VLSI implementation of a neural network classifier based on the saturating linear activation function

Amine Bermak, A. Bouzerdoum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

This paper presents a digital VLSI implementation of a feedforward neural network classifier based on the saturating linear activation function. The architecture consists of one-hidden layer performing the weighted sum followed by a saturating linear activation function. The hardware implementation of such a network presents a significant advantage in terms of circuit complexity as compared to a network based on a sigmoid activation function, but without compromising the classification performance. Simulation results on two benchmark problems show that feedforward neural networks with the saturating linearity perform as well as networks with the sigmoid activation function. The architecture can also handle variable precision resulting in a higher computational resources at lower precision.

Original languageEnglish
Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages981-985
Number of pages5
Volume2
ISBN (Electronic)9810475241, 9789810475246
DOIs
Publication statusPublished - 2002
Externally publishedYes
Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
Duration: 18 Nov 200222 Nov 2002

Other

Other9th International Conference on Neural Information Processing, ICONIP 2002
CountrySingapore
CitySingapore
Period18/11/0222/11/02

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Bermak, A., & Bouzerdoum, A. (2002). VLSI implementation of a neural network classifier based on the saturating linear activation function. In ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age (Vol. 2, pp. 981-985). [1198207] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICONIP.2002.1198207