Stochastic domain wall-magnetic tunnel junction artificial neurons for noise-resilient spiking neural networks

被引：8

作者：

Leonard, Thomas ^{[1
]}

Liu, Samuel ^{[1
]}

Jin, Harrison ^{[1
]}

Incorvia, Jean Anne C. ^{[1
]}

机构：

[1] Univ Texas Austin, Elect & Comp Engn, Austin, TX 78712 USA

来源：

APPLIED PHYSICS LETTERS | 2023年 / 122卷 / 26期

基金：

美国国家科学基金会;

关键词：

Domain walls - Gaussian noise (electronic) - Magnetic devices - Magnetic domains - Neural networks - Neurons - Stochastic systems - Tunnel junctions;

D O I：

10.1063/5.0152211

中图分类号：

O59 [应用物理学];

学科分类号：

摘要：

The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) makes 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 DW-MTJ device. The validation accuracy during training was also shown to be comparable to an ideal leaky integrate and fire 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.

引用

页数：6