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Influence of variability on the performance of HfO2 memristor-based convolutional neural networks

  • A study of convolutional neural networks (CNNs) was performed to analyze the influence of quantization and variability in the network synaptic weights. Different CNNs were considered accounting for the number of convolutional layers, size of the filters in the convolutional layer, number of neurons in the final network layers and different sets of quantization levels. The conductance levels of fabricated 1T1R structures based on HfO2 memristors were considered as reference for four or eight level quantization processes at the inference stage of the CNNs, which were previous trained with the MNIST dataset. We also included the variability of the experimental conductance levels that was found to be Gaussian distributed and was correspondingly modeled for the synaptic weight implementation.

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Author: Rocío Romero-Zaliz, Eduardo Perez, Francisco Jimenez-Molinos, Christian WengerORCiD, Juan Bautista Roldan
DOI:https://doi.org/10.1016/j.sse.2021.108064
ISSN:0038-1101
Title of the source (English):Solid State Electronics
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2021
Tag:HfO2; RRAM; memristive switching; neural network
Volume/Year:185
Number of pages:5
Article number:108064
Faculty/Chair:Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Halbleitermaterialien
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