High-Accuracy Deep Belief Network: Fuzzy Neural Networks Using MRAM-Based Stochastic Neurons
Two-terminal magnetoresistive random access memory (MRAM) devices provide a recent approach to intrinsically realizing stochastic neuronal behavior in cognitive architectures such as restricted Boltzmann machines (RBMs) for deep belief networks (DBNs). MRAM-based DBNs have achieved substantial energ...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
IEEE
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/da222e16512b49659d0eb142e2eb88d5 |
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| Sumario: | Two-terminal magnetoresistive random access memory (MRAM) devices provide a recent approach to intrinsically realizing stochastic neuronal behavior in cognitive architectures such as restricted Boltzmann machines (RBMs) for deep belief networks (DBNs). MRAM-based DBNs have achieved substantial energy and area improvements in comparison with the prior DBN hardware implementations. However, MRAM-based DBNs suffer from low accuracy in image classification. In order to resolve this problem, we present a new DBN-fuzzy system based on the combination of MRAM-based DBNs and fuzzy systems in the interest of improving the accuracy of MRAM-based neural networks. First, the MRAM-based DBN is employed to identify the top recognition results with the highest probability. Second, the fuzzy system is utilized to obtain the top-1 recognition results. We assess the accuracy of neural networks on the MNIST dataset, finding that the top-1 accuracy of the DBN-fuzzy neural network is improved from 64% to 82% in comparison to the MRAM-based DBNs for a <inline-formula> <tex-math notation="LaTeX">$784\times10$ </tex-math></inline-formula> network. |
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