Optimization of the structural complexity of artificial neural network for hardware-driven neuromorphic computing application

Kannan Udaya Mohanan, Seongjae Cho, Byung Gook Park

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

This work focuses on the optimization of the structural complexity of a single-layer feedforward neural network (SLFN) for neuromorphic hardware implementation. The singular value decomposition (SVD) method is used for the determination of the effective number of neurons in the hidden layer for Modified National Institute of Standards and Technology (MNIST) dataset classification. The proposed method is also verified on a SLFN using weights derived from a synaptic transistor device. The effectiveness of this methodology in estimating the reduced number of neurons in the hidden layer makes this method highly useful in optimizing complex neural network architectures for their hardware realization.

Original languageEnglish
Pages (from-to)6288-6306
Number of pages19
JournalApplied Intelligence
Volume53
Issue number6
DOIs
StatePublished - Mar 2023

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

Keywords

  • Hardware neuromorphic systems
  • Hidden layer
  • Neural networks
  • Neuron circuits
  • Pattern recognition
  • Singular value decomposition (SVD)
  • Synaptic device

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