Perspective on unconventional computing using magnetic skyrmions

Lee, Oscar; Msiska, Robin; Brems, Maarten A.; Kläui, Mathias; Kurebayashi, Hidekazu; Everschor-Sitte, Karin

doi:10.1063/5.0148469

Cited by 10 publications

(3 citation statements)

References 163 publications

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“…The very accessible fabrication method, and their stability at room temperature, establishes a new platform for both fundamental and applied research of dipolar SKs and ASKs of arbitrary topological charge. This material system and the hosted magnetic textures, reported in this work, provide extra degrees of freedom enabling different applications ranging from unconventional computing 27 to new storage concepts 28 , and deliver an important contribution to the emerging field of skyrmionics 29 .…”

Section: Mainmentioning

confidence: 95%

Dipolar skyrmions and antiskyrmions of arbitrary topological charge at room temperature

Hassan,

Koraltan,

Ullrich

et al. 2024

Nat. Phys.

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Magnetic skyrmions are localized, stable topological magnetic textures that can move and interact with each other like ordinary particles when an external stimulus is applied. The efficient control of the motion of spin textures using spin-polarized currents opened an opportunity for skyrmionic devices such as racetrack memory and neuromorphic or reservoir computing. The coexistence of skyrmions with high topological charge in the same system promises further possibilities for efficient technological applications. In this work, we directly observe dipolar skyrmions and antiskyrmions with arbitrary topological charge in Co/Ni multilayers at room temperature. We explore the dipolar-stabilized spin objects with topological charges of up to 10 and characterize their nucleation process, their energy dependence on the topological charge and the effect of the material parameters on their stability. Furthermore, our micromagnetic simulations demonstrate spin-transfer-induced motion of these spin objects, which is important for their potential device application.

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Section: Mainmentioning

confidence: 95%

Dipolar skyrmions and antiskyrmions of arbitrary topological charge at room temperature

Hassan,

Koraltan,

Ullrich

et al. 2024

Nat. Phys.

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“…Recent proposals have shown that dynamical systems can be used to emulate neuron synapses and firing for synaptic neural networks [103], as well as to perform weight computations or completely replace hidden layers in a neural network [26,34,102,104,105]. Two promising applications that consolidate the use of dynamical systems for spatio-temporal pattern recognition are reservoir computing [34,105,108] and physical neural networks [26, 102-104, 106, 109]. While both computational paradigms allow for learning and extracting patterns from data, they rely on different learning schemes.…”

Section: Statusmentioning

confidence: 99%

Roadmap for unconventional computing with nanotechnology

Finocchio,

Incorvia,

Friedman

et al. 2024

Nano Futures

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In the “Beyond Moore’s Law” era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, the adoption of a wide variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber-resilience and processing prowess. The time is ripe to lay out a roadmap for unconventional computing with nanotechnologies to guide future research and this collection aims to fulfill that need. The authors provide a comprehensive roadmap for neuromorphic computing with electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets and assorted dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain inspired computing for incremental learning and solving problems in severely resource constrained environments. All of these approaches have advantages over conventional Boolean computing predicated on the von-Neumann architecture. With the computational need for artificial intelligence growing at a rate 50×faster than Moore’s law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon and this roadmap will aid in identifying future needs and challenges.

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“…The field of physical reservoir computing has been rapidly expanding with several promising demonstrations using optical systems 9 , analogue electronic circuits 10 , memristors 11 , 12 , ferroelectrics 13 , magnetic systems 14 – 19 and even a bucket of water 20 . Skyrmions, topologically non-trivial magnetic whirls, have also been proposed as hosts for reservoir computing 21 – 24 and experimentally demonstrated 25 – 27 as part of rapidly growing research efforts towards neuromorphic computing using magnetic systems 28 – 32 .…”

Section: Mainmentioning

confidence: 99%

Task-adaptive physical reservoir computing

Lee,

Wei,

Stenning

et al. 2023

Nat. Mater.

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Reservoir computing is a neuromorphic architecture that may offer viable solutions to the growing energy costs of machine learning. In software-based machine learning, computing performance can be readily reconfigured to suit different computational tasks by tuning hyperparameters. This critical functionality is missing in ‘physical’ reservoir computing schemes that exploit nonlinear and history-dependent responses of physical systems for data processing. Here we overcome this issue with a ‘task-adaptive’ approach to physical reservoir computing. By leveraging a thermodynamical phase space to reconfigure key reservoir properties, we optimize computational performance across a diverse task set. We use the spin-wave spectra of the chiral magnet Cu2OSeO3 that hosts skyrmion, conical and helical magnetic phases, providing on-demand access to different computational reservoir responses. The task-adaptive approach is applicable to a wide variety of physical systems, which we show in other chiral magnets via above (and near) room-temperature demonstrations in Co8.5Zn8.5Mn3 (and FeGe).

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Perspective on unconventional computing using magnetic skyrmions

Cited by 10 publications

References 163 publications

Dipolar skyrmions and antiskyrmions of arbitrary topological charge at room temperature

Dipolar skyrmions and antiskyrmions of arbitrary topological charge at room temperature

Roadmap for unconventional computing with nanotechnology

Task-adaptive physical reservoir computing

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