Spiking Neuromorphic Systems Research Articles

Analog Memcapacitor by Ferroelectric Capacitor and Its Application to Spiking Neuromorphic System

Analog Memcapacitor by Ferroelectric Capacitor and Its Application to Spiking Neuromorphic System

Graphene-Based Artificial Dendrites for Bio-Inspired Learning in Spiking Neuromorphic Systems.

Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.

A tunable leaky integrate-and-fire neuron based on one neuromorphic transistor and one memristor

Artificial leaky integrate-and-fire (LIF) neurons have attracted significant attention for building brain-like computing and neuromorphic systems. However, previous artificial LIF neurons have primarily focused on implementing integrate-and-fire function, and the function of dendritic modulation has rarely been reported. In this Letter, a tunable artificial LIF neuron based on an IGZO electric-double-layer (EDL) transistor and a TaOx memristor is fabricated, and dendritic modulation is investigated. An IGZO-based EDL transistor with a modulatory terminal is used to realize dendritic nonlinear integration and filtering capability, as well as the tunable neural excitability. An Ag/TaOx/ITO threshold switching memristor mimics the all-or-nothing spiking and threshold switching of the soma. By incorporating these two components in a customized way, such artificial LIF neuron can emulate the key function of biological neuron with rich computational flexibility. Our artificial LIF neurons with rich nonlinear dynamics have great potential to perform more complex tasks in future spiking neuromorphic systems.

Combination of Organic‐Based Reservoir Computing and Spiking Neuromorphic Systems for a Robust and Efficient Pattern Classification

Nowadays, neuromorphic systems based on memristors are considered promising approaches to the hardware realization of artificial intelligence systems with efficient information processing. However, a major bottleneck in the physical implementation of these systems is the strong dependence of their performance on the unavoidable variations (cycle‐to‐cycle, c2c, or device‐to‐device, d2d) of memristive devices. Recently, reservoir computing (RC) and spiking neuromorphic systems (SNSs) are separately proposed as valuable options to partially mitigate this problem. Herein, both approaches are combined to create a fully organic system based on 1) volatile polyaniline memristive devices for the reservoir layer and 2) nonvolatile parylene memristors for the SNS readout layer. This combination provides a simpler SNS training procedure compared with the formal neural networks and results in greater robustness to device variability, while ensuring the extraction and encoding of the input critical features (performed by the polyaniline reservoir) and the analysis and classification performed by the SNS layer. Furthermore, the spatiotemporal pattern recognition of the system brings us closer to the implementation of efficient and reliable brain‐inspired computing systems built with partially unreliable analog elements.

Visual growth of nano-HOFs for low‐power memristive spiking neuromorphic system

Memristors showcasing progressive conductance modulation are now recognized as one of the most promising candidates to act like electronic synapses inside computer circuits, mimicking the behavior of neurons in the human brain. Recently, the concept of biomimic synapse devices based on organic materials has attracted enormous interest, nevertheless, largely limited by their poor electrical conductivity and charge transfer capacity. Here, 2D ribbon-structured hydrogen-bonded organic frameworks (Nano-HOFs) embedded with the transition metal nanoparticles are firstly grown under visual surveillance to serve as highly reliable memristive materials, where the localized surface plasmon resonance effect and enhanced charge-carrier transport are strongly confirmed. The HOFs@Au-based memristor presents gradient electrical conductances under continuous voltage sweep or pulse algorithms, thereby closely simulating the synaptic behaviors in biological neurons. The newly designed artificial synapses owns an ultra-low areal switching energy density (35.8 fJ µm−2) and maintains extremely stable synaptic functions even after being exposed to ambient conditions over 6 months without any encapsulation. Further brain-inspired cognitive systems are established to realize color coding and image cognition in a secure way. This work creates a precedent for developing highly viable Nano-HOFs materials to manipulate artificial synapse mimicking, prospective neuromorphic computing and intelligent cognition applications.

A Single Chip SPAD Based Vision Sensing System With Integrated Memristive Spiking Neuromorphic Processing

This paper presents a new scalable 8 × 8 single photon avalanche diode (SPAD) based vision sensor with integrated spiking neuromorphic system on a single chip. The proposed vision sensing system adopts the benefits of SPAD’s high quantum efficiency and energy efficiency of memristive spiking neuromorphic processing. The SPAD based vision sensor includes biologically inspired address event representation (AER) readout to generate asynchronous digital address events at the output reducing computation and making it suitable to process directly with integrated on-chip spiking neuromorphic system in a faster and more energy efficient way. A novel on-chip interface is designed to convert the output events of a SPAD-based event sensor into temporally coded spikes (TCS) that enable on-chip processing with integrated spiking neuromorphic system. We have tested the prototype vision sensing system for imaging characters by SPAD based vision sensor and recognizing them using the integrated memristive spiking neuromorphic system. To help with the evaluation, we have built a complete temporal pulses data set from simulating the SPAD vision sensor with AER readout in imaging characters and applied directly to integrated spiking neuromorphic system via designed novel on-chip interface. The achieved accuracy is 89.54% with a power consumption of 316 μW for the memristive neuromorphic processor. The SPAD based vision sensor exhibits array-level dynamic range of 148 dB with a power consumption of 2.8mW. The designed SPAD-based vision sensing system with an integrated spiking neuromorphic system on a single chip shows great promise for robotics, autonomous vehicles, health, and security applications.

Arrays of Nanocomposite Crossbar Memristors for the Implementation of Formal and Spiking Neuromorphic Systems

Memristors are promising devices for use as synapses in hardware-based artificial formal and spiking neural networks. Their use can increase the energy efficiency of such networks when solving various cognitive tasks. At the same time, for the implementation of hardware-based neural networks, a large number of rather densely packed structures are required, which can be achieved by creating memristive matrices in a crossbar architecture. Crossbar arrays of nanocomposite memristors on silicon substrates with protection against edge effects are developed and manufactured, their resistive switching is investigated, a formal neural network is created on their basis, which is capable of recognizing simple patterns after loading a pretrained weight map, and the possibility of precise adjustment of the conductivity of the crossbar memristors by various means applicable in the creation of more complex spiking and formal neural networks is demonstrated.

Temporal Coding of Binary Patterns for Learning of Spiking Neuromorphic Systems Based on Nanocomposite Memristors

The metal/nanocomposite/metal (M/NC/M) memristive structures based on (Co40Fe40B20)x(LiNbO3)100–x have been studied. It has been shown that such memristors may change their conductance according to the bioinspired spike-timing-dependent plasticity (STDP) rules. Spiking neural network with 4 presynaptic inputs connected by memristor-synapses with a postsynaptic threshold neuron-integrator has been created, in which the images clustering with temporal coding has been implemented using the STDP rule. Thus, the fundamental possibility of using a temporal coding method, which is more effective than population-frequency coding, has been demonstrated for self-learning of spiking neuromorphic systems with synaptic weights based on nanocomposite memristors.

Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware.

Any large-scale spiking neuromorphic system striving for complexity at the level of the human brain and beyond will need to be co-optimized for communication and computation. Such reasoning leads to the proposal for optoelectronic neuromorphic platforms that leverage the complementary properties of optics and electronics. Starting from the conjecture that future large-scale neuromorphic systems will utilize integrated photonics and fiber optics for communication in conjunction with analog electronics for computation, we consider two possible paths toward achieving this vision. The first is a semiconductor platform based on analog CMOS circuits and waveguide-integrated photodiodes. The second is a superconducting approach that utilizes Josephson junctions and waveguide-integrated superconducting single-photon detectors. We discuss available devices, assess scaling potential, and provide a list of key metrics and demonstrations for each platform. Both platforms hold potential, but their development will diverge in important respects. Semiconductor systems benefit from a robust fabrication ecosystem and can build on extensive progress made in purely electronic neuromorphic computing but will require III-V light source integration with electronics at an unprecedented scale, further advances in ultra-low capacitance photodiodes, and success from emerging memory technologies. Superconducting systems place near theoretically minimum burdens on light sources (a tremendous boon to one of the most speculative aspects of either platform) and provide new opportunities for integrated, high-endurance synaptic memory. However, superconducting optoelectronic systems will also contend with interfacing low-voltage electronic circuits to semiconductor light sources, the serial biasing of superconducting devices on an unprecedented scale, a less mature fabrication ecosystem, and cryogenic infrastructure.

Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. The implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.

Toward Robust Cognitive 3D Brain-Inspired Cross-Paradigm System.

Spiking Neuromorphic systems have been introduced as promising platforms for energy-efficient spiking neural network (SNNs) execution. SNNs incorporate neuronal and synaptic states in addition to the variant time scale into their computational model. Since each neuron in these networks is connected to many others, high bandwidth is required. Moreover, since the spike times are used to encode information in SNN, a precise communication latency is also needed, although SNN is tolerant to the spike delay variation in some limits when it is seen as a whole. The two-dimensional packet-switched network-on-chip was proposed as a solution to provide a scalable interconnect fabric in large-scale spike-based neural networks. The 3D-ICs have also attracted a lot of attention as a potential solution to resolve the interconnect bottleneck. Combining these two emerging technologies provides a new horizon for IC design to satisfy the high requirements of low power and small footprint in emerging AI applications. Moreover, although fault-tolerance is a natural feature of biological systems, integrating many computation and memory units into neuromorphic chips confronts the reliability issue, where a defective part can affect the overall system's performance. This paper presents the design and simulation of R-NASH-a reliable three-dimensional digital neuromorphic system geared explicitly toward the 3D-ICs biological brain's three-dimensional structure, where information in the network is represented by sparse patterns of spike timing and learning is based on the local spike-timing-dependent-plasticity rule. Our platform enables high integration density and small spike delay of spiking networks and features a scalable design. R-NASH is a design based on the Through-Silicon-Via technology, facilitating spiking neural network implementation on clustered neurons based on Network-on-Chip. We provide a memory interface with the host CPU, allowing for online training and inference of spiking neural networks. Moreover, R-NASH supports fault recovery with graceful performance degradation.

Low-Power Artificial Neurons Based on Ag/TiN/HfAlOx/Pt Threshold Switching Memristor for Neuromorphic Computing

Threshold switching (TS) devices are promising candidates to build highly compact and energy efficient artificial neurons. Here, we present a Pt/Ag/TiN/HfAlOx/Pt (PATHP) device with excellent TS characteristics, including a large selectivity(1010), a wide range of operation current from 10 nA to 1 mA, an extremely steep slope (0.63 mV/dec) and fast turn-on speed (50 ns). The stable TS performance can be ascribed to the introduction of TiN buffer layer and the alternate atomic layer deposited HfAlOx layer. Further, we experimentally demonstrate the functions of leaky-integrate-and-fire neurons with low power feature based on a RC circuit and a single device, respectively, which are essential for constructing spiking neuromorphic systems.

Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights

Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than traditional hardware with the Von-Neumann architecture. However, the memristive weight adjustment in the formal neuromorphic networks by the standard back-propagation techniques suffers from poor device-to-device reproducibility. One of the most promising approaches to overcome this problem is to use local learning rules for spiking neuromorphic architectures which potentially could be adaptive to the variability issue mentioned above. Different kinds of local rules for learning spiking systems are mostly realized on a bio-inspired spike-time-dependent plasticity (STDP) mechanism, which is an improved type of classical Hebbian learning. Whereas the STDP-like mechanism has already been shown to emerge naturally in memristive devices, the demonstration of its self-adaptive learning property, potentially overcoming the variability problem, is more challenging and has yet to be reported. Here we experimentally demonstrate an STDP-based learning protocol that ensures self-adaptation of the memristor resistive states, after only a very few spikes, and makes the plasticity sensitive only to the input signal configuration, but neither to the initial state of the devices nor their device-to-device variability. Then, it is shown that the self-adaptive learning of a spiking neuron with memristive weights on rate-coded patterns could also be realized with hardware-based STDP rules. The experiments have been carried out with nanocomposite-based (Co40Fe40B20)х(LiNbO3−y)100−х memristive structures, but their results are believed to be applicable to a wide range of memristive devices. All the experimental data were supported and extended by numerical simulations. There is a hope that the obtained results pave the way for building up reliable spiking neuromorphic systems composed of partially unreliable analog memristive elements, with a more complex architecture and the capability of unsupervised learning.

Dopamine-like STDP modulation in nanocomposite memristors

The development of memristor-based spiking neuromorphic systems (NS) has been essentially driven by the hope to replicate the extremely high energy efficiency of biological systems. Spike-timing-dependent plasticity (STDP) mechanism is considered as one of the most promising learning rules for NS. STDP learning has been observed in different types of biological synapses in presence of neuromodulators, e.g. dopamine, and is believed to be an enabling phenomenon for important biological functions such as associative and reinforcement learning. However, the direct STDP window change under dopamine-like modulation has not been yet demonstrated in memristive synapses. In this study, we experimentally demonstrate a simple way for the STDP window shape modulation by introducing the coefficients controlling the neuron spike amplitudes. In such a way the STDP window shape could be modulated from a classical asymmetric shape to a bell-shaped, as well as to anti-STDP and to anti-bell-shaped. The experiments have been carried out with (Co0.4Fe0.4B0.2)x(LiNbO3)1−x nanocomposite-based memristors. Memristive characteristics of the nanocomposite structures with different metal content are also comprehensively studied. Obtained results give every hope for bio-inspired operation of the future large memristor-based NS with reinforcement learning ability.