标题： 三元随机神经元——使用单个应变磁致伸缩纳米磁铁实现 显示英文标题

标题： Ternary Stochastic Neuron -- Implemented with a Single Strained Magnetostrictive Nanomagnet

摘要： 随机神经元是解决一大类问题的高效硬件，通常有两种类型——“二进制”类型中，神经元状态在-1和+1之间随机变化，“模拟”类型中，神经元状态可以在-1到+1之间随机取任意值。 两者在神经形态计算中都有其用途，并且都可以用低能垒或零能垒的纳米磁体实现，这些纳米磁体在热噪声存在下的随机磁化方向编码了二进制或模拟状态变量。 在这两种类型之间的是n元随机神经元，主要是三元随机神经元（TSN），其状态随机地取三个值中的一个（-1，0，+1），它们已被证明在模式分类任务中很有效，例如从MNIST数据集中识别手写数字或从CIFAR-10数据集中识别模式。 在这里，我们展示如何通过施加单轴应变的零能垒（形状各向同性）磁致伸缩纳米磁体来实现TSN。 显示英文摘要

摘要： Stochastic neurons are extremely efficient hardware for solving a large class of problems and usually come in two varieties -- "binary" where the neuronal statevaries randomly between two values of -1, +1 and "analog" where the neuronal state can randomly assume any value between -1 and +1. Both have their uses in neuromorphic computing and both can be implemented with low- or zero-energy-barrier nanomagnets whose random magnetization orientations in the presence of thermal noise encode the binary or analog state variables. In between these two classes is n-ary stochastic neurons, mainly ternary stochastic neurons (TSN) whose state randomly assumes one of three values (-1, 0, +1), which have proved to be efficient in pattern classification tasks such as recognizing handwritten digits from the MNIST data set or patterns from the CIFAR-10 data set. Here, we show how to implement a TSN with a zero-energy-barrier (shape isotropic) magnetostrictive nanomagnet subjected to uniaxial strain.