multi-Structure Attractors And Space Initial-Offset Behaviors

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TCAD.2023.3287760Memristors have recently demonstrated great promise in constructing memristive neural networks with complex dynamics. This paper proposes a memristive Hopfield neural network with three memristive coupling synaptic weights. The complex dynamical behaviors of the triple-memristor Hopfield neural network (TM-HNN), which have never been observed in previous Hopfield-type neural networks, include space multi-structure chaotic attractors and space initial-offset coexisting behaviors. Bifurcation diagrams, Lyapunov exponents, phase portraits, Poincaré maps, and basins of attraction are used to reveal and examine the specific dynamics. Theoretical analysis and numerical simulation show that the number of space multi-structure attractors can be adjusted by changing the control parameters of the memristors, and the position of space coexisting attractors can be changed by switching the initial states of the memristors. Extreme multistability emerges as a result of the TM-HNN’s unique dynamical behaviors, making it more suitable for applications based on chaos. Moreover, a digital hardware platform is developed and the space multi-structure attractors as well as the space coexisting attractors are experimentally demonstrated. Finally, we design a pseudo-random number generator to explore the potential application of the proposed TM-HNN.Peer reviewe

User Multi-Interest Modeling for Behavioral Cognition

Representation modeling based on user behavior sequences is an important direction in user cognition. In this study, we propose a novel framework called Multi-Interest User Representation Model. Specifically, the model consists of two sub-models. The first sub-module is used to encode user behaviors in any period into a super-high dimensional sparse vector. Then, we design a self-supervised network to map vectors in the first module to low-dimensional dense user representations by contrastive learning. With the help of a novel attention module which can learn multi-interests of user, the second sub-module achieves almost lossless dimensionality reduction. Experiments on several benchmark datasets show that our approach works well and outperforms state-of-the-art unsupervised representation methods in different downstream tasks.Comment: during peer revie

Neural Bursting and Synchronization Emulated by Neural Networks and Circuits

© 2021 IEEE - All rights reserved. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TCSI.2021.3081150Nowadays, research, modeling, simulation and realization of brain-like systems to reproduce brain behaviors have become urgent requirements. In this paper, neural bursting and synchronization are imitated by modeling two neural network models based on the Hopfield neural network (HNN). The first neural network model consists of four neurons, which correspond to realizing neural bursting firings. Theoretical analysis and numerical simulation show that the simple neural network can generate abundant bursting dynamics including multiple periodic bursting firings with different spikes per burst, multiple coexisting bursting firings, as well as multiple chaotic bursting firings with different amplitudes. The second neural network model simulates neural synchronization using a coupling neural network composed of two above small neural networks. The synchronization dynamics of the coupling neural network is theoretically proved based on the Lyapunov stability theory. Extensive simulation results show that the coupling neural network can produce different types of synchronous behaviors dependent on synaptic coupling strength, such as anti-phase bursting synchronization, anti-phase spiking synchronization, and complete bursting synchronization. Finally, two neural network circuits are designed and implemented to show the effectiveness and potential of the constructed neural networks.Peer reviewe

Exploiting Behavioral Consistence for Universal User Representation

User modeling is critical for developing personalized services in industry. A common way for user modeling is to learn user representations that can be distinguished by their interests or preferences. In this work, we focus on developing universal user representation model. The obtained universal representations are expected to contain rich information, and be applicable to various downstream applications without further modifications (e.g., user preference prediction and user profiling). Accordingly, we can be free from the heavy work of training task-specific models for every downstream task as in previous works. In specific, we propose Self-supervised User Modeling Network (SUMN) to encode behavior data into the universal representation. It includes two key components. The first one is a new learning objective, which guides the model to fully identify and preserve valuable user information under a self-supervised learning framework. The other one is a multi-hop aggregation layer, which benefits the model capacity in aggregating diverse behaviors. Extensive experiments on benchmark datasets show that our approach can outperform state-of-the-art unsupervised representation methods, and even compete with supervised ones.Comment: Preprint of accepted AAAI2021 pape

WIYN Open Cluster Study 89. M48 (NGC 2548) 2: Lithium Abundances in the 420 Myr Open Cluster M48 From Giants Through K Dwarfs

We consider WIYN/Hydra spectra of 329 photometric candidate members of the 420-Myr-old open cluster M48, and report Lithium detections or upper limits for 234 members and likely members. The 171 single members define a number of notable Li-mass trends, some delineated even more clearly than in Hyades/Praesepe: The giants are consistent with subgiant Li dilution and prior MS Li depletion due to rotational mixing. A dwarfs (8600-7700K) have upper limits higher than the presumed initial cluster Li abundance. Two of five late A dwarfs (7700- 7200K) are Li-rich, possibly due to diffusion, planetesimal accretion, and/or engulfment of hydrogen-poor planets. Early F dwarfs already show evidence of Li depletion seen in older clusters. The Li-$T_{\rm eff}$ trends of the Li Dip (6675-6200K), Li Plateau (6200-6000K), and G and K dwarfs (6000-4000K) are very clearly delineated and are intermediate to those of the 120-Myr-old Pleiades and 650-Myr-old Hyades/Praesepe, which suggests a sequence of Li depletion with age. The cool side of the Li Dip is especially well-defined with little scatter. The Li-$T_{\rm eff}$ trend is very tight in the Li Plateau and early G dwarfs, but scatter increases gradually for cooler dwarfs. These patterns support and constrain models of the universally dominant Li depletion mechanism for FGK dwarfs, namely rotational mixing due to angular momentum loss; we discuss how diffusion and gravity-wave driven mixing may also play roles. For late-G/K dwarfs, faster rotators show higher Li than slower rotators, and we discuss possible connections between angular momentum loss and Li depletion.Comment: 29 pages, 10 figures, 5 tables, accepted for publication in the Astrophysical Journa