Perspective on unconventional computing using magnetic skyrmions
Perspective on unconventional computing using magnetic skyrmions
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DOI：
10.1063/5.0148469
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
O. Lee;Robin Msiska;M. Brems;M. Kläui;H. Kurebayashi;K. Everschor-Sitte
中科院分区：
文献类型：
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作者：
O. Lee;Robin Msiska;M. Brems;M. Kläui;H. Kurebayashi;K. Everschor-Sitte
学习和模式识别不可避免地需要对先前事件的记忆，这是传统CMOS硬件需要人工模拟的功能。动态系统自然地提供了大量不同的非传统计算方法所需的内存，复杂性和非线性。在这篇透视文章中，我们专注于油藏计算的非常规计算概念，并概述了所报道的关键物理油藏工作。我们专注于磁结构的有前途的平台，特别是skyrmions，这可能允许低功耗应用。此外，我们讨论了基于skyrmion的布朗计算，最近已与水库计算的实现。这种计算模式利用了许多skyrmion系统中存在的热波动。最后，我们提供了在这一领域的最重要的挑战的前景。
Learning and pattern recognition inevitably requires memory of previous events, a feature that conventional CMOS hardware needs to artificially simulate. Dynamical systems naturally provide the memory, complexity, and nonlinearity needed for a plethora of different unconventional computing approaches. In this perspective article, we focus on the unconventional computing concept of reservoir computing and provide an overview of key physical reservoir works reported. We focus on the promising platform of magnetic structures and, in particular, skyrmions, which potentially allow for low-power applications. Moreover, we discuss skyrmion-based implementations of Brownian computing, which has recently been combined with reservoir computing. This computing paradigm leverages the thermal fluctuations present in many skyrmion systems. Finally, we provide an outlook on the most important challenges in this field.