Abstract
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.
Highlights
A great deal has been written about the end of CMOS scaling, continuation of Moore’s Law and the need for alternative models of computing and related technologies
We have argued that skyrmion fabrics embedded in broken inversion symmetry magnetic substrates are potentially attractive options for implementing Echo State (ES) recognition and prediction systems
The principle result from this paper shows how skyrmion fabrics induce a strongly perturbed current flow through the magnetic texture as compared to the expected current flow through the homogenously magnetized state
Summary
A great deal has been written about the end of CMOS scaling, continuation of Moore’s Law and the need for alternative models of computing and related technologies. One of the most authoritative discussions on Moore’s Law can be found in the “final” International Technology Roadmap for Semiconductors (ITRS)[1] published in 2015 and which had been continuously published since 1991 It predicted that CMOS transistors would quit shrinking in 2021 with the 5 nm node and that a great many technical challenges would need to be met for the 5 nm node to be economically viable. In this paper we focus on Reservoir Computing (RC) models[26,27,28,29,30,31] implemented with selforganizing neural networks in complex magnetic textures.[22] The nodes are represented by magnetic skyrmions and the random connectivity by low magnetoresistive pathways in the material. IV we conclude how well the capabilities of magnetic substrates meet the needs of RC and delineate areas of future research
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