Year: 2018
Type: article
Abstract: 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 the one-shot calculation of sigmoid and hyperbolic tangent activation functions most commonly used in MLP. The synapse features both positive and negative signal transfer coefficients in the range ∼(−0.5,0.5). We briefly discuss implementation issues and further steps toward the multilayer adiabatic superconducting artificial neural network, which promises to be a compact and the most energy-efficient implementation of MLP.
Source: Journal of Applied Physics
DOI: 10.1063/1.5042147
Institutions: Moscow Institute of Physics and Technology, Lomonosov Moscow State University, Kazan Federal University, Osipyan Institute of Solid State Physics RAS, All Russia Research Institute of Automatics, Moscow Technical University of Communication and Informatics, MIREA - Russian Technological University, Skobeltsyn Institute of Nuclear Physics, Lomonosov Moscow State University, University of Twente
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Award: G5850874444
OpenAlex ID: W2883046252