6533b7d6fe1ef96bd12671af
RESEARCH PRODUCT
Potential implementation of reservoir computing models based on magnetic skyrmions
Daniele PinnaKarin Everschor-sitteMatthias SitteGeorge I. Bourianoffsubject
Distributed computingMathematicsofComputing_NUMERICALANALYSISFOS: Physical sciencesGeneral Physics and Astronomy02 engineering and technologyMemristor01 natural scienceslaw.inventionlawMesoscale and Nanoscale Physics (cond-mat.mes-hall)0103 physical sciences010306 general physicsTopology (chemistry)PhysicsCondensed Matter - Mesoscale and Nanoscale PhysicsArtificial neural networkHierarchy (mathematics)SkyrmionReservoir computingPhysik (inkl. Astronomie)021001 nanoscience & nanotechnologylcsh:QC1-999Recurrent neural networkNode (circuits)0210 nano-technologylcsh:Physicsdescription
Reservoir Computing is a type of recursive neural network commonly used for recognizing and predicting spatio-temporal events relying on a complex hierarchy of nested feedback loops to generate a memory functionality. The Reservoir Computing paradigm does not require any knowledge of the reservoir topology or node weights for training purposes and can therefore utilize naturally existing networks formed by a wide variety of physical processes. Most efforts prior to this have focused on utilizing memristor techniques to implement recursive neural networks. This paper examines the potential of skyrmion fabrics formed in magnets with broken inversion symmetry that may provide an attractive physical instantiation for Reservoir Computing.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-05-01 | AIP Advances |