Memristive Neuromorphic Systems Research Articles

Tunable ion energy barrier modulation through aliovalent halide doping for reliable and dynamic memristive neuromorphic systems

Tunable ion energy barrier modulation through aliovalent halide doping for reliable and dynamic memristive neuromorphic systems

Синхронизация мемристивных нейропободных генераторов

The article discusses a memristive neuromorphic system consisting of two analog memristive neurons, Fitzhugh-Nagumo, connected through a memristive device Au/Zr/SiO2/TiN/Ti/SiO2/Si. Such a system, on one hand, imitates the biologically plausible dynamics of ion channels in neurons, and on the other hand, the interneuronal synaptic connections. It has been established that the memristive device, under the influence of a signal from the presynaptic electrical neuron, exhibits synaptic plasticity. Experimental results have shown forced synchronization modes with frequency ratios of 1:1, 2:1, N:1. The developed system effectively reproduces the dynamics of synaptic connections in neural networks of the brain. From an applied point of view, due to the adaptive properties of the memristor, it can be used for the development of neurosensory devices.

Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system.

Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices. In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms. Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage. This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.

Pure-Attention-Based Multifunction Memristive Neuromorphic Circuit and System

The use of memristive neuromorphic circuit and system is a promising solution for next-generation Artificial Intelligence (AI) computing, as it offers possibilities that go beyond conventional GPU-based artificial neural network computing platforms. However, most of the existing memristive neuromorphic circuits and systems are designed for the specific networks, which is lack of universality and flexibility. Therefore, this paper proposes a universal memristive circuit and system framework for pure-attention-based transformer networks to implement multifunction applications on edge devices. Furthermore, the verification of image recognition and speech recognition was achieved by extending the size of the memristor crossbar array macros and reconfiguring the memristor weights without changing the memristive transformer circuit and framework. This paper not only provides a universal edge implementation framework for multifunction applications of the transformer, but also offers a low-power and promising solution for the application of pure-attention-based transformers on edge devices.

Intelligent Control of Seizure-Like Activity in a Memristive Neuromorphic Circuit Based on the Hodgkin–Huxley Model

Memristive neuromorphic systems represent one of the most promising technologies to overcome the current challenges faced by conventional computer systems. They have recently been proposed for a wide variety of applications, such as nonvolatile computer memory, neuroprosthetics, and brain–machine interfaces. However, due to their intrinsically nonlinear characteristics, they present a very complex dynamic behavior, including self-sustained oscillations, seizure-like events, and chaos, which may compromise their use in closed-loop systems. In this work, a novel intelligent controller is proposed to suppress seizure-like events in a memristive circuit based on the Hodgkin–Huxley equations. For this purpose, an adaptive neural network is adopted within a Lyapunov-based nonlinear control scheme to attenuate bursting dynamics in the circuit, while compensating for modeling uncertainties and external disturbances. The boundedness and convergence properties of the proposed control scheme are rigorously proved by means of a Lyapunov-like stability analysis. The obtained results confirm the effectiveness of the proposed intelligent controller, presenting a much improved performance when compared with a conventional nonlinear control scheme.

Memristor‐Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems

Internet‐of‐things (IoT) edge devices with a memristive neuromorphic system can more effectively enhance daily lives. However, cyberattacks remain critical concerns for smart IoT edge devices that process a vast body of information via networks. Herein, a highly secure neuromorphic system is reported, which can be implemented using a physically unclonable function (PUF) that exploits the high entropy achieved via the stochastic switching of a poly(1,3,5‐trivinyl‐1,3,5‐trimethyl cyclotrisiloxane) (pV3D3)‐based memristor. The excellent insulating property of pV3D3 enhances the stochasticity of the tunneling distance for randomly ruptured Cu filaments. The pV3D3 memristor‐based PUF (pV3D3‐PUF) achieves near‐ideal 50% averages for uniformity and uniqueness, excellent reliability under conditions of mechanical stress and water immersion, and reconfigurability‐bolstering security without additional hardware. Using stochastic in‐memory computing, the pV3D3‐PUF shows resilience to machine learning attacks. Furthermore, a cryptography protocol is demonstrated, which enables artificial intelligence service implementation without security issues for PUF‐integrated pV3D3 memristor‐based neuromorphic systems.

Electromagnetic Interference Effects of Continuous Waves on Memristors: A Simulation Study.

As two-terminal passive fundamental circuit elements with memory characteristics, memristors are promising devices for applications such as neuromorphic systems, in-memory computing, and tunable RF/microwave circuits. The increasingly complex electromagnetic interference (EMI) environment threatens the reliability of memristor systems. However, various EMI signals’ effects on memristors are still unclear. This paper selects continuous waves (CWs) as EMI signals. It provides a deeper insight into the interference effect of CWs on the memristor driven by a sinusoidal excitation voltage, as well as a method for investigating the EMI effect of memristors. The optimal memristor model is obtained by the exhaustive traversing of the possible model parameters, and the interference effect of CWs on memristors is quantified based on this model and the proposed evaluation metrics. Simulation results indicate that CW interference may affect the switching time, dynamic range, nonlinearity, symmetry, time to the boundary, and variation of memristance. The specific interference effect depends on the operating mode of the memristor, the amplitude, and the frequency of the CW. This research provides a foundation for evaluating EMI effects and designing electromagnetic protection for memristive neuromorphic systems.

Optically modulated dual‐mode memristor arrays based on core‐shell CsPbBr3@graphdiyne nanocrystals for fully memristive neuromorphic computing hardware

AbstractArtificial synapses and neurons are crucial milestones for neuromorphic computing hardware, and memristors with resistive and threshold switching characteristics are regarded as the most promising candidates for the construction of hardware neural networks. However, most of the memristors can only operate in one mode, that is, resistive switching or threshold switching, and distinct memristors are required to construct fully memristive neuromorphic computing hardware, making it more complex for the fabrication and integration of the hardware. Herein, we propose a flexible dual‐mode memristor array based on core–shell CsPbBr3@graphdiyne nanocrystals, which features a 100% transition yield, small cycle‐to‐cycle and device‐to‐device variability, excellent flexibility, and environmental stability. Based on this dual‐mode memristor, homo‐material‐based fully memristive neuromorphic computing hardware—a power‐free artificial nociceptive signal processing system and a spiking neural network—are constructed for the first time. Our dual‐mode memristors greatly simplify the fabrication and integration of fully memristive neuromorphic systems.

Lightweight Memristive Neural Network for Gas Classification Based on Heterogeneous Strategy

The memristive neuromorphic computing system (MNCS) can complete related calculations with lower power consumption and higher speed, which has attracted widespread attention. However, due to the limitations of memristor and circuit, the realization of MNCS faces many challenges. In this paper, we propose a heterogeneous deployment strategy for the MNCS and construct a lightweight heterogeneous memristive gas classification neural network (LHM-GSNN) based on the electronic nose (e-nose) application. In addition, the model parameters are quantified by clustering strategy to adapt to the nonideal characteristics of memristor. The experimental results show that the complex structure in the model is visibly simplified, and the number of parameters is correspondingly reduced using the heterogeneous deployment strategy. Furthermore, we also analyze the power consumption of the LHM-GSNN model deployed to the MNCS. This work may provide new solutions for constructing and implementing the MNCS.

Nanoscale-Resistive Switching in Forming-Free Zinc Oxide Memristive Structures.

This article presents the results of experimental studies of the impact of electrode material and the effect of nanoscale film thickness on the resistive switching in forming-free nanocrystalline ZnO films grown by pulsed laser deposition. It was demonstrated that the nanocrystalline ZnO film with TiN, Pt, ZnO:In, and ZnO:Pd bottom electrodes exhibits a nonlinear bipolar effect of forming-free resistive switching. The sample with Pt showed the highest resistance values RHRS and RLRS and the highest value of Uset = 2.7 ± 0.4 V. The samples with the ZnO:In and ZnO:Pd bottom electrode showed the lowest Uset and Ures values. An increase in the number of laser pulses from 1000 to 5000 was shown to lead to an increase in the thickness of the nanocrystalline ZnO film from 7.2 ± 2.5 nm to 53.6 ± 18.3 nm. The dependence of electrophysical parameters (electron concentration, electron mobility, and resistivity) on the thickness of the forming-free nanocrystalline ZnO film for the TiN/ZnO/W structure was investigated. The endurance test and homogeneity test for TiN/ZnO/W structures were performed. The structure Al2O3/TiN/ZnO/W with a nanocrystalline ZnO thickness 41.2 ± 9.7 nm was shown to be preferable for the manufacture of ReRAM and memristive neuromorphic systems due to the highest value of RHRS/RLRS = 2307.8 ± 166.4 and low values of Uset = 1.9 ± 0.2 V and Ures = −1.3 ± 0.5 V. It was demonstrated that the use of the TiN top electrode in the Al2O3/TiN/ZnO memristor structure allowed for the reduction in Uset and Ures and the increase in the RHRS/RLRS ratio. The results obtained can be used in the manufacturing of resistive-switching nanoscale devices for neuromorphic computing based on the forming-free nanocrystalline ZnO oxide films.

Methodology for Hardware-in-the-Loop Simulation of Memristive Neuromorphic Systems

Methodology for Hardware-in-the-Loop Simulation of Memristive Neuromorphic Systems

Unsupervised Learning Implemented by Ti3C2-MXene-Based Memristive Neuromorphic System

The neuromorphic hardware system has been a promising candidate for future computing architectures, as it enables adaptive learning at low energy and area consumption. However, hardware implementat...

Device-aware Circuit Design for Robust Memristive Neuromorphic Systems with STDP-based Learning

In the past decade, complementary metal oxide semiconductor-memristor hybrid neuromorphic systems have gained importance owing to the advantages of memristors such as nano-scale size, non-volatility, and low-power operation. However, they are often accompanied by non-ideal properties that can impact the system’s performance. This article presents device-aware circuit design to mitigate such effects. A bi-memristor synapse with a robust spike-timing-dependent plasticity (STDP) is designed. A mixed-mode neuron is presented whose accumulation rate is tunable on-chip and can be used with a variety of memristors without needing a re-design. The proposed designs are employed together in an example pattern recognition system. A scalable winner-takes-all circuit is presented for the output stage. A pattern recognition task based on a simple STDP-based learning is demonstrated such that the recognition rate is directly dependent on the learnt weights. Device-level issues such as switching speed/threshold asymmetry, limited switching resolution, endurance, and varying resistance range (across devices) are shown to adversely affect learning at the system level and it is demonstrated that the proposed circuits can mitigate them. Last, the area and energy costs of the proposed designs are evaluated and compared against other implementations in the literature.

An Associative-Memory-Based Reconfigurable Memristive Neuromorphic System With Synchronous Weight Training

Memristive neuromorphic systems are emerging potential hardware platforms to implement artificial neural networks. Combining features of memristive neuromorphic systems with associative memory, this article proposes an associative-memory-based reconfigurable memristive neuromorphic system. In the proposed system, there are two neural networks: 1) the neural network for computing acceleration and 2) the neural network mimicking associative memory. Then, a case study of the system is presented, which includes an associative memory network to realize apple recognition and a computing acceleration network for iris classification. The associative memory network depends on associative learning to achieve the recognition function. In addition, during the corresponding forgetting process, the connections of the related synaptic circuits are cut off and sent to a synaptic circuit block, realizing variable circuit topology. Further, the synaptic circuits in the block are applied to construct the iris classification network, accomplishing the reconfiguration of the proposed system. The circuit structure of this classification network matches backpropagation (BP) algorithm well. Meanwhile, the network reaches a relatively high classification accuracy after training. In an iteration of the training, all the synaptic circuits that need to change can adjust weights synchronously, which improves training speed.

Electroforming in Metal-Oxide Memristive Synapses.

Memristors have shown an extraordinary potential to emulate the plastic and dynamic electrical behaviors of biological synapses and have been already used to construct neuromorphic systems with in-memory computing and unsupervised learning capabilities; moreover, the small size and simple fabrication process of memristors make them ideal candidates for ultradense configurations. So far, the properties of memristive electronic synapses (i.e., potentiation/depression, relaxation, linearity) have been extensively analyzed by several groups. However, the dynamics of electroforming in memristive devices, which defines the position, size, shape, and chemical composition of the conductive nanofilaments across the device, has not been analyzed in depth. By applying ramped voltage stress (RVS), constant voltage stress (CVS), and pulsed voltage stress (PVS), we found that electroforming is highly affected by the biasing methods applied. We also found that the technique used to deposit the oxide, the chemical composition of the adjacent metal electrodes, and the polarity of the electrical stimuli applied have important effects on the dynamics of the electroforming process and in subsequent post-electroforming bipolar resistive switching. This work should be of interest to designers of memristive neuromorphic systems and could open the door for the implementation of new bioinspired functionalities into memristive neuromorphic systems.

Artificial Perception Built on Memristive System: Visual, Auditory, and Tactile Sensations

The widespread implementation and rapid development of autonomous systems pose stringent performance requirements on emerging sensory systems. In addition to the basic sensing requirements, leading sensory systems are required to process data and extract featured information from highly redundant data in real time. With the added edge‐computational capabilities, data shuttling is avoided, leading to significant reduction of computational burden and bandwidth pressure in the cloud. Among the different computing architectures, the neuromorphic sensory system stands out due to its high power efficiency, low latency, and excellent processing capability. Mimicking the biological neural network, the colocation of sensory, processor, and memory components of neuromorphic sensory systems enables the requirements for frequent data shuttles to be circumvented. In particular, artificial intelligent perceptions built on memristive neuromorphic systems exhibit outstanding characteristics of small footprint, low power consumption, 3D stacking ability, and high density. Herein, the two essential parts of the memristive artificial perceptron system are presented: 1) memristive systems for neuromorphic computing and 2) high‐performance sensors. Next, the current state of the art established on artificial perceptron systems covering visual, auditory, and tactile sensations is highlighted. To conclude, the current challenges and future direction in the area of advanced intelligent perceptions are presented.

Truly Concomitant and Independently Expressed Short- and Long-Term Plasticity in a Bi2 O2 Se-Based Three-Terminal Memristor.

Concomitance of diverse synaptic plasticity across different timescales produces complex cognitive processes. To achieve comparable cognitive complexity in memristive neuromorphic systems, devices that are capable of emulating short-term (STP) and long-term plasticity (LTP) concomitantly are essential. In existing memristors, however, STP and LTP can only be induced selectively because of the inability to be decoupled using different loci and mechanisms. In this work, the first demonstration of truly concomitant STP and LTP is reported in a three-terminal memristor that uses independent physical phenomena to represent each form of plasticity. The emerging layered material Bi2 O2 Se is used for memristors for the first time, opening up the prospects for ultrathin, high-speed, and low-power neuromorphic devices. The concerted action of STP and LTP allows full-range modulation of the transient synaptic efficacy, from depression to facilitation, by stimulus frequency or intensity, providing a versatile device platform for neuromorphic function implementation. A heuristic recurrent neural circuitry model is developed to simulate the intricate "sleep-wake cycle autoregulation" process, in which the concomitance of STP and LTP is posited as a key factor in enabling this neural homeostasis. This work sheds new light on the development of generic memristor platforms for highly dynamic neuromorphic computing.

Technology Aware Training in Memristive Neuromorphic Systems for Nonideal Synaptic Crossbars

The advances in the field of machine learning using neuromorphic systems have paved the pathway for extensive research on possibilities of hardware implementations of neural networks. Various memristive technologies such as oxide-based devices, spintronics and phase change materials have been explored to implement the core functional units of neuromorphic systems, namely the synaptic network, and the neuronal functionality, in a fast and energy efficient manner. However, various non-idealities in the crossbar implementations of the synaptic arrays can significantly degrade performance of neural networks and hence, impose restrictions on feasible crossbar sizes. In this work, we build mathematical models of various non-idealities that occur in crossbar implementations such as source resistance, neuron resistance and chip-to-chip device variations and analyze their impact on the classification accuracy of a fully connected network (FCN) and convolutional neural network (CNN) trained with standard training algorithm. We show that a network trained under ideal conditions can suffer accuracy degradation as large as 59.84% for FCNs and 62.4% for CNNs when implemented on non-ideal crossbars for relevant non-ideality ranges. This severely constrains the sizes for crossbars. As a solution, we propose a technology aware training algorithm which incorporates the mathematical models of the non-idealities in the standard training algorithm. We demonstrate that our proposed methodology achieves significant recovery of testing accuracy within 1.9% of the ideal accuracy for FCNs and 1.5% for CNNs. We further show that our proposed training algorithm can potentially allow the use of significantly larger crossbar arrays of sizes 784$\times$500 for FCNs and 4096$\times$512 for CNNs with a minor or no trade-off in accuracy

Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons

Biologically plausible neuromorphic computing systems are attracting considerable attention due to their low latency, massively parallel information processing abilities, and their high energy efficiency. To achieve these features neuromorphic silicon neuron circuits need to be integrated with plastic synapse circuits capable of on-line learning and storage of synaptic weights. Within this context, memristive devices play a key role thanks to their non-volatility, scalability, and compatibility with the complementary metal–oxide–semiconductor fabrication process. However, neuro-memristive systems are still facing difficult challenges for implementing efficient learning protocols. Here, we propose and demonstrate in hardware a spike-driven threshold-based learning rule which goes beyond conventional spike-timing dependent plasticity mechanisms, by also taking into account the neuron membrane potential and its firing rate. The mixed memristive–neuromorphic system we demonstrate comprises an oxide-based memristive synapse device placed between two silicon neurons implemented on a neuromorphic chip that comprises the proper interfacing and spike-based learning circuits designed to drive the memristive elements. We show how the system is able to emulate in real-time weight dependent post-synaptic activity and drive synaptic weight updates at the memristive synapse level following the spike-driven learning rule presented. We validate this spike-based learning mechanism with experimental results and quantify the system performance with basic learning experiments.

Memristive Mixed-Signal Neuromorphic Systems: Energy-Efficient Learning at the Circuit-Level

Neuromorphic computing is non-von Neumann computer architecture for the post Moore’s law era of computing. Since a main focus of the post Moore’s law era is energy-efficient computing with fewer resources and less area, neuromorphic computing contributes effectively in this research. In this paper, we present a memristive neuromorphic system for improved power and area efficiency. Our particular mixed-signal approach implements neural networks with spiking events in a synchronous way. Moreover, the use of nano-scale memristive devices saves both area and power in the system. We also provide device-level considerations that make the system more energy-efficient. The proposed system additionally includes synchronous digital long term plasticity, an online learning methodology that helps the system train the neural networks during the operation phase and improves the efficiency in learning considering the power consumption and area overhead.