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SUMMARY:IMX Talks - Magnonic Hardware for Pattern Recognition
DTSTART:20250321T104500
DTEND:20250321T114500
DTSTAMP:20260921T171553Z
UID:aee13f359b46b4ce906ea869e2c3f53f43d2a6537c4b44756d01ad94
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr. Katrin Schultheiss\, Helmholtz-Zentrum Dresden – Rossen
 dorf e. V.\, Germany\nNeural networks are powerful tools to learn patterns
  and make inferences in complex problems. However\, they rely on a massive
  number of neurons and interconnecting weights which require extensive tra
 ining using a large dataset. To compensate for this\, reservoir computing 
 is based on recurrent neural networks with randomly fixed weights. Thereby
 \, only the output weights require training for a particular task\, reduci
 ng the training to a simple linear regression. Recently\, there has been a
  shift towards physical reservoir computing\, offering potential\nadvantag
 es in speed\, energy efficiency\, and hardware simplicity. Physical reserv
 oir computing utilizes the inherent nonlinearity of physical systems to ma
 p the input into a higher-dimensional space in which different input patte
 rns become linearly separable. New advancements and experimental implement
 ations use diverse physical substrates\, including mechanical structures\,
  optical systems\, and spintronic devices.\nIn our work\, we take advantag
 e of the rich nonlinear dynamics inside magnetic vortices. Their eigenmode
  system comprises the gyrotropic motion of the vortex core as well as magn
 on modes with well-defined radial and azimuthal quantization in the vortex
 ’s skirt. Harnessing the nonlinear interactions between these different 
 vortex eigenmodes in reciprocal space\, it is possible to perform temporal
  information processing and pattern recognition without relying on informa
 tion transport in real space [1]. This presentation will give a comprehens
 ive overview of experimental results and numerical simulations demonstrati
 ng the capabilities and advantages of magnon reservoir computing. Addition
 ally\, the mutual nonlinear interactions between the magnon modes and the 
 vortex core gyration lead to a much richer set of eigenstates\, extending 
 the dimensionality of the magnon scattering reservoir.\nACKNOWLEDGMENT\nTh
 is work has received funding from the EU Research and Innovation Programme
  Horizon Europe under grant agreement no. 101070290 (NIMFEIA).\nREFERENCES
 \n[1] L. Körber\, et al.\, “Pattern recognition in reciprocal space wit
 h a magnon-scattering reservoir” Nature Communications\, 14\, 3954 (2023
 ).\n\nBio: Dr. Katrin Schultheiss received her PhD in Physics (Dr. rer. na
 t.) on spin-wave transport in two-dimensional microstructures from the Tec
 hnische Universität Kaiserslautern\, Germany in 2013. Since 2015\, she wo
 rks at the Institute of Ion Beam Physics and Materials Research at the Hel
 mholtz-Zentrum Dresden-Rossendorf (HZDR) in Dresden\, Germany. As a member
  of the Department of Magnetism\, her research is focussed on the study of
  linear and nonlinear magnetization dynamics in micro- and nanostructure a
 s well as in spin textures using Brillouin light scattering microscopy. Fo
 r her achievements on "Nonlinear magnonics as the foundation of spin-based
  neuromorphic computing" she received the HZDR Forschungspreis in 2022.
LOCATION:BM 3241 https://plan.epfl.ch/?room==BM%203241
STATUS:CONFIRMED
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