NoC simulation steered by NEST: McAERsim and a Noxim patch.

Introduction: Great knowledge was gained about the computational substrate of the brain, but the way in which components and entities interact to perform information processing still remains a secret. Complex and large-scale network models have been developed to unveil processes at the ensemble level taking place over a large range of timescales. They challenge any kind of simulation platform, so that efficient implementations need to be developed that gain from focusing on a set of relevant models. With increasing network sizes imposed by these models, low latency inter-node communication becomes a critical aspect. This situation is even accentuated, if slow processes like learning should be covered, that require faster than real-time simulation. Methods: Therefore, this article presents two simulation frameworks, in which network-on-chip simulators are interfaced with the neuroscientific development environment NEST. This combination yields network traffic that is directly defined by the relevant neural network models and used to steer the network-on-chip simulations. As one of the outcomes, instructive statistics on network latencies are obtained. Since time stamps of different granularity are used by the simulators, a conversion is required that can be exploited to emulate an intended acceleration factor. Results: By application of the frameworks to scaled versions of the cortical microcircuit model-selected because of its unique properties as well as challenging demands-performance curves, latency, and traffic distributions could be determined. Discussion: The distinct characteristic of the second framework is its tree-based source-address driven multicast support, which, in connection with the torus topology, always led to the best results. Although currently biased by some inherent assumptions of the network-on-chip simulators, the results suit well to those of previous work dealing with node internals and suggesting accelerated simulations to be in reach.

Memristors with Tunable Volatility for Reconfigurable Neuromorphic Computing.

Neuromorphic computing promises an energy-efficient alternative to traditional digital processors in handling data-heavy tasks, primarily driven by the development of both volatile (neuronal) and nonvolatile (synaptic) resistive switches or memristors. However, despite their energy efficiency, memristor-based technologies presently lack functional tunability, thus limiting their competitiveness with arbitrarily programmable (general purpose) digital computers. This work introduces a two-terminal bilayer memristor, which can be tuned among neuronal, synaptic, and hybrid behaviors. The varying behaviors are accessed via facile control over the filament formed within the memristor, enabled by the interplay between the two active ionic species (oxygen vacancies and metal cations). This solution is unlike single-species ion migration employed in most other memristors, which makes their behavior difficult to control. By reconfiguring a single crossbar array of hybrid memristors, two different applications that usually require distinct types of devices are demonstrated - reprogrammable heterogeneous reservoir computing and arbitrary non-Euclidean graph networks. Thus, this work outlines a potential path toward functionally reconfigurable postdigital computers.

Eco-Friendly Biomemristive Nonvolatile Memory: Harnessing Organic Waste for Sustainable Technology.

Organic material-based bioelectronic nonvolatile memory devices have recently received a lot of attention due to their environmental compatibility, simple fabrication recipe, preferred scalability, low cost, low power consumption, and numerous additional advantages. Resistive random-access memory (RRAM) devices work on the principle of resistive switching, which has the potential for applications in memory storage and neuromorphic computing. Here, natural organically grown orange peel was used to extract biocompatible pectin to design a resistive switching-based memory device of the structure Ag/Pectin/Indium tin oxide (ITO), and the behavior was studied between a temperature range of 10K and 300K. The microscopic characterization revealed the texture of the surface and thickness of the layers. The memristive current-voltage characteristics performed over 1000 consecutive cycles of repeated switching revealed sustainable bipolar resistive switching behavior with a high ON/OFF ratio. The underlying principle of Resistive Switching behavior is based on the formation of conductive filaments between the electrodes, which is explained in this work. Further, we have also designed a 2 × 2 crossbar array of RRAM devices to demonstrate various logic circuit operations useful for neuromorphic computing. The robust switching characteristics suggest possible uses of such devices for the design of ecofriendly bioelectronic memory applications and in-memory computing.

2D Reconfigurable Memory Device Enabled by Defect Engineering for Multifunctional Neuromorphic Computing.

In this era of artificial intelligence and Internet of Things, emerging new computing paradigms such as in-sensor and in-memory computing call for both structurally simple and multifunctional memory devices. Although emerging two-dimensional (2D) memory devices provide promising solutions, the most reported devices either suffer from single functionalities or structural complexity. Here, this work reports a reconfigurable memory device (RMD) based on MoS2/CuInP2S6 heterostructure, which integrates the defect engineering-enabled interlayer defects and the ferroelectric polarization in CuInP2S6, to realize a simplified structure device for all-in-one sensing, memory and computing. The plasma treatment-induced defect engineering of the CuInP2S6 nanosheet effectively increases the interlayer defect density, which significantly enhances the charge-trapping ability in synergy with ferroelectric properties. The reported device not only can serve as a non-volatile electronic memory device, but also can be reconfigured into optoelectronic memory mode or synaptic mode after controlling the ferroelectric polarization states in CuInP2S6. When operated in optoelectronic memory mode, the all-in-one RMD could diagnose ophthalmic disease by segmenting vasculature within biological retinas. On the other hand, operating as an optoelectronic synapse, this work showcases in-sensor reservoir computing for gesture recognition with high energy efficiency.

Reservoir computing with a random memristor crossbar array.

Physical implementations of reservoir computing (RC) based on the emerging memristors have become promising candidates of unconventional computing paradigms. Traditionally, sequential approaches by time-multiplexing volatile memristors have been prevalent because of their low hardware overhead. However, they suffer from the problem of speed degradation and fall short of capturing the spatial relationship between the time-domain inputs. Here, we explore a new avenue for RC using memristor crossbar arrays (MCAs) with device-to-device variations, which serve as physical random weight matrices of the reservoir layers, enabling faster computation thanks to the parallelism of matrix-vector multiplication as an intensive operation in RC. To achieve this new RC architecture, ultralow-current, self-selective memristors are fabricated and integrated without the need of transistors, showing greater potential of high scalability and three-dimensional integrability compared to the previous realizations. The information processing ability of our RC system is demonstrated in tasks of recognizing digit images and waveforms. This work indicates that the "nonidealities" of the emerging memristor devices and circuits are a useful source of inspiration for new computing paradigms.

Rational Design of Fluorinated 2D Polymer Film Based on Donor-Accepter Architecture toward Multilevel Memory Device for Neuromorphic Computing.

Fluorine-containing 2D polymer (F-2DP) film is a desired system to regulate the charge transport in organic electronics but rather rarely reports due to the limited fluorine-containing building blocks and difficulties in synthesis. Herein, a novel polar molecule with antiparallel columnar stacking is synthesized and further embedded into an F-2DP system to control over the crystallinity of F-2DP film through self-complementary π-electronic forces. The donor-accepter-accepter'-donor' (D-A-A'-D') structure regulates the charge transportation efficiently, inducing multilevel memory behavior through stepwise charge capture and transfer processes. Thus, the device exhibits ternary memory behavior with low threshold voltage (Vth1 of 1.1 V, Vth2 of 2.0 V), clearly distinguishable resistance states (1:102:104) and ternary yield (83%). Furthermore, the stepwise formation of the charge complex endows the device with a wider range to regulate the conductive state, which allows its application in brain-inspired neuromorphic computing. Modified National Institute of Standards and Technology recognition can reach an accuracy of 86%, showing great potential in neuromorphic computing applications in the post-Moore era.

Highly Efficient Back-End-of-Line Compatible Flexible Si-Based Optical Memristive Crossbar Array for Edge Neuromorphic Physiological Signal Processing and Bionic Machine Vision.

The emergence of the Internet-of-Things is anticipated to create a vast market for what are known as smart edge devices, opening numerous opportunities across countless domains, including personalized healthcare and advanced robotics. Leveraging 3D integration, edge devices can achieve unprecedented miniaturization while simultaneously boosting processing power and minimizing energy consumption. Here, we demonstrate a back-end-of-line compatible optoelectronic synapse with a transfer learning method on health care applications, including electroencephalogram (EEG)-based seizure prediction, electromyography (EMG)-based gesture recognition, and electrocardiogram (ECG)-based arrhythmia detection. With experiments on three biomedical datasets, we observe the classification accuracy improvement for the pretrained model with 2.93% on EEG, 4.90% on ECG, and 7.92% on EMG, respectively. The optical programming property of the device enables an ultra-low power (2.8 × 10-13 J) fine-tuning process and offers solutions for patient-specific issues in edge computing scenarios. Moreover, the device exhibits impressive light-sensitive characteristics that enable a range of light-triggered synaptic functions, making it promising for neuromorphic vision application. To display the benefits of these intricate synaptic properties, a 5 × 5 optoelectronic synapse array is developed, effectively simulating human visual perception and memory functions. The proposed flexible optoelectronic synapse holds immense potential for advancing the fields of neuromorphic physiological signal processing and artificial visual systems in wearable applications.

Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities.

In the dynamic landscape of Artificial Intelligence (AI), two notable phenomena are becoming predominant: the exponential growth of large AI model sizes and the explosion of massive amount of data. Meanwhile, scientific research such as quantum computing and protein synthesis increasingly demand higher computing capacities. As the Moore's Law approaches its terminus, there is an urgent need for alternative computing paradigms that satisfy this growing computing demand and break through the barrier of the von Neumann model. Neuromorphic computing, inspired by the mechanism and functionality of human brains, uses physical artificial neurons to do computations and is drawing widespread attention. This review studies the expansion of optoelectronic devices on photonic integration platforms that has led to significant growth in photonic computing, where photonic integrated circuits (PICs) have enabled ultrafast artificial neural networks (ANN) with sub-nanosecond latencies, low heat dissipation, and high parallelism. In particular, various technologies and devices employed in neuromorphic photonic AI accelerators, spanning from traditional optics to PCSEL lasers are examined. Lastly, it is recognized that existing neuromorphic technologies encounter obstacles in meeting the peta-level computing speed and energy efficiency threshold, and potential approaches in new devices, fabrication, materials, and integration to drive innovation are also explored. As the current challenges and barriers in cost, scalability, footprint, and computing capacity are resolved one-by-one, photonic neuromorphic systems are bound to co-exist with, if not replace, conventional electronic computers and transform the landscape of AI and scientific computing in the foreseeable future.

Machine learning without a processor: Emergent learning in a nonlinear analog network.

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN-an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Two-dimensional material-based memristive devices for alternative computing.

Two-dimensional (2D) materials have emerged as promising building blocks for next generation memristive devices, owing to their unique electronic, mechanical, and thermal properties, resulting in effective switching mechanisms for charge transport. Memristors are key components in a wide range of applications including neuromorphic computing, which is becoming increasingly important in artificial intelligence applications. Crossbar arrays are an important component in the development of hardware-based neural networks composed of 2D materials. In this paper, we summarize the current state of research on 2D material-based memristive devices utilizing different switching mechanisms, along with the application of these devices in neuromorphic crossbar arrays. Additionally, we discuss the challenges and future directions for the field.

Neuromorphic Computing with Emerging Antiferromagnetic Ordering in Spin-Orbit Torque Devices.

Field-free switching (FFS) and spin-orbit torque (SOT)-based neuromorphic characteristics were realized in a W/Pt/Co/NiO/Pt heterostructure with a perpendicular exchange bias (HEB) for brain-inspired neuromorphic computing (NC). Experimental results using NiO-based SOT devices guided the development of fully spin-based artificial synapses and sigmoidal neurons for implementation in a three-layer artificial neural network. This system achieved impressive accuracies of 91-96% when applied to the Modified National Institute of Standards and Technology (MNIST) image data set and 78.85-81.25% when applied to Fashion MNIST images, due presumably to the emergence of robust NiO antiferromagnetic (AFM) ordering. The emergence of AFM ordering favored the FFS with an enhanced HEB, which suppressed the memristivity and reduced the recognition accuracy. This indicates a trade-off between the requirements for solid-state memory and those required for brain-inspired NC devices. Nonetheless, our findings revealed opportunities by which the two technologies could be aligned via controllable exchange coupling.

Development of a novel self-healing Zn(II)-metallohydrogel with wide bandgap semiconducting properties for non-volatile memory device application.

A rapid and effective strategy has been devised for the swift development of a Zn(II)-ion-based supramolecular metallohydrogel, termed Zn@PEH, using pentaethylenehexamine as a low molecular weight gelator. This process occurs in an aqueous medium at room temperature and atmospheric pressure. The mechanical strength of the synthesized Zn@PEH metallohydrogel has been assessed through rheological analysis, considering angular frequency and oscillator stress dependencies. Notably, the Zn@PEH metallohydrogel exhibits exceptional self-healing abilities and can bear substantial loads, which have been characterized through thixotropic analysis. Additionally, this metallohydrogel displays injectable properties. The structural arrangement resembling pebbles within the hierarchical network of the supramolecular Zn@PEH metallohydrogel has been explored using FESEM and TEM measurements. EDX elemental mapping has confirmed the primary chemical constituents of the metallohydrogel. The formation mechanism of the metallohydrogel has been analyzed via FT-IR spectroscopy. Furthermore, zinc(II) metallohydrogel (Zn@PEH)-based Schottky diode structure has been fabricated in a lateral metal-semiconductor-metal configuration and  it's charge transport behavior has also been studied. Notably, the zinc(II) metallohydrogel-based resistive random access memory (RRAM) device (Zn@PEH) demonstrates bipolar resistive switching behavior at room temperature. This RRAM device showcases remarkable switching endurance over 1000 consecutive cycles and a high ON/OFF ratio of approximately 270. Further, 2 × 2 crossbar array of the RRAM devices were designed to demonstrate OR and NOT logic circuit operations, which can be extended for performing higher order computing operations. These structures hold promise for applications in non-volatile memory design, neuromorphic and in-memory computing, flexible electronics, and optoelectronic devices due to their straightforward fabrication process, robust resistive switching behavior, and overall system stability.

GeTe/MoTe2 Van der Waals Heterostructures: Enabling Ultralow Voltage Memristors for Nonvolatile Memory and Neuromorphic Computing Applications.

Advanced electronic semiconducting Van der Waals heterostructures (HSs) are promising candidates for exploring next-generation nanoelectronics owing to their exceptional electronic properties, which present the possibility of extending their functionalities to diverse potential applications. In this study, GeTe/MoTe2 HS are explored for nonvolatile memory and neuromorphic-computing applications. Sputter-deposited Ag/GeTe/MoTe2/Pt HS cross-point devices are fabricated, and they demonstrate memristor behavior at ultralow switching voltages (VSET: 0.15 V and VRESET: -0.14 V) with very low energy consumption (≈30 nJ), high memory window, long retention time (104 s), and excellent endurance (105 cycles). Resistive switching is achieved by adjusting the interface between the Ag top electrode and the heterojunction switching layer. Cross-sectional transmission electron microscope images and conductive atomic force microscopy analysis confirm the presence of a conducting filament in the heterojunction switching layer. Further, emulating various synaptic functions of a biological synapse reveals that GeTe/MoTe2 HS can be utilized for energy-efficient neuromorphic-computing applications. A multilayer perceptron is implemented using the synaptic weights of the Ag/GeTe/MoTe2/Pt HS device, revealing high pattern accuracy (81.3%). These results indicate that HS devices can be considered a potential solution for high-density memory and artificial intelligence applications.

New-Style Logic Operation and Neuromorphic Computing Enabled by Optoelectronic Artificial Synapses in an MXene/Y:HfO2 Ferroelectric Memristor.

Today's computing systems, to meet the enormous demands of information processing, have driven the development of brain-inspired neuromorphic systems. However, there are relatively few optoelectronic devices in most brain-inspired neuromorphic systems that can simultaneously regulate the conductivity through both optical and electrical signals. In this work, the Au/MXene/Y:HfO2/FTO ferroelectric memristor as an optoelectronic artificial synaptic device exhibited both digital and analog resistance switching (RS) behaviors under different voltages with a good switching ratio (>103). Under optoelectronic conditions, optimal weight update parameters and an enhanced algorithm achieved 97.1% recognition accuracy in convolutional neural networks. A new logic gate circuit specifically designed for optoelectronic inputs was established. Furthermore, the device integrates the impact of relative humidity to develop an innovative three-person voting mechanism with a veto power. These results provide a feasible approach for integrating optoelectronic artificial synapses with logic-based computing devices.

On Transducing Properties of Kombucha-Proteinoid Complexes.

We investigate the information processing capacities of kombucha-proteinoid proto-brains, focusing on the transducing properties through accommodation spiking, tonic bursting spiking, and optical and acoustic stimulation. We explore self-organization, adaptability, and emergent phenomena in this unconventional proto-architecture. By constructing kombucha-proteinoid networks exposed to diverse audio stimuli, we analyze nonlinear dynamics using time series analysis. Assessing information representation in the presence of extreme noise, we examine the system's resilience. Our results illustrate intricate pathways resulting from the interplay between the synthetic biological substrate and bio-inspired stimulation. The kombucha-proteinoid proto-brains consistently map complex stimuli to distinct activation levels, showcasing their adaptability and potential for information processing without the need for external shaping circuits.

Optically driven intelligent computing with ZnO memristor.

Artificial vision is crucial for most artificial intelligence applications. Conventional artificial visual systems have been facing challenges in terms of real-time information processing due to the physical separation of sensors, memories, and processors, which results in the production of a large amount of redundant data as well as the data conversion and transfer between these three components consuming most of the time and energy. Emergent optoelectronic memristors with the ability to realize integrated sensing-computing-memory (ISCM) are key candidates for solving such challenges and therefore attract increasing attention. At present, the memristive ISCM devices can only perform primary-level computing with external light signals due to the fact that only monotonic increase of memconductance upon light irradiation is achieved in most of these devices. Here, we propose an all-optically controlled memristive ISCM device based on a simple structure of Au/ZnO/Pt with the ZnO thin film sputtered at pure Ar atmosphere. This device can perform advanced computing tasks such as nonvolatile neuromorphic computing and complete Boolean logic functions only by light irradiation, owing to its ability to reversibly tune the memconductance with light. Moreover, the device shows excellent operation stability ascribed to a purely electronic memconductance tuning mechanism. Hence, this study is an important step towards the next generation of artificial visual systems.

Bioinspired In-Sensor Multimodal Fusion for Enhanced Spatial and Spatiotemporal Association.

Multimodal perception can capture more precise and comprehensive information compared with unimodal approaches. However, current sensory systems typically merge multimodal signals at computing terminals following parallel processing and transmission, which results in the potential loss of spatial association information and requires time stamps to maintain temporal coherence for time-series data. Here we demonstrate bioinspired in-sensor multimodal fusion, which effectively enhances comprehensive perception and reduces the level of data transfer between sensory terminal and computation units. By adopting floating gate phototransistors with reconfigurable photoresponse plasticity, we realize the agile spatial and spatiotemporal fusion under nonvolatile and volatile photoresponse modes. To realize an optimal spatial estimation, we integrate spatial information from visual-tactile signals. For dynamic events, we capture and fuse in real time spatiotemporal information from visual-audio signals, realizing a dance-music synchronization recognition task without a time-stamping process. This in-sensor multimodal fusion approach provides the potential to simplify the multimodal integration system, extending the in-sensor computing paradigm.

A Crayfish-Inspired Sensor Fusion Platform for Super Additive Integration of Visual, Chemical, and Tactile Information.

The concept of cross-sensor modulation, wherein one sensor modality can influence another's response, is often overlooked in traditional sensor fusion architectures, leading to missed opportunities for enhancing data accuracy and robustness. In contrast, biological systems, such as aquatic animals like crayfish, demonstrate superior sensor fusion through multisensory integration. These organisms adeptly integrate visual, tactile, and chemical cues to perform tasks such as evading predators and locating prey. Drawing inspiration from this, we propose a neuromorphic platform that integrates graphene-based chemitransistors, monolayer molybdenum disulfide (MoS2) based photosensitive memtransistors, and triboelectric tactile sensors to achieve "Super-Additive" responses to weak chemical, visual, and tactile cues and demonstrate contextual response modulation, also referred to as the "Inverse Effectiveness Effect." We hold the view that integrating bio-inspired sensor fusion principles across various modalities holds promise for a wide range of applications.

Introducing Chiro-optical Activities in Photonic Synapses for Neuromorphic Computing and In-Memory Logic Operations.

In artificial synaptic devices aimed at mimicking neuromorphic computing systems, electrical or optical pulses, or both, are generally used as stimuli. In this work, we introduce chiral materials for tailoring the characteristics of photonic synaptic devices to achieve handedness-dependent neuromorphic computing and in-memory logic gates. In devices based on a pair of chiral perovskites, the use of circularly polarized light (CPL) as the optical stimuli mimicked a series of electrical and opto-synaptic functionalities in order to emulate the multifunctional complex behavior of the human brain. Upon illumination in this two-terminal device, anisotropy in current has been observed due to the out-of-plane carrier transport, originating from spin-selective carrier transport. More importantly, the logic gate achieved in devices based on optoelectronic memristors turned out to be chirality-dependent; while an R-device functioned as an AND gate, the device based on the same perovskite of the opposite chirality (S-device) acted as a NOR gate toward in-memory logic operations. These findings in chiral perovskite-based artificial synapses can identify further strategies for future neuromorphic computing, vision simulation, and artificial intelligence.

Solid polymer electrolyte-based atomic switches: from materials to mechanisms and applications.

As miniaturization of semiconductor memory devices is reaching its physical and technological limits, there is a demand for memory technologies that operate on new principles. Atomic switches are nanoionic devices that show repeatable resistive switching between high-resistance and low-resistance states under bias voltage applications, based on the transport of metal ions and redox reactions in solids. Their essential structure consists of an ion conductor sandwiched between electrochemically active and inert electrodes. This review focuses on the resistive switching mechanism of atomic switches that utilize a solid polymer electrolyte (SPE) as the ion conductor. Owing to the superior properties of polymer materials such as mechanical flexibility, compatibility with various substrates, and low fabrication costs, SPE-based atomic switches are a promising candidate for the next-generation of volatile and nonvolatile memories. Herein, we describe their operating mechanisms and key factors for controlling the device performance with different polymer matrices. In particular, the effects of moisture absorption in the polymer matrix on the resistive switching behavior are addressed in detail. As potential applications, atomic switches with inkjet-printed SPE and quantum conductance behavior are described. SPE-based atomic switches also have great potential in use for neuromorphic devices. The development of these devices will be enhanced using nanoarchitectonics concepts, which integrate functional materials and devices.

This article reviews a series of works starting with the author's 2011 paper on solid polymer electrolyte-based atomic switches, and describes the current status and future prospects for this technology.