Nonzero Skyrmion Hall Effect in Topologically Trivial Structures

Msiska, Robin; Rodrigues, Davi R.; Leliaert, Jonathan; Everschor-Sitte, Karin

doi:10.1103/physrevapplied.17.064015

Cited by 12 publications

(2 citation statements)

References 63 publications

(72 reference statements)

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“…The common demonimator of all these implementations is the exploitation of separate and distinct sets of electrodes as network inputs and outputs. It is worth noticing that a similar implementation strategy was exploited also in case of a multichannel skyrmion-based reservoir [56].…”

Section: Implementations Of Rc In Nanonetworkmentioning

confidence: 99%

In materia implementation strategies of physical reservoir computing with memristive nanonetworks

Milano

Montano

Ricciardi

2023

J. Phys. D: Appl. Phys.

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Physical reservoir computing represents a computational framework that exploits information-processing capabilities of programmable matter, allowing the realization of energy-efficient neuromorphic hardware with fast learning and low training cost. Despite self-organized memristive networks have been demonstrated as physical reservoir able to extract relevant features from spatiotemporal input signals, multiterminal nanonetworks open the possibility for novel strategies of computing implementation. In this work, we report on implementation strategies of in materia reservoir computing with self-assembled memristive networks. Besides showing the spatiotemporal information processing capabilities of self-organized nanowire networks, we show through simulations that the emergent collective dynamics allows unconventional implementations of reservoir computing where the same electrodes can be used as both reservoir inputs and outputs. By comparing different implementation strategies on a digit recognition task, simulations show that the unconventional implementation allows a reduction of the hardware complexity without limiting computing capabilities, thus providing new insights for taking full advantage of in materia computing toward a rational design of neuromorphic systems.

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Section: Implementations Of Rc In Nanonetworkmentioning

confidence: 99%

In materia implementation strategies of physical reservoir computing with memristive nanonetworks

Milano

Montano

Ricciardi

2023

J. Phys. D: Appl. Phys.

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“…86) Msiska et al introduced a nanosecondscale multichannel skyrmion reservoir for pattern recognition, achieving outstanding accuracy in spoken digit speech recognition. 87) Similarly, Liu et al proposed a stackable reservoir system using ferroelectric α-In 2 Se 3 devices, demonstrating impressive memory capacity and computing capability for advanced neuromorphic computing with emerging two-dimensional materials. 88) Nakane et al delved into spin-wave-based RC, emphasizing its potential for edgecomputing applications (Fig.…”

Section: Spintronic Reservoir Computingmentioning

confidence: 99%

An organized view of reservoir computing: a perspective on theory and technology development

Abdi,

Mazur,

Szaciłowski

2024

Jpn. J. Appl. Phys.

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Reservoir computing is an unconventional computing paradigm that uses system complexity and dynamics as computational medium. Currently it is the leading computational paradigm in the fields of unconventional in materia computing. This review briefly outlines the theory behind the term ‘reservoir computing’, presents the basis for evaluation of reservoirs and presents a cultural reference of reservoir computing in haiku. The summary highlights recent advances in physical reservoir computing and points out the importance of the drive, usually neglected in physical implementations of reservoir computing. However, drive signals may further simplify the training of reservoirs’ readout layer training, thus contributing to improved performance of reservoir computer performance.

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Antiferromagnetic spintronics and beyond

Dal Din,

Amin,

Wadley

et al. 2024

npj Spintronics

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In this review article, we summarize some recent key results in the development of antiferromagnetic spintronics. Current-induced switching of the Néel vector orientation has now been established in a wide range of antiferromagnetic films and antiferromagnet / heavy metal bilayers, as well as current-driven motion of antiferromagnetic spin textures. The latter are particularly promising due to their small size and topological stability, but reading their magnetic state presents challenges. We also focus on materials whose compensated spin arrangements (either collinear or noncollinear) are coexistent with a spin-split band structure, enabling first-order spintronic phenomena including giant and tunneling magnetoresistance, and the anomalous Hall effect. The resulting combination of efficient electrical readout mechanisms with the advantages of a near-zero net magnetization has potential to be transformative for spintronic applications.

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Nonzero Skyrmion Hall Effect in Topologically Trivial Structures

Cited by 12 publications

References 63 publications

In materia implementation strategies of physical reservoir computing with memristive nanonetworks

In materia implementation strategies of physical reservoir computing with memristive nanonetworks

An organized view of reservoir computing: a perspective on theory and technology development

Antiferromagnetic spintronics and beyond

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