Learning cell for superconducting neural networks
Andrey E. Schegolev, N. V. Klenov, I. I. Soloviev, М. В. Терешонок
Abstract
Abstract 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.
Topics & Concepts
Artificial neural networkAdiabatic processComputer scienceSuperconductivitySupervised learningBiological systemMaterials scienceArtificial intelligencePhysicsCondensed matter physicsQuantum mechanicsBiologyAdvanced Memory and Neural ComputingNeural Networks and ApplicationsFerroelectric and Negative Capacitance Devices