Potential implementation of reservoir computing models based on magnetic skyrmions
Potential implementation of reservoir computing models based on magnetic skyrmions
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DOI：
10.1063/1.5006918
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发表时间：
2018-05-01
期刊：
影响因子：
1.6
通讯作者：
Everschor-Sitte, Karin
中科院分区：
文献类型：
--
作者：
Bourianoff, George;Pinna, Daniele;Everschor-Sitte, Karin
水库计算是一种递归神经网络，通常用于识别和预测时空事件，依赖于嵌套反馈回路的复杂层次来产生记忆功能。油藏计算范式不需要任何油藏拓扑或节点权重的知识来进行训练，因此可以利用由各种物理过程形成的自然存在的网络。在此之前，实现储层计算的大多数努力都集中在利用忆阻器技术实现递归神经网络上。本文探讨了磁性离子结构的潜力和在其中形成的复杂电流模式，作为储层计算的一个有吸引力的物理实例。我们认为，各向异性磁阻和自旋扭矩效应导致的非线性动力学相互作用允许对时空事件进行有效和节能的非线性处理，以实现事件识别和预测。(C) 2018作者。
Reservoir Computing is a type of recursive neural network commonly used for recognizing and predicting spatio-temporal events relying on a complex hierarchy of nested feedback loops to generate a memory functionality. The Reservoir Computing paradigm does not require any knowledge of the reservoir topology or node weights for training purposes and can therefore utilize naturally existing networks formed by a wide variety of physical processes. Most efforts to implement reservoir computing prior to this have focused on utilizing memristor techniques to implement recursive neural networks. This paper examines the potential of magnetic skyrmion fabrics and the complex current patterns which form in them as an attractive physical instantiation for Reservoir Computing. We argue that their nonlinear dynamical interplay resulting from anisotropic magnetoresistance and spin-torque effects allows for an effective and energy efficient nonlinear processing of spatial temporal events with the aim of event recognition and prediction. (C) 2018 Author(s).