NDLI: An Analysis on the Architecture and the Size of Quantized Hardware Neural Networks Based on Memristors

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An Analysis on the Architecture and the Size of Quantized Hardware Neural Networks Based on Memristors

Content Provider	MDPI
Author	Romero-Zaliz, Rocio Cantudo, Antonio Perez, Eduardo Jimenez-Molinos, Francisco Wenger, Christian Roldan, Juan Bautista
Copyright Year	2021
Description	We have performed different simulation experiments in relation to hardware neural networks (NN) to analyze the role of the number of synapses for different NN architectures in the network accuracy, considering different datasets. A technology that stands upon 4-kbit 1T1R ReRAM arrays, where resistive switching devices based on $HfO2$ dielectrics are employed, is taken as a reference. In our study, fully dense (FdNN) and convolutional neural networks (CNN) were considered, where the NN size in terms of the number of synapses and of hidden layer neurons were varied. CNNs work better when the number of synapses to be used is limited. If quantized synaptic weights are included, we observed that NN accuracy decreases significantly as the number of synapses is reduced; in this respect, a trade-off between the number of synapses and the NN accuracy has to be achieved. Consequently, the CNN architecture must be carefully designed; in particular, it was noticed that different datasets need specific architectures according to their complexity to achieve good results. It was shown that due to the number of variables that can be changed in the optimization of a NN hardware implementation, a specific solution has to be worked in each case in terms of synaptic weight levels, NN architecture, etc.
Starting Page	3141
e-ISSN	20799292
DOI	10.3390/electronics10243141
Journal	Electronics
Issue Number	24
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2021-12-17
Access Restriction	Open
Subject Keyword	Electronics Hardware and Architecturee Memristor Multilevel Operation Hardware Neural Network Deep Neural Network Convolutional Neural Network Network Architecture Synaptic Weight
Content Type	Text
Resource Type	Article

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