The deep multichannel discrete-time cellular neural network model for classification


Abtioğlu E., Yalçın M. E.

INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, vol.50, no.11, pp.4171-4178, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 50 Issue: 11
  • Publication Date: 2022
  • Doi Number: 10.1002/cta.3401
  • Journal Name: INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.4171-4178
  • Keywords: cellular neural network, convolutional neural network, deep learning, image processing
  • Istanbul Technical University Affiliated: Yes

Abstract

High latency and power consumption are two major problems that need to be addressed in convolutional neural networks (CNN). In this paper, the convolutional layer is replaced with a discrete-time cellular neural network (CellNN) to overcome these problems. Multiple configurations of CellNNs are trained in a framework called TensorFlow to classify objects from the CIFAR-10 database. Effects of the number of iterations, the number of channels, batch normalization, and activation functions on the classification accuracies are presented. It is shown that TensorFlow is a tool that is capable of training discrete-time CellNNs. Although the accuracies of the proposed networks on CIFAR-10 are slightly lesser than the existing CNNs, with reduced parameters and multiply-accumulates (MACs), power consumption and computation time of our networks will be less than CNNs.