论文标题
用于神经形态应用的基于Skyrmion的泄漏集成和消防神经元
Skyrmion-based Leaky Integrate and Fire Neurons for Neuromorphic Applications
论文作者
论文摘要
SpinTronics是用于数据存储和计算的重要新兴技术。在这一领域，由于磁性天空的尺寸和能量消耗较小，因此基于Skyrmion的设备具有吸引力。但是，控制天空的创造，删除和运动是具有挑战性的。在这里，我们提出了一种新型的基于Skyrmion的设备结构，并证明了其用作神经形态计算的泄漏整合（LIF）和FIRE神经元的用途。在这里，我们表明，可以通过在磁性隧道连接处（MTJ）中对自由层的几何形状进行限制，并证明可以通过施加脉冲电压应力来调整天空的大小。由此类基于天际的LIF神经元制成的尖峰神经网络（SNN）显示了从改良国家标准技术研究所（MNIST）数据集中对图像进行分类的能力。
Spintronics is an important emerging technology for data storage and computation. In this field, magnetic skyrmion-based devices are attractive due to their small size and energy consumption. However, controlling the creation, deletion and motion of skyrmions is challenging. Here we propose a novel energy-efficient skyrmion-based device structure, and demonstrate its use as leaky integrate (LIF) and fire neuron for neuromorphic computing. Here we show that skyrmions can be confined by patterning the geometry of the free layer in a magnetic tunnel junction (MTJ), and demonstrate that the size of the skyrmion can be adjusted by applying pulsed voltage stresses. A spiking neural network (SNN) made of such skyrmion-based LIF neurons shows the capability of classifying images from the Modified National Institute of Standards and Technology (MNIST) dataset.
