Kernel Application of the Stacked Crossbar Array Composed of Self‐Rectifying Resistive Switching Memory for Convolutional Neural Networks

Kernel Application of the Stacked Crossbar Array Composed of Self‐Rectifying Resistive Switching Memory for Convolutional Neural Networks

Herein, a feasible method is provided for circuit implementation of the convolutional neural network (CNN) in neuromorphic hardware using the multiple layers‐stacked resistance switching random access memory (ReRAM). The specific ReRAM is accompanied by self‐rectification functionality. The single‐i...

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Bibliographic Details
Main Authors: Yumin Kim, Jihun Kim, Seung Soo Kim, Young Jae Kwon, Gil Seop Kim, Jeong Woo Jeon, Dae Eun Kwon, Jung Ho Yoon, Cheol Seong Hwang
Format: Article
Language:English
Published: Wiley 2020-02-01
Series:Advanced Intelligent Systems
Subjects:
convolutional neural networks
hafnium oxide
kernels
self-rectifying resistive switching random access memory
stacked crossbar arrays
Online Access:https://doi.org/10.1002/aisy.201900116

Internet

https://doi.org/10.1002/aisy.201900116

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