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.
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Stochastic domain wall-magnetic tunnel junction artificial neurons for noise-resilient spiking neural networks
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26 June 2023
Research Article|
June 27 2023
Stochastic domain wall-magnetic tunnel junction artificial neurons for noise-resilient spiking neural networks
Special Collection:
2023 Rising Stars Collection
Thomas Leonard
;
Thomas Leonard
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing)
Electrical and Computer Engineering, University of Texas at Austin
, Austin, Texas 78712, USA
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Samuel Liu
;
Samuel Liu
(Conceptualization, Data curation, Formal analysis, Software, Visualization, Writing – original draft, Writing – review & editing)
Electrical and Computer Engineering, University of Texas at Austin
, Austin, Texas 78712, USA
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Harrison Jin
;
Harrison Jin
(Investigation, Methodology, Software)
Electrical and Computer Engineering, University of Texas at Austin
, Austin, Texas 78712, USA
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Jean Anne C. Incorvia
Jean Anne C. Incorvia
a)
(Investigation, Supervision, Writing – original draft, Writing – review & editing)
Electrical and Computer Engineering, University of Texas at Austin
, Austin, Texas 78712, USA
a)Author to whom correspondence should be addressed: incorvia@austin.utexas.edu
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a)Author to whom correspondence should be addressed: incorvia@austin.utexas.edu
Appl. Phys. Lett. 122, 262406 (2023)
Article history
Received:
March 28 2023
Accepted:
June 10 2023
Citation
Thomas Leonard, Samuel Liu, Harrison Jin, Jean Anne C. Incorvia; Stochastic domain wall-magnetic tunnel junction artificial neurons for noise-resilient spiking neural networks. Appl. Phys. Lett. 26 June 2023; 122 (26): 262406. https://doi.org/10.1063/5.0152211
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