Hardware in Loop Learning with Spin Stochastic Neurons
Despite the promise of superior efficiency and scalability, real‐world deployment of emerging nanoelectronic platforms for brain‐inspired computing have been limited thus far, primarily because of inter‐device variations and intrinsic non‐idealities. In this work, mitigation of these issues is demonstrated by performing learning directly on practical devices through a hardware‐in‐loop approach, utilizing stochastic neurons based on heavy metal/ferromagnetic spin–orbit torque heterostructures. The probabilistic switching and device‐to‐device variability of the fabricated devices of various sizes is characterized to showcase the effect of device dimension on the neuronal dynamics and its consequent impact on network‐level performance. The efficacy of the hardware‐in‐loop scheme is illustrated in a deep learning scenario achieving equivalent software performance. This work paves the way for future large‐scale implementations of neuromorphic hardware and realization of truly autonomous edge‐intelligent devices.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Hardware in Loop Learning with Spin Stochastic Neurons ; day:03 ; month:04 ; year:2024 ; extent:9
Advanced intelligent systems ; (03.04.2024) (gesamt 9)
- Urheber
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Islam, A. N. M. Nafiul
Yang, Kezhou
Shukla, Amit Kumar
Khanal, Pravin
Zhou, Bowei
Wang, Wei‐Gang
Sengupta, Abhronil
- DOI
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10.1002/aisy.202300805
- URN
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urn:nbn:de:101:1-2024040414342025099780
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:47 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Islam, A. N. M. Nafiul
- Yang, Kezhou
- Shukla, Amit Kumar
- Khanal, Pravin
- Zhou, Bowei
- Wang, Wei‐Gang
- Sengupta, Abhronil
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