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Modelling in-device inference and classification of binary digits using nonlinear dynamics of spin Hall Oscillator
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Type
Conference paper
Citation
Mohan, J. R., Yamanaka, C., Feng, R., Mathew, A. J., Nakamura, Y., Medwal, R., Gupta, S., Rawat, R. S., & Fukuma, Y. (2023). Modelling in-device inference and classification of binary digits using nonlinear dynamics of spin Hall Oscillator. 2023 IEEE International Magnetic Conference - Short Papers (INTERMAG Short Papers). https://doi.org/10.1109/intermagshortpapers58606.2023.10228745
Author
Mohan, John Rex
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Yamanaka, Chisato
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Feng, Ryoyan
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Mathew, Arun Jacob
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Nakamura, Yoji
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Medwal, Rohit
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Gupta, Surbhi
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Fukuma, Yosuhiro
Abstract
In this work, we employ micromagnetic modelling of a spin Hall oscillator for a direct inference and classification of binary digit inputs. The spectral characteristics of the oscillation is utilized for the classification. We observed a direct inference of binary digit inputs up to a sequence of four binary digits. Subsequently, handwritten digit image recognition is tested with the Modified National Institute of Standards and Testing (MNIST) handwritten digit database and acquired an accuracy of 88.6% with a linear classifier network.
Date Issued
2023