Potential implementation of reservoir computing models based on magnetic skyrmions

G. Bourianoff, Daniele Pinna, Matthias Sitte, Karin Everschor-Sitte

Research output: Contribution to journalArticle

28 Citations (Scopus)


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 to implement reservoir computing prior to this have focused on utilizing memristor techniques to implement recursive neural networks. This paper examines the potential of magnetic skyrmion fabrics and the complex current patterns which form in them as an attractive physical instantiation for Reservoir Computing. We argue that their nonlinear dynamical interplay resulting from anisotropic magnetoresistance and spin-torque effects allows for an effective and energy efficient nonlinear processing of spatial temporal events with the aim of event recognition and prediction.

Original languageEnglish
Article number5006918
JournalAIP Advances
Issue number5
Publication statusPublished - May 1 2018

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Fingerprint Dive into the research topics of 'Potential implementation of reservoir computing models based on magnetic skyrmions'. Together they form a unique fingerprint.

  • Cite this