Controllable Reset Behavior in Domain Wall–Magnetic Tunnel Junction Artificial Neurons for Task-Adaptable Computation
Controllable Reset Behavior in Domain Wall–Magnetic Tunnel Junction Artificial Neurons for Task-Adaptable Computation
复制标题
用于任务适应性计算的磁畴壁磁隧道结人工神经元中的可控重置行为
DOI：
10.1109/lmag.2021.3069666
复制
发表时间：
2021
影响因子：
1.2
通讯作者：
Incorvia, Jean Anne
中科院分区：
文献类型：
--
作者：
Liu, Samuel;Bennett, Christopher;Friedman, Joseph;Marinella, Matthew;Paydarfar, David;Incorvia, Jean Anne
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。在这封信中，我们证明了这种行为可以通过三种替代机制在DW-MTJ人工神经元中实现：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。这封信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。
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 letter, 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 letter establishes methods by which artificial spintronic neurons can be flexibly adapted to datasets.
影响因子：
4
作者：
Alamdar, Mahshid;Leonard, Thomas;Incorvia, Jean Anne C.
通讯作者：
Incorvia, Jean Anne C.
影响因子：
3.1
作者：
Brigner, Wesley H.;Friedman, Joseph S.;Garcia-Sanchez, Felipe
通讯作者：
Garcia-Sanchez, Felipe