畴壁磁隧道结人工神经元任务适应计算中的可控复位行为
Controllable reset behavior in domain wall-magnetic tunnel junction artificial neurons for task-adaptable computation
摘要：
由于CMOS驱动的冯·诺依曼计算的局限性，使用自旋电子器件的神经形态计算已经引起了人们的兴趣。畴壁磁性隧道结（DW-MTJ）器件已被证明能够固有地捕获生物神经元行为。边缘松弛行为，其中频繁激发的神经元经历较低的动作电位阈值，可以在执行重复任务时提供附加的人工神经元功能。在这项研究中，我们证明了这种行为可以通过三种替代机制：形状各向异性，磁场，和电流驱动的软复位在DW-MTJ人工神经元中实现。使用微磁学和分析设备建模来对Optdigits手写数字数据集进行分类，我们表明边缘放松行为提高了有序数据集的分类准确性和分类率，同时牺牲了随机数据集的很少或没有准确性。这项工作建立了人工自旋电子神经元可以灵活地适应数据集的方法。
英文摘要：
Neuromorphic computing with spintronic devices has been of interest due to the limitations of CMOS-driven von Neumann computing. Domain wall-magnetic tunnel junction (DW-MTJ) devices have been shown to be able to intrinsically capture biological neuron behavior. Edgy-relaxed behavior, where a frequently firing neuron experiences a lower action potential threshold, may provide additional artificial neuronal functionality when executing repeated tasks. In this study, we demonstrate that this behavior can be implemented in DW-MTJ artificial neurons via three alternative mechanisms: shape anisotropy, magnetic field, and current-driven soft reset. Using micromagnetics and analytical device modeling to classify the Optdigits handwritten digit dataset, we show that edgy-relaxed behavior improves both classification accuracy and classification rate for ordered datasets while sacrificing little to no accuracy for a randomized dataset. This work establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.
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