Neuromorphic Spintronics.
Neuromorphic Spintronics.
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DOI：
10.1038/s41928-019-0360-9
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发表时间：
2020
影响因子：
34.3
通讯作者：
Stiles MD
中科院分区：
文献类型：
--
作者：
Grollier J;Querlioz D;Camsari KY;Everschor-Sitte K;Fukami S;Stiles MD
神经形态计算利用受大脑启发的基本原理来设计电路，以卓越的能量效率执行人工智能任务。传统的方法受到传统电子设备实现的人工神经元和突触能量区域的限制。近年来，多个研究小组已经证明，利用电子的磁性和电学特性的自旋电子纳米器件可以提高能量效率并减少这些电路的面积。在已经使用的各种自旋电子器件中，磁隧道结因其与标准集成电路的兼容性和多功能性而起着突出的作用。磁隧道结可以用作突触，存储连接权重，作为本地非易失性数字存储器或连续变化的电阻。作为纳米振荡器，它们可以作为神经元，模拟生物神经元组的振荡行为。作为超顺磁体，它们可以通过模拟生物神经元的随机尖峰来做到这一点。磁纹理，如畴壁或skyrmions，可以通过其非线性动力学配置为神经元。用自旋电子设备实现的几种神经形态计算在这方面展示了它们的前景。作为可变电阻突触，磁隧道连接在联想记忆中执行模式识别。作为振荡器，它们在储层计算中执行语音数字识别，当耦合在一起时，执行信号分类。作为超顺磁体，它们执行种群编码和概率计算。仿真结果表明，纳米磁体阵列和skyrmicons薄膜可以作为神经形态计算机的组成部分。虽然这些例子显示了自旋电子学在该领域的独特前景，但要扩大规模还存在一些挑战，包括设备之间的耦合效率和单个设备中相对较低的最大与最小电阻比率。
Neuromorphic computing uses basic principles inspired by the brain to design circuits that perform artificial intelligence tasks with superior energy efficiency. Traditional approaches have been limited by the energy area of artificial neurons and synapses realized with conventional electronic devices. In recent years, multiple groups have demonstrated that spintronic nanodevices, which exploit the magnetic as well as electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits. Among the variety of spintronic devices that have been used, magnetic tunnel junctions play a prominent role because of their established compatibility with standard integrated circuits and their multifunctionality. Magnetic tunnel junctions can serve as synapses, storing connection weights, functioning as local, nonvolatile digital memory or as continuously varying resistances. As nano-oscillators, they can serve as neurons, emulating the oscillatory behavior of sets of biological neurons. As superparamagnets, they can do so by emulating the random spiking of biological neurons. Magnetic textures like domain walls or skyrmions can be configured to function as neurons through their non-linear dynamics. Several implementations of neuromorphic computing with spintronic devices demonstrate their promise in this context. Used as variable resistance synapses, magnetic tunnel junctions perform pattern recognition in an associative memory. As oscillators, they perform spoken digit recognition in reservoir computing and when coupled together, classification of signals. As superparamagnets, they perform population coding and probabilistic computing. Simulations demonstrate that arrays of nanomagnets and films of skyrmions can operate as components of neuromorphic computers. While these examples show the unique promise of spintronics in this field, there are several challenges to scaling up, including the efficiency of coupling between devices and the relatively low ratio of maximum to minimum resistances in the individual devices.
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影响因子：
19.6
作者：
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通讯作者：
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影响因子：
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4.9
作者：
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通讯作者：
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4
作者：
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