scholarly journals Potential implementation of reservoir computing models based on magnetic skyrmions

AIP Advances ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 055602 ◽  
Author(s):  
George Bourianoff ◽  
Daniele Pinna ◽  
Matthias Sitte ◽  
Karin Everschor-Sitte
2021 ◽  
pp. 24-36
Author(s):  
Unai Armentia ◽  
Irantzu Barrio ◽  
Javier Del Ser

2012 ◽  
Vol 9 (5) ◽  
pp. 6101-6134 ◽  
Author(s):  
N. J. de Vos

Abstract. Despite theoretical benefits of recurrent artificial neural networks over their feedforward counterparts, it is still unclear whether the former offer practical advantages as rainfall-runoff models. The main drawback of recurrent networks is the increased complexity of the training procedure due to their architecture. This work uses recently introduced, conceptually simple reservoir computing models for one-day-ahead forecasts on twelve river basins in the Eastern United States, and compares them to a variety of traditional feedforward and recurrent models. Two modifications on the reservoir computing models are made to increase the hydrologically relevant information content of their internal state. The results show that the reservoir computing networks outperform feedforward networks and are competitive with state-of-the-art recurrent networks, across a range of performance measures. This, along with their simplicity and ease of training, suggests that reservoir computing models can be considered promising alternatives to traditional artificial neural networks in rainfall-runoff modelling.


2021 ◽  
pp. 115022
Author(s):  
Wei-Jia Wang ◽  
Yong Tang ◽  
Jason Xiong ◽  
Yi-Cheng Zhang

2019 ◽  
Author(s):  
Federica Eftimiadi ◽  
Enrico Pugni Trimigliozzi

Reversible computing is a paradigm where computing models are defined so that they reflect physical reversibility, one of the fundamental microscopic physical property of Nature. Also, it is one of the basic microscopic physical laws of nature. Reversible computing refers tothe computation that could always be reversed to recover its earlier state. It is based on reversible physics, which implies that we can never truly erase information in a computer. Reversible computing is very difficult and its engineering hurdles are enormous. This paper provides a brief introduction to reversible computing. With these constraints, one can still satisfactorily deal with both functional and structural aspects of computing processes; at the same time, one attains a closer correspondence between the behavior of abstract computing systems and the microscopic physical laws (which are presumed to be strictly reversible) that underlay any implementation of such systems Available online at https://int-scientific-journals.com


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Anthony K. C. Tan ◽  
Pin Ho ◽  
James Lourembam ◽  
Lisen Huang ◽  
Hang Khume Tan ◽  
...  

AbstractMagnetic skyrmions are nanoscale spin textures touted as next-generation computing elements. When subjected to lateral currents, skyrmions move at considerable speeds. Their topological charge results in an additional transverse deflection known as the skyrmion Hall effect (SkHE). While promising, their dynamic phenomenology with current, skyrmion size, geometric effects and disorder remain to be established. Here we report on the ensemble dynamics of individual skyrmions forming dense arrays in Pt/Co/MgO wires by examining over 20,000 instances of motion across currents and fields. The skyrmion speed reaches 24 m/s in the plastic flow regime and is surprisingly robust to positional and size variations. Meanwhile, the SkHE saturates at ∼22∘, is substantially reshaped by the wire edge, and crucially increases weakly with skyrmion size. Particle model simulations suggest that the SkHE size dependence — contrary to analytical predictions — arises from the interplay of intrinsic and pinning-driven effects. These results establish a robust framework to harness SkHE and achieve high-throughput skyrmion motion in wire devices.


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