Abstract
We investigated the dynamic processes in a superconducting neuron based on Josephson contacts without resistive shunting (SC-neuron). Such a cell is a key element of perceptron-type neural networks that operate in both classical and quantum modes. The analysis of the obtained results allowed us to find the mode when the transfer characteristic of the element implements the “sigmoid” activation function. The numerical approach to the analysis of the equations of motion and the Monte Carlo method revealed the influence of inertia (capacitances), dissipation, and temperature on the dynamic characteristics of the neuron.
Highlights
We have developed the design of the basic cell of the neural network named the SC -neuron, which is based on Josephson junctions without resistive shunting
The functioning of a superconducting neuron was discussed only within the framework of the inertialess model, in which the output current instantly follows the change in the control magnetic flux
As it was shown in this paper, if we take into account the capacity of the Josephson contact, this leads to a qualitative change in the switching mode: i) if the element in “logical mode” was initially in the equilibrium position and its state was represented by a point in the phase space; ii) after half of the switching cycle, its states are described by the dots on a closed curve
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Implementation of the principles of adiabatic superconducting logic (ASL) [16,17,18] The latter helped to overcome the fundamental limitation on energy efficiency [14]. The performance of the hardware implementation of a superconducting neural processor while executing the test on examples of standard configurations of neural networks exceeds the performance of a semiconductor analog (TPU) by 23 times on average [1]. These indicators were demonstrated when using memory with a bandwidth (300 GB/s) and a typical clock frequency of a superconducting processor (52.6 GHz) [1]. We carried out the selection and optimization of the parameters of a perceptron-type superconducting circuit for the implementation of the “sigmoid” activation function, which is most convenient for neural network training algorithms and solving problems of pattern recognition and images
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