Novel Analog Implementation of a Hyperbolic Tangent Neuron in Artificial Neural Networks

Fatemeh Mohammadi Shakiba, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Recently, enormous datasets have made power dissipation and area usage lie at the heart of designs for artificial neural networks (ANNs). Considering the significant role of activation functions in neurons and the growth of hardware-based neural networks like memristive neural networks, this work proposes a novel design for a hyperbolic tangent activation function (Tanh) to be used in memristive-based neuromorphic architectures. The purpose of implementing a CMOS-based design for Tanh is to decrease power dissipation and area usage. This design also increases the overall speed of computation in ANNs, while keeping the accuracy in an acceptable range. The proposed design is one of the first analog designs for the hyperbolic tangent and its performance is analyzed by using two well-known datsets, including the Modified National Institute of Standards and Technology (MNIST) and Fashion-MNIST. The direct implementation of the proposed design for Tanh is proposed and investigated via software and hardware modeling.

Original languageEnglish (US)
Article number9248592
Pages (from-to)10856-10867
Number of pages12
JournalIEEE Transactions on Industrial Electronics
Volume68
Issue number11
DOIs
StateAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Artificial neural network (ANN)
  • activation function
  • hardware implementation
  • hyperbolic tangent
  • machine learning
  • memristive neural network (MNN)
  • neuromorphic architecture

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