Dynamically Driven Emergence in a Nanomagnetic System

Dawidek, Richard W. S.; Hayward, Thomas J.; Vidamour, Ian T.; Broomhall, T. J.; Venkat, Guru; Mamoori, Mohanad Al; Mullen, Aidan; Kyle, Stephan J.; Fry, P. W.; Steinke, Nina‐Juliane; Cooper, Joshaniel F. K.; Maccherozzi, Francesco; Dhesi, S. S.; Aballe, Lucía; Foerster, Michael; Prat, Jordi; Vasilaki, Eleni; Ellis, Matthew O. A.; Allwood, D. A.

doi:10.1002/adfm.202008389

Cited by 35 publications

(31 citation statements)

References 38 publications

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“…No input mask was used (i.e only one virtual neuron was used) since there were already a large number of outputs available to the classifier and additional virtual neurons in this context separated each input timestep by a time on the reservoir but in this case it was important to allow the input at successive timesteps to interact through the fading memory. Our approach here replicated the preprocessing and data input approach used successfully in a previous publication 13 .…”

Section: Resultsmentioning

confidence: 60%

Neuromorphic computation with a single magnetic domain wall

Ababei

Ellis

Vidamour

et al. 2021

Sci Rep

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Machine learning techniques are commonly used to model complex relationships but implementations on digital hardware are relatively inefficient due to poor matching between conventional computer architectures and the structures of the algorithms they are required to simulate. Neuromorphic devices, and in particular reservoir computing architectures, utilize the inherent properties of physical systems to implement machine learning algorithms and so have the potential to be much more efficient. In this work, we demonstrate that the dynamics of individual domain walls in magnetic nanowires are suitable for implementing the reservoir computing paradigm in hardware. We modelled the dynamics of a domain wall placed between two anti-notches in a nickel nanowire using both a 1D collective coordinates model and micromagnetic simulations. When driven by an oscillating magnetic field, the domain exhibits non-linear dynamics within the potential well created by the anti-notches that are analogous to those of the Duffing oscillator. We exploit the domain wall dynamics for reservoir computing by modulating the amplitude of the applied magnetic field to inject time-multiplexed input signals into the reservoir, and show how this allows us to perform machine learning tasks including: the classification of (1) sine and square waves; (2) spoken digits; and (3) non-temporal 2D toy data and hand written digits. Our work lays the foundation for the creation of nanoscale neuromorphic devices in which individual magnetic domain walls are used to perform complex data analysis tasks.

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Section: Resultsmentioning

confidence: 60%

Neuromorphic computation with a single magnetic domain wall

Ababei

Ellis

Vidamour

et al. 2021

Sci Rep

Self Cite

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“…The resulting TW is now in its ground state and remains metastable even at significantly higher diameters, making it hard to induce an inverse transformation. Additionally, our results indicate how curvature can play a key role for the control of the stability and pinning of DWs, tuning their stochastic behavior in proposed neuromorphic devices 60 .…”

mentioning

confidence: 78%

Micromagnetic modelling of magnetic domain walls in curved cylindrical nanotubes and nanowires

Skoric,

Donnelly,

Abert

et al. 2021

Preprint

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“…3D magnetic lattices are already well-suited for neuromorphic computing applications. They have intrinsic high-connectivity with a random distribution of synaptic weights at junctions, naturally realizing the essential ingredients for reservoir computing as seen in 2D magnetic arrays [250]. Furthermore, the use of coherent spin-wave propagation in such systems allows the exploration of interference-based computing.…”

Section: F Magnetic Charge Transport and Spin-wave Propagation In 3d ...mentioning

confidence: 99%

Advances in Magnetics Roadmap on Spin-Wave Computing

et al. 2022

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Magnonics addresses the physical properties of spin waves and utilizes them for data processing. Scalability down to atomic dimensions, operation in the GHz-to-THz frequency range, utilization of nonlinear and nonreciprocal phenomena, and compatibility with CMOS are just a few of many advantages offered by magnons. Although magnonics is still primarily positioned in the academic domain, the scientific and technological challenges of the field are being extensively investigated, and many proof-of-concept prototypes have already been realized in laboratories. This roadmap is a product of the collective work of many authors that covers versatile spin-wave computing approaches, conceptual building blocks, and underlying physical phenomena. In particular, the roadmap discusses the computation operations with Boolean digital data, unconventional approaches like neuromorphic computing, and the progress towards magnon-based quantum computing. The article is organized as a collection of sub-sections grouped into seven large thematic sections. Each sub-section is prepared by one or a group of authors and concludes with a brief description of current challenges and the outlook of further development for each research direction.

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Dynamically Driven Emergence in a Nanomagnetic System

Cited by 35 publications

References 38 publications

Neuromorphic computation with a single magnetic domain wall

Neuromorphic computation with a single magnetic domain wall

Micromagnetic modelling of magnetic domain walls in curved cylindrical nanotubes and nanowires

Advances in Magnetics Roadmap on Spin-Wave Computing

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