Learning cell for superconducting neural networks

An energy-efficient adiabatic learning neuro cell is proposed. The cell can be used for on-chip learning of adiabatic superconducting artificial neural networks. The static and dynamic characteristics of the proposed learning cell have been investigated. Optimization of the learning cell parameters was performed within simulations of the multi-layer neural network supervised learning with the resilient propagation method.

Automated summary

This study introduces an energy-efficient adiabatic learning neuro cell for on-chip learning of adiabatic superconducting artificial neural networks. The static and dynamic characteristics of the learning cell have been researched, and optimization of its parameters was conducted through simulations of a multi-layer neural network with the resilient propagation method.