Stochastic neuron based on IGZO Schottky diodes for neuromorphic computing

Dang, Bingjie; Liu, Keqin; Zhu, Jian‐Gang; Xu, Lei; Zhang, Teng; Cheng, Caidie; Wang, Hong; Yang, Yuchao; Hao, Yue; Huang, Ru

doi:10.1063/1.5109090

Cited by 39 publications

(25 citation statements)

References 28 publications

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“…[42][43][44][134][135][136][137][138][139][140][141][142][143] Furthermore, the stochastic neuronal population for performing complex computational tasks such as Bayesian inference may be readily implemented using the memristor as an additive source of stochasticity. [42,135,141] When the electrical input pulse (V INPUT ) is applied to the top electrode, the voltage potential across the memristor progressively accumulates until it reaches a specific threshold (red dotted line in Figure 4a), thus mimicking the IF function. Once it exceeds the threshold, the memristor based on threshold switching can suddenly release its current (I OUTPUT ) to the bottom electrode.…”

Section: Memristive Artificial Neuronsmentioning

confidence: 99%

Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

2020

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as the thermal budget has increased rapidly in recent years, which could surpass the future revenue generated worldwide by the semiconductor industry. [4,5] Consequently, electronic miniaturization limits the sustainable growth of computing technology. The von Neumann design is a conventional computing architecture, which separates the processor and memory unit. This architecture performs a computational task through sequential procedures and has functioned a mainstay of modern computing since 1945. [6] In fact, the architecture is significantly beneficial to both hardware and software developers because each component can be improved and readily extended into a united electronic system, even without a comprehensive understanding of all the components. However, the von Neumann bottleneck generates substantial power consumption and latency during the computing operation. [2,7] This limitation results from data transmission between two functional units (processor and memory), particularly when the memory is accessed through a bus with restricted bandwidth. [2,7] Moreover, according to the International Data Corporation (IDC), global data will rapidly increase to 175 ZB (1.75 × 10 23 B) by 2025. [8] Consequently, the von Neumann bottleneck will be more detrimental under tremendous workload because it may be produced by the daily operation of the design. Recently, there has been increasing demand for intellectual computers that can efficiently process substantial global data, thereby resulting in the development of a wide range of softwareor hardware-based artificial neural networks (ANNs) aimed at achieving the computing ability of the human brain. [2,9] Softwarebased ANNs have recently exhibited remarkable capabilities, such as image recognition, [10,11] natural language processing, [12,13] and performing specific tasks, [14] some beyond the human level. However, since existing ANNs are built on conventional computing architecture, that is, the von Neumann architecture, the learning parameters stored in memory are iteratively introduced to the processor to perform a task. The bottleneck issue arising from the movement of large datasets would eventually reduce the energy and time efficiency when operating the ANN software. [2,7] To alleviate this problem, several advanced software-and hardware-based ANN approaches have been suggested. [2,7,15-17] Advanced algorithms, such as network pruning, quantization, Huffman coding, and knowledge distillation, have been Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the...

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Section: Memristive Artificial Neuronsmentioning

confidence: 99%

Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

2020

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“…A large number of recent theoretical works have revealed that a single neuron can be used as a highly powerful unit of computation [16][17][18] , whose functionality far exceeds a simple point neuron model. Many of the important functionalities, such as gain modulation, have not been implemented in artificial neurons to date, including Hodgkin-Huxley neurons 19,20 , leaky integrate and fire (LIF) neurons [21][22][23] , and oscillation neurons 24,25 , etc. Beyond the exploration for more capable neuromorphic devices, there are very few reports on integration of artificial synaptic devices with artificial neurons in the same array and hardware construction of a fully memristive neural network for functional demonstrations 26,27 .…”

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confidence: 99%

Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks

et al. 2020

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As a key building block of biological cortex, neurons are powerful information processing units and can achieve highly complex nonlinear computations even in individual cells. Hardware implementation of artificial neurons with similar capability is of great significance for the construction of intelligent, neuromorphic systems. Here, we demonstrate an artificial neuron based on NbO x volatile memristor that not only realizes traditional all-or-nothing, threshold-driven spiking and spatiotemporal integration, but also enables dynamic logic including XOR function that is not linearly separable and multiplicative gain modulation among different dendritic inputs, therefore surpassing neuronal functions described by a simple point neuron model. A monolithically integrated 4 × 4 fully memristive neural network consisting of volatile NbO x memristor based neurons and nonvolatile TaO x memristor based synapses in a single crossbar array is experimentally demonstrated, showing capability in pattern recognition through online learning using a simplified δ-rule and coincidence detection, which paves the way for bio-inspired intelligent systems.

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“…In addition, when the memristive device is stochastic, a stochastic neuron can be realized based on this structure. [131] Simplified artificial neurons were also proposed: a single device emulated the LIF functions. [132] In a three-terminal structured device, there exists an intrinsic capacitor between the gate electrode and the channel, and the capacitor is used to integrate electric signals.…”

Section: Artificial Neurons Based On Memristive Devicesmentioning

confidence: 99%

Artificial Neural Networks Based on Memristive Devices: From Device to System

Huang

Wang

et al. 2020

Advanced Intelligent Systems

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Thanks to the advance in transistor scaling, computing capabilities of central processing units (CPUs) and graphics processing units (GPUs) have been increased greatly, resulting in the fast development of artificial neural networks (ANNs). ANNs predict the correlation between a set of input and output parameters by emulating the principles of biological neural networks. [1] Nowadays, ANNs have been applied to a broad spectrum, such as computer vision, natural language processing, autonomous driving, and decision making. Although ANNs are advantageous in some specific tasks, such as playing the game of Go, [2,3] the human brain outperforms ANNs in cognitive and classification tasks with fast speed and high power efficiency. The advantages of the human brain lie in the special architectures with high massive parallelism, spiking neurons, and enormous number of basic computing units of neurons (%10 11) and synapses (%10 15), whereas computers show high speed of basic operation and precision. [4] The massively parallel processing compensates the low speed of neurons and synapses, whereas spiking neurons compute more powerful than other types of neurons. [5] The parallel processing occurs based on variable synaptic weights, which is the basis of memory. Moreover, the same information is processed by many neurons and transmitted to a downstream neuron, which enhances the precision as well. Therefore, the human brain shows a salient feature, i.e., in-memory computing, suitable for high-performance applications with a large amount of data. In contrast, conventional digital computing systems face the Von Neumann bottleneck, because in the Von Neumann architecture, computing units and memory are separated, hindering the enhancement of ANNs. Thus, several novel architectures are promoted, such as brain-inspired computing (processing information based on spiking neurons, also called as spiking neural networks [SNNs]) and in-memory computing on chip, to overcome the Von Neumann bottleneck. [6] To implement the new architectures, artificial synapses and neurons are necessary. The massive parallelism of the brain takes advantage of a large number of neurons and synapses; however, the emulation of the functions of biological synapses and neurons requires tens of complement metal-oxide-semiconductor (CMOS) transistors. Therefore, it is urgent to realize synapses and neurons with a simplified method for the development of ANNs. [7] As synapses adjust their weights step-by-step repeatedly, synaptic devices need to exhibit analog switching behavior. [8] However, biological neurons show the analog membrane potential and generate the all-or-nothing rapidly, so both analog and digital devices are acceptable for the emulation of neurons. Many emerging devices have been proposed to develop ANNs based on hardware, e.g., memristive devices, phase change memories (PCMs), [9] magnetic random-access memories (MRAMs), [10] and ferroelectric field-effect transistors (FeFETs). [11] Each technology has its advantages and disadvantages. PCM and M...

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Stochastic neuron based on IGZO Schottky diodes for neuromorphic computing

Cited by 39 publications

References 28 publications

Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

Emerging Memristive Artificial Synapses and Neurons for Energy‐Efficient Neuromorphic Computing

Spiking neurons with spatiotemporal dynamics and gain modulation for monolithically integrated memristive neural networks

Artificial Neural Networks Based on Memristive Devices: From Device to System

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