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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Hlavní autoři: 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
Médium: Artigo
Jazyk:Inglês
Vydáno: Wiley 2020-02-01
Edice:Advanced Intelligent Systems
Témata:
convolutional neural networks
hafnium oxide
kernels
self-rectifying resistive switching random access memory
stacked crossbar arrays
On-line přístup:https://doi.org/10.1002/aisy.201900116
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