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.

中文翻译：

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用于任务自适应计算的域壁-磁隧道结人工神经元中的可控重置行为

由于CMOS驱动的冯·诺伊曼（von Neumann）计算的局限性，使用自旋电子设备进行神经形态计算已引起关注。域壁-磁性隧道结（DW-MTJ）设备已被证明能够固有地捕获生物神经元行为。在执行重复任务时，频繁触发的神经元经历较低的动作电位阈值的前卫放松行为可能会提供其他人工神经元功能。在这项研究中，我们证明可以通过三种替代机制在DW-MTJ人工神经元中实现此行为：形状各向异性，磁场和电流驱动的软复位。使用微磁学和分析设备建模对Optdigits手写数字数据集进行分类，我们表明，前卫松弛行为提高了有序数据集的分类准确性和分类率，同时又为随机数据集牺牲了很少甚至没有准确性。这项工作建立了方法，可以使人工自旋电子神经元灵活地适应数据集。