Neuromorphic computation with a single magnetic domain wall.
Neuromorphic computation with a single magnetic domain wall.
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DOI：
10.1038/s41598-021-94975-y
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发表时间：
2021-08-02
影响因子：
4.6
通讯作者：
Hayward TJ
中科院分区：
文献类型：
--
作者：
Ababei RV;Ellis MOA;Vidamour IT;Devadasan DS;Allwood DA;Vasilaki E;Hayward TJ
机器学习技术通常用于对复杂关系进行建模，但由于传统计算机架构与它们需要模拟的算法结构之间的匹配较差，因此在数字硬件上的实现相对低效。神经形态设备，特别是水库计算架构，利用物理系统的固有属性来实现机器学习算法，因此有可能更有效。在这项工作中，我们证明了磁性纳米线中的各个畴壁的动态适合于在硬件中实现水库计算范例。我们使用一维集体坐标模型和微磁模拟，模拟了镍纳米线中两个反缺口之间的畴壁的动力学。当由振荡磁场驱动时，该域在由反凹口产生的势阱内表现出非线性动力学，该反凹口类似于Duffing振子。我们通过调制所施加磁场的幅度来将时间复用输入信号注入储层，从而利用畴壁动力学进行储层计算，并展示了这如何使我们能够执行机器学习任务，包括：（1）正弦波和方波的分类;（2）口语数字;（3）非时间二维玩具数据和手写数字。我们的工作为创建纳米级神经形态设备奠定了基础，在这些设备中，单个磁畴壁用于执行复杂的数据分析任务。
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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