Towards energy efficient magnetic in materio computing

Karin Everschor-Sitte, University of Duisburg-Essen, Germany

Fig. 1: Artistic view superimposing the spatially resolved non-linearity and memory measure of a skyrmion fabrics.

Novel computational paradigms in combination with proper hardware solutions are required to overcome the limitations of our state-of-the-art computer technology [1-3]. In this talk, I will focus on the potential of magnetic textures for energy efficient reservoir computing. Reservoir computing is a computational scheme that allows to drastically simplify spatial-temporal recognition tasks. We have shown that random skyrmion fabrics provide a suitable physical implementation of the reservoir [4,5] and allow to classify patterns via their complex resistance responses either by tracing the signal over time or by a single spatially resolved measurement [6]. Efficient task agnostic metrics benchmarking the reservoir’s key features – non-linearity, complexity and fading memory – drastically speed up the parameter search to design efficient and high-performance reservoirs [7].

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[2] E. Vedmedenko, R. Kawakami, D. Sheka, …, KES, et al., J. of Phys. D 53, 453001 (2020)
[3] G. Finocchio, M. Di Ventra, K.Y. Camsari, KES, P. K. Amiri, Z. Zeng, JMMM 521, 167506 (2021)
[4] D. Prychynenko, M. Sitte, et al, KES, Phys. Rev. Appl. 9, 014034 (2018)
[5] G. Bourianoff, D. Pinna, M. Sitte and KES, AIP Adv. 8, 055602 (2018)
[6] D. Pinna, G. Bourianoff and KES, Phys. Rev. Appl. 14, 054020 (2020)
[7] J. Love, J. Mulkers, G. Bourianoff, J. Leliaert, KES, arXiv:2108.01512