Spontaneous sparse learning for PCM-based memristor neural networks

Spontaneous sparse learning for PCM-based memristor neural networks

Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors present 1 Gb phase change memory memristor array with a spontaneous sparse learning scheme able to leverage the resistance drift issue improving the classification accuracy o...

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Bibliographic Details
Main Authors: Dong-Hyeok Lim, Shuang Wu, Rong Zhao, Jung-Hoon Lee, Hongsik Jeong, Luping Shi
Format: Article
Language:English
Published: Nature Publishing Group 2021-01-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-020-20519-z
Description
Summary:Designing energy efficient and scalable artificial networks for neuromorphic computing remains a challenge. Here, the authors present 1 Gb phase change memory memristor array with a spontaneous sparse learning scheme able to leverage the resistance drift issue improving the classification accuracy on MNIST handwritten digit dataset.
ISSN:2041-1723

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