Adiabatic Superconducting Artificial Neural Network: Basic Cells

  1. I. I. Soloviev,
  2. A. E. Schegolev,
  3. N. V. Klenov,
  4. S. V. Bakurskiy,
  5. M. Yu. Kupriyanov,
  6. M. V. Tereshonok,
  7. A. V. Shadrin,
  8. V. S. Stolyarov,
  9. and A. A. Golubov
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.