Signal and noise extraction from analog memory elements for neuromorphic computing
The application of resistive and phase-change memories in neuromorphic computation will require efficient methods to quantify device-to-device and switching variability. Here, the authors assess the impact of a broad range of device switching mechanisms using machine learning regression techniques.
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Main Authors: | N. Gong, T. Idé, S. Kim, I. Boybat, A. Sebastian, V. Narayanan, T. Ando |
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Format: | article |
Language: | EN |
Published: |
Nature Portfolio
2018
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Subjects: | |
Online Access: | https://doaj.org/article/a1f3b1e3f21e4dcd812626edb73f3983 |
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