Fast implementation of neural network classification

Guiwon Seo, Jiheon Ok, Chulhee Lee

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

Abstract

Most artificial neural networks use a nonlinear activation function that includes sigmoid and hyperbolic tangent functions. Most artificial networks employ nonlinear functions such as these sigmoid and hyperbolic tangent functions, which incur high complexity costs, particularly during hardware implementation. In this paper, we propose new polynomial approximation methods for nonlinear activation functions that can substantially reduce complexity without sacrificing performance. The proposed approximation methods were applied to pattern classification problems. Experimental results show that the processing time was reduced by up to 50% without any performance degradations in terms of computer simulation.

Original languageEnglish
Title of host publicationSatellite Data Compression, Communications, and Processing IX
PublisherSPIE
ISBN (Print)9780819497215
DOIs
Publication statusPublished - 2013 Jan 1
EventSatellite Data Compression, Communications, and Processing IX - San Diego, CA, United States
Duration: 2013 Aug 262013 Aug 27

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8871
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

OtherSatellite Data Compression, Communications, and Processing IX
CountryUnited States
CitySan Diego, CA
Period13/8/2613/8/27

Fingerprint

Nonlinear Function
Tangent function
Hyperbolic tangent
Hyperbolic functions
Hyperbolic function
Activation Function
Neural Networks
Neural networks
Approximation Methods
Chemical activation
Nonlinear networks
Polynomial approximation
Polynomial Methods
tangents
Pattern Classification
Hardware Implementation
Polynomial Approximation
Classification Problems
Pattern recognition
Artificial Neural Network

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Seo, G., Ok, J., & Lee, C. (2013). Fast implementation of neural network classification. In Satellite Data Compression, Communications, and Processing IX [887107] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8871). SPIE. https://doi.org/10.1117/12.2026666
Seo, Guiwon ; Ok, Jiheon ; Lee, Chulhee. / Fast implementation of neural network classification. Satellite Data Compression, Communications, and Processing IX. SPIE, 2013. (Proceedings of SPIE - The International Society for Optical Engineering).
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Seo, G, Ok, J & Lee, C 2013, Fast implementation of neural network classification. in Satellite Data Compression, Communications, and Processing IX., 887107, Proceedings of SPIE - The International Society for Optical Engineering, vol. 8871, SPIE, Satellite Data Compression, Communications, and Processing IX, San Diego, CA, United States, 13/8/26. https://doi.org/10.1117/12.2026666

Fast implementation of neural network classification. / Seo, Guiwon; Ok, Jiheon; Lee, Chulhee.

Satellite Data Compression, Communications, and Processing IX. SPIE, 2013. 887107 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 8871).

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

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Seo G, Ok J, Lee C. Fast implementation of neural network classification. In Satellite Data Compression, Communications, and Processing IX. SPIE. 2013. 887107. (Proceedings of SPIE - The International Society for Optical Engineering). https://doi.org/10.1117/12.2026666