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Multi-neuron connection using multi-terminal floating-gate memristor for unsupervised learning

Engineering and Technology

Multi-neuron connection using multi-terminal floating-gate memristor for unsupervised learning

U. Y. Won, Q. A. Vu, et al.

This groundbreaking research by Ui Yeon Won and colleagues reveals a multi-neuron connection using an innovative multi-terminal floating-gate memristor, enabling unprecedented efficiency in artificial neural networks. With an impressive accuracy of 83.08% achieved on the MNIST dataset, their work paves the way for energy-efficient and highly effective neuromorphic computing.... show more
Citation Metrics
Citations
69
Influential Citations
0
Reference Count
42
Citation by Year

Note: The citation metrics presented here have been sourced from Semantic Scholar and OpenAlex.

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