Grollier J, Querlioz D, Camsari K Y, Everschor-Sitte K, Fukami S, Stiles M D
Unité Mixte de Physique CNRS, Thales, Univ. Paris-Sud, Université Paris-Saclay, 91767 Palaiseau, France.
Centre de Nanosciences et de Nanotechnologies, Univ. Paris-Sud, CNRS, Université Paris-Saclay, 91405 Orsay, France.
Nat Electron. 2020;3(7). doi: 10.1038/s41928-019-0360-9.
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.
神经形态计算利用受大脑启发的基本原理来设计电路，这些电路能够以卓越的能源效率执行人工智能任务。传统方法一直受到传统电子设备实现的人工神经元和突触的能量面积的限制。近年来，多个研究团队已经证明，利用电子的磁特性和电特性的自旋电子纳米器件可以提高这些电路的能源效率并减小其面积。在已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已确立的兼容性及其多功能性而发挥着突出作用。磁隧道结可以充当突触，存储连接权重，用作本地非易失性数字存储器或作为连续变化的电阻。作为纳米振荡器，它们可以充当神经元，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。诸如畴壁或斯格明子之类的磁纹理可以通过其非线性动力学配置为充当神经元。自旋电子器件的几种神经形态计算实现方式在这方面展现出了潜力。用作可变电阻突触时，磁隧道结在关联存储器中执行模式识别。作为振荡器，它们在储层计算中执行语音数字识别，并且当耦合在一起时执行信号分类。作为超顺磁体，它们执行群体编码和概率计算。模拟表明，纳米磁体阵列和斯格明子薄膜可以作为神经形态计算机的组件运行。虽然这些例子显示了自旋电子学在该领域的独特潜力，但在扩大规模方面仍存在若干挑战，包括器件之间耦合的效率以及单个器件中最大电阻与最小电阻的相对较低比率。
