An artificial neural cell implemented with superconducting circuits

Hidaka, Mutsuo; Akers, L.A.

doi:10.1088/0953-2048/4/11/027

Cited by 21 publications

(14 citation statements)

References 2 publications

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“…We argue that this effect facilitates new possibilities for development of tunable superconducting electronic components. For example, the considered "stranded wire" can be readily applied in a synaptic connection for a superconducting artificial neural networks (ANN) where information is represented in a "current domain" [10][11][12][13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…magnetically" the kinetic inductance of superconducting layers, and even transfer thin layers to a normal state at a fixed temperature.Experimental studies have shown that transition of thin s-layers to the normal state in the considered multilayer structure is possible; the temperature of this transition depends on the magnetic environment.Summarizing the entire above one can conclude that the electronic transport properties found in multilayer structure S/[F1/s/F2/s]n can be used to create different switching electronic elements, including synapses. Let's discuss this new type of application in more detail.The creation of artificial neural networks is one of the current trends in the development of superconductor electronics[10][11][12][13][14][15]. Such an artificial neural network contains layers of elements that nonlinearly transform the incoming signal (neurons) connected by linear tunable connections (synapses).…”

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confidence: 99%

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Controlling the proximity in a Co/Nb multilayer: the properties of electronic transport

Bakurskiy¹,

Kupriyanov²,

Klenov³

et al. 2020

Preprint

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We present both a theoretical and experimental investigation of the proximity effect in a stack-like superconductor/ferromagnet (S/F) superlattice, where ferromagnetic layers with different thicknesses and coercive fields are made of Co. Calculations based on Usadel equations allow us to find conditions at which switching from the parallel to the antiparallel alignment of neighboring F-layers leads to a significant change of the superconducting order parameter in thin s-films. Experimentally we study the transport properties of a lithographically patterned Nb/Co multilayer. We observe that the resistive transition of the multilayer contains multiple steps, which we attribute to the transition of individual s-layers with T c’s depending on the local magnetization orientation of neighbor F-layers. We argue that such superlattices can be used as tunable kinetic inductors, designed for artificial neural networks with a representation of information in the current domain.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Controlling the proximity in a Co/Nb multilayer: the properties of electronic transport

Bakurskiy¹,

Kupriyanov²,

Klenov³

et al. 2020

Preprint

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“…Superconducting neurons have been studied nearly as long as CMOS implementations, with a mapping between neuronal functions and superconducting electronics identified in the early 1990s (Harada and Goto, 1991 ; Hidaka and Akers, 1991 ). In this case, Faraday's Law, governing the addition of magnetic flux through mutual inductors to superconducting loops provides the necessary synaptic summation function.…”

Section: Electronic Neuronal Computationmentioning

confidence: 99%

Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware

Primavera

Shainline

2021

Front. Neurosci.

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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.

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“…One need to correspondingly change the sign of the terms containing sine function in (5) to perform the fitting procedure. The fitting result is presented in Fig.…”

Section: A Artificial Neuronmentioning

confidence: 99%

Adiabatic superconducting artificial neural network: Basic cells

Soloviev

Schegolev

Klenov

et al. 2018

Journal of Applied Physics

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We consider adiabatic superconducting cells operating as an artificial neuron and synapse of a multilayer perceptron (MLP). Their compact circuits contain just one and two Josephson junctions, respectively. While the signal is represented as magnetic flux, the proposed cells are inherently nonlinear and close-to-linear magnetic flux transformers. The neuron is capable of providing a one-shot calculation of sigmoid and hyperbolic tangent activation functions most commonly used in MLP. The synapse features by both positive and negative signal transfer coefficients in the range ∼ (−0.5, 0.5). We briefly discuss implementation issues and further steps toward multilayer adiabatic superconducting artificial neural network which promises to be a compact and the most energy-efficient implementation of MLP.

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An artificial neural cell implemented with superconducting circuits

Cited by 21 publications

References 2 publications

Controlling the proximity in a Co/Nb multilayer: the properties of electronic transport

Controlling the proximity in a Co/Nb multilayer: the properties of electronic transport

Considerations for Neuromorphic Supercomputing in Semiconducting and Superconducting Optoelectronic Hardware

Adiabatic superconducting artificial neural network: Basic cells

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