The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) make SNNs promising for edge applications that require high energy efficiency. To realize SNNs in hardware, spintronic neuron implementations can bring advantages of scalability and energy efficiency. Domain wall (DW) based magnetic tunnel junction (MTJ) devices are well suited for probabilistic neural networks given their intrinsic integrate-and-fire behavior with tunable stochasticity. Here, we present a scaled DW-MTJ neuron with voltage-dependent firing probability. The measured behavior was used to simulate a SNN that attains accuracy during learning compared to an equivalent, but more complicated, multi-weight (MW) DW-MTJ device. The validation accuracy during training was also shown to be comparable to an ideal leaky integrate and fire (LIF) device. However, during inference, the binary DW-MTJ neuron outperformed the other devices after gaussian noise was introduced to the Fashion-MNIST classification task. This work shows that DW-MTJ devices can be used to construct noise-resilient networks suitable for neuromorphic computing on the edge.

翻译：脉冲神经网络（SNN）中神经元行为的时空特性使其在需要高能效的边缘计算应用中具有潜力。为实现SNN的硬件化，自旋电子神经元实现可在可扩展性和能效方面带来优势。基于畴壁（DW）的磁隧道结（MTJ）器件因其固有的可调随机性整合-发放行为，特别适用于概率神经网络。本文提出一种具有电压依赖发放概率的缩放型DW-MTJ神经元。利用实测行为模拟了SNN，该网络在学习过程中达到了与等效但更复杂的多权重（MW）DW-MTJ器件相当的精度。训练过程中的验证精度也显示出与理想漏积分-发放（LIF）器件相媲美的性能。然而在推理阶段，当向Fashion-MNIST分类任务引入高斯噪声后，二值DW-MTJ神经元的性能优于其他器件。本研究表明，DW-MTJ器件可用于构建适用于边缘神经形态计算的抗噪声网络。