Threshold switching memristor-based stochastic neurons for probabilistic computing

Wang, Kuan; Hu, Qinghe; Gao, Bin; Lin, Qi; Zhuge, Fuwei; Zhang, Dayou; Wang, Lun; He, Yuhui; Scheicher, Ralph H.; Tong, Hao; Miao, Xiangshui

doi:10.1039/d0mh01759k

Cited by 59 publications

(40 citation statements)

References 32 publications

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“…As depicted in Figure 4F, tuning the input pulse width can generate stochastic outputs with different firing rates. Another implementation of a Cu filament-based TS device (Wang et al, 2021) presented LIF neuron behavior by coupling the device with two resistors in series and a capacitor in parallel (Figure 4G). The capacitor imitates the membrane potential, whereas the resistors limit the total current intensity and divide the input voltage.…”

Section: Filament-based Neuronmentioning

confidence: 99%

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Emerging Artificial Neuron Devices for Probabilistic Computing

Geng

Wang

et al. 2021

Front. Neurosci.

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In recent decades, artificial intelligence has been successively employed in the fields of finance, commerce, and other industries. However, imitating high-level brain functions, such as imagination and inference, pose several challenges as they are relevant to a particular type of noise in a biological neuron network. Probabilistic computing algorithms based on restricted Boltzmann machine and Bayesian inference that use silicon electronics have progressed significantly in terms of mimicking probabilistic inference. However, the quasi-random noise generated from additional circuits or algorithms presents a major challenge for silicon electronics to realize the true stochasticity of biological neuron systems. Artificial neurons based on emerging devices, such as memristors and ferroelectric field-effect transistors with inherent stochasticity can produce uncertain non-linear output spikes, which may be the key to make machine learning closer to the human brain. In this article, we present a comprehensive review of the recent advances in the emerging stochastic artificial neurons (SANs) in terms of probabilistic computing. We briefly introduce the biological neurons, neuron models, and silicon neurons before presenting the detailed working mechanisms of various SANs. Finally, the merits and demerits of silicon-based and emerging neurons are discussed, and the outlook for SANs is presented.

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Section: Filament-based Neuronmentioning

confidence: 99%

“…(H) Measured stochastic spike events of the CuS/GeSe-based neuronal circuit. (I) Comparison of the output probability of deterministic and stochastic neurons(Wang et al, 2021).…”

mentioning

confidence: 99%

Emerging Artificial Neuron Devices for Probabilistic Computing

Geng

Wang

et al. 2021

Front. Neurosci.

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“…The dynamic properties of FLBP-CsPbBr 3 TSM were investigated in Fig. 2d by applying the voltage pulses and recording the resulting current [39][40][41][42][43] . Following the applied voltage pulses of 1 V, the current jumped suddenly to Icc of 1 × 10 −5 A.…”

Section: Resultsmentioning

confidence: 99%

Memristor-based biomimetic compound eye for real-time collision detection

et al. 2021

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The lobula giant movement detector (LGMD) is the movement-sensitive, wide-field visual neuron positioned in the third visual neuropile of lobula. LGMD neuron can anticipate collision and trigger avoidance efficiently owing to the earlier occurring firing peak before collision. Vision chips inspired by the LGMD have been successfully implemented in very-large-scale-integration (VLSI) system. However, transistor-based chips and single devices to simulate LGMD neurons make them bulky, energy-inefficient and complicated. The devices with relatively compact structure and simple operation mode to mimic the escape response of LGMD neuron have not been realized yet. Here, the artificial LGMD visual neuron is implemented using light-mediated threshold switching memristor. The non-monotonic response to light flow field originated from the formation and break of Ag conductive filaments is analogue to the escape response of LGMD neuron. Furthermore, robot navigation with obstacle avoidance capability and biomimetic compound eyes with wide field-of-view (FoV) detection capability are demonstrated.

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“…It can be observed that V hold (~1.2 V) is more stable than V th (~3.1 V) of the Ag/V 2 C/W memristor. Still, there is a window between V th and V hold , which shows potential for neuromorphic circuits design [43,44].…”

Section: Resultsmentioning

confidence: 99%

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

et al. 2021

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Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V2C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V2C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V2C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.

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Threshold switching memristor-based stochastic neurons for probabilistic computing

Abstract: Stochastic neurons based on CuS/GeSe conductive bridge threshold switching memristors are designed to mimic the probabilistic computing ability of the brain at a hardware level.

Cited by 59 publications

References 32 publications

Emerging Artificial Neuron Devices for Probabilistic Computing

Emerging Artificial Neuron Devices for Probabilistic Computing

Memristor-based biomimetic compound eye for real-time collision detection

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

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