Stochastic Spin‐Orbit‐Torque Synapse and its Application in Uncertainty Quantification
Abstract: Stochasticity plays a significant role in the low‐power operation of a biological neural network. In an artificial neural network, stochasticity also contributes to critical functions such as the uncertainty quantification (UQ) for estimating the probability for the correctness of prediction. This UQ is vital for cutting‐edge applications, including medical diagnostics, autopilots, and large language models. Thanks to nonlinear variation of analogous Hall resistance with high computing velocity and low dissipation, a stochastic spin‐orbit‐torque (SOT) device exhibits significant potential for implementing the UQ. However, up until now, the application of UQ for stochastic SOT devices remains unexplored. In this study, based on SOT‐induced stochastic magnetic domain wall (DW) motion with varying velocity, a SOT synapse is fabricated that can emulate stochastic weight update following the Spike‐Timing‐Dependent‐Plasticity (STDP) rule. Furthermore, a stochastic SNN is set up, which, when compared to its deterministic counterpart, demonstrates a clear advantage in quantifying uncertainty for diagnosing the type of breast tumor (benign or malignant).
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Stochastic Spin‐Orbit‐Torque Synapse and its Application in Uncertainty Quantification ; day:05 ; month:02 ; year:2024 ; extent:8
Advanced electronic materials ; (05.02.2024) (gesamt 8)
- Creator
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Wang, Cen
Zeng, Guang
Wen, Xinyu
He, Yuhui
Luo, Wei
Chen, Shiwei
Yang, Xiaofei
Liang, Shiheng
Zhang, Yue
- DOI
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10.1002/aelm.202300805
- URN
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urn:nbn:de:101:1-2024020614222610301711
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:38 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Wang, Cen
- Zeng, Guang
- Wen, Xinyu
- He, Yuhui
- Luo, Wei
- Chen, Shiwei
- Yang, Xiaofei
- Liang, Shiheng
- Zhang, Yue
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