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Reservoir Computing with Random Skyrmion Textures

Daniele Pinna, George I. Bourianoff, Karin Everschor‐Sitte

2020Physical Review Applied161 citationsDOIOpen Access PDF

Abstract

The reservoir computing paradigm posits that complex physical systems can be used to simplify pattern recognition tasks and nonlinear signal prediction. We show that random topological magnetic textures pinned by grain inhomogeneities demonstrate desirable dynamical responses for the implementation of reservoir computing as applied to ac current pulses. By harnessing the complex resistance or magnetization responses exhibited by random magnetic skyrmion textures to demonstrate simple pattern recognition, we explain how spintronics systems offer an advantage in the search for an ideal reservoir computer. The dynamical properties of compact skyrmion fabrics, coupled with their CMOS integrability operating on similar length and timescales, open the door for skyrmion-based reservoir computing applications.

Topics & Concepts

SkyrmionPhysicsComputer scienceCondensed matter physicsNeural Networks and Reservoir ComputingAdvanced Memory and Neural ComputingMachine Learning and ELM
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