期刊
JOURNAL OF APPLIED PHYSICS
卷 124, 期 15, 页码 -出版社
AIP Publishing
DOI: 10.1063/1.5042359
关键词
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资金
- French National Research Agency (ANR) as part of the Investissements d'Avenir program [ANR-10-LABX-0035]
- ANR MEMOS grant [ANR-14-CE26-0021]
- French Ministere de l'ecologie, du developpement durable et de l'energie
- Agence Nationale de la Recherche (ANR) [ANR-14-CE26-0021] Funding Source: Agence Nationale de la Recherche (ANR)
Pattern classification architectures leveraging the physics of coupled nano-oscillators have been demonstrated as promising alternative computing approaches but lack effective learning algorithms. In this work, we propose a nano-oscillator based classification architecture where the natural frequencies of the oscillators are learned linear combinations of the inputs and define an offline learning algorithm based on gradient back-propagation. Our results show significant classification improvements over a related approach with online learning. We also compare our architecture with a standard neural network on a simple machine learning case, which suggests that our approach is economical in terms of the number of adjustable parameters. The introduced architecture is also compatible with existing nano-technologies: the architecture does not require changes in the coupling between nano-oscillators, and it is tolerant to oscillator phase noise. Published by AIP Publishing.
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