Pattern recognition with magnons

Within the last decade, spintronics and magnonics have demonstrated an impressive development in the experimental realization of Boolean logic gates. However, the exponential growth of data and the rise of the internet of things are pushing the deterministic Boolean computing of von-Neumann architectures to their limits or simply consume too much energy. Moreover, conventional Boolean computer architectures are likely to remain inefficient for certain cognitive tasks in which the human brain excels, such as pattern recognition, particularly when incomplete or noisy data are involved.
One of the most generic and abstract implementations of brain-inspired computing schemes is reservoir computing, which uses the nonlinearity and recurrence of a physical system to separate patterns of time series data into distinct manifolds of a higher dimensional output space. In this presentation, I will demonstrate the experimental realization of pattern recognition based on reservoir computing using magnons.
Recently, we reported on the nonlinear scattering of magnons in vortices in micron-sized NiFe discs [1] which we learned to control and stimulate by means of other magnons [2]. Now, we utilize these phenomena to employ magnons for pattern recognition without relying on magnon transport in real space. I will present a comprehensive overview of experimental results and numerical simulations demonstrating the capabilities and advantages of magnon reservoir computing in reciprocal space.
[1] K. Schultheiss, et al. Physical Review Letters 122, 097202 (2019)
[2] L. Körber, et al. Physical Review Letters 125, 207203 (2020)