The deep multichannel discrete-time cellular neural network model for classification,International Journal of Circuit Theory and Applications

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The deep multichannel discrete-time cellular neural network model for classification
International Journal of Circuit Theory and Applications ( IF 1.8 ) Pub Date : 2022-08-05 , DOI: 10.1002/cta.3401
Emrah Abtioglu ₁ , Mustak Erhan Yalcin ₂

Affiliation

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.

中文翻译：

用于分类的深度多通道离散时间细胞神经网络模型

高延迟和功耗是卷积神经网络 (CNN) 需要解决的两个主要问题。在本文中，卷积层被替换为离散时间细胞神经网络（CellNN）来克服这些问题。CellNN 的多种配置在称为 TensorFlow 的框架中进行训练，以对 CIFAR-10 数据库中的对象进行分类。给出了迭代次数、通道数、批量归一化和激活函数对分类精度的影响。结果表明，TensorFlow 是一种能够训练离散时间 CellNNs 的工具。尽管在 CIFAR-10 上提出的网络的精度略低于现有的 CNN，但参数减少和乘法累加（MAC），

更新日期：2022-08-05

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