Computation in artificial spin ice

Jensen, Johannes H.; Folven, Erik; Tufte, Gunnar

doi:10.1162/isal_a_00011

Cited by 48 publications

(53 citation statements)

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“…Our work will also have interesting implications for artificial spin ice systems, the dynamics of which are intimately linked to the types of DW dynamics studied here 5,6 . These have proposed applications in the rapidly emerging field of neuromorphic computing 55,56 , and the ability to tune the level of stochasticity in such systems may be highly valuable to realising viable devices.…”

Section: Discussionmentioning

confidence: 99%

Suppression of Dynamically Induced Stochastic Magnetic Behavior Through Materials Engineering

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Suppression of Dynamically Induced Stochastic Magnetic Behavior Through Materials Engineering

et al. 2020

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“…To continue to fulfill the rapidly growing computing demands of the modern day, it will be necessary to develop novel physical computing architectures that are scalable, capable of learning, energy-efficient, and fault-tolerant. The use of selforganizing substrates showing an inherent capacity for information transmission, storage, and modification [1] would bring computation into the physical domain, enabling improved efficiency through the direct exploitation of material and physical processes for computation [2][3][4]. Some key properties of self-organizing systems that make them well-suited for computational tasks include their lack of centralized control and their adaptive response to changes in their environment [5].…”

Section: Introductionmentioning

confidence: 99%

Assessment and manipulation of the computational capacity of in vitro neuronal networks through criticality in neuronal avalanches

Heiney

Ramstad

Sandvig

et al. 2019

2019 IEEE Symposium Series on Computational Intelligence (SSCI)

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In this work, we report the preliminary analysis of the electrophysiological behavior of in vitro neuronal networks to identify when the networks are in a critical state based on the size distribution of network-wide avalanches of activity. The results presented here demonstrate the importance of selecting appropriate parameters in the evaluation of the size distribution and indicate that it is possible to perturb networks showing highly synchronized-or supercritical-behavior into the critical state by increasing the level of inhibition in the network. The classification of critical versus non-critical networks is valuable in identifying networks that can be expected to perform well on computational tasks, as criticality is widely considered to be the state in which a system is best suited for computation. This type of analysis is expected to enable the identification of networks that are well-suited for computation and the classification of networks as perturbed or healthy. This study is part of a larger research project, the overarching aim of which is to develop computational models that are able to reproduce target behaviors observed in in vitro neuronal networks. These models will ultimately be used to aid in the realization of these behaviors in nanomagnet arrays to be used in novel computing hardwares.

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“…These could enable simultaneous waveguiding and data processing through time-dependent spin-wave channel reconfiguration, enabling e.g. new schemes for computing 19,47 using artificial spin ices.…”

Section: Discussionmentioning

confidence: 99%

Tailoring Spin-Wave Channels in a Reconfigurable Artificial Spin Ice

2020

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Artificial spin ices are ensembles of geometrically-arranged, interacting nanomagnets which have shown promising potential for the realization of reconfigurable magnonic crystals. Such systems allow for manipulating spin waves on the nanoscale and potentially using them as information carriers. However, there are two general obstacles to the realization of spin ice-based magnonic crystals: the magnetic state of spin ices is difficult to reconfigure and the magnetostatic interactions between the islands are often weak, preventing mode coupling. We demonstrate, using micromagnetic modeling, that coupling a reconfigurable spin ice geometry made of weakly interacting nanomagnets to a soft magnetic underlayer creates a complex system exhibiting strongly coupled modes. These give rise to spin wave channels, which can be reconfigured by the magnetic state of the spin ice. These findings open the door to the realization of reconfigurable magnonic crystals with potential applications for data transport and processing in magnonic-based logic architectures.

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Computation in artificial spin ice

Cited by 48 publications

References 0 publications

Suppression of Dynamically Induced Stochastic Magnetic Behavior Through Materials Engineering

Suppression of Dynamically Induced Stochastic Magnetic Behavior Through Materials Engineering

Assessment and manipulation of the computational capacity of in vitro neuronal networks through criticality in neuronal avalanches

Tailoring Spin-Wave Channels in a Reconfigurable Artificial Spin Ice

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