Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics

Biologically inspired recurrent neural networks, such as reservoir computers are of interest in designing spatio-temporal data processors from a hardware point of view due to the simple learning scheme and deep connections to Kalman filters. In this work we discuss using in-depth simulation studies a way to construct hardware reservoir computers using an analog stochastic neuron cell built from a low energy-barrier magnet based magnetic tunnel junction and a few transistors. This allows us to implement a physical embodiment of the mathematical model of reservoir computers. Compact implementation of reservoir computers using such devices may enable building compact, energy-efficient signal processors for standalone or in-situ machine cognition in edge devices.Comment: To be presented at International Conference on Neuromorphic Systems 202

Skyrmion stabilization and non-equilibrium nucleation routes in ultrathin cobalt-based magnetic trilayers

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Física de la Materia Condensada. Fecha de lectura: 29-09-2021Esta tesis tiene embargado el acceso al texto completo hasta el 29-03-202