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 language | English |
|---|---|
| Title of host publication | Satellite Data Compression, Communications, and Processing IX |
| Publisher | SPIE |
| Volume | 8871 |
| ISBN (Print) | 9780819497215 |
| DOIs | |
| Publication status | Published - 2013 Jan 1 |
| Event | Satellite Data Compression, Communications, and Processing IX - San Diego, CA, United States Duration: 2013 Aug 26 → 2013 Aug 27 |
Other
| Other | Satellite Data Compression, Communications, and Processing IX |
|---|---|
| Country | United States |
| City | San Diego, CA |
| Period | 13/8/26 → 13/8/27 |
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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
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Fast implementation of neural network classification. / Seo, Guiwon; Ok, Jiheon; Lee, Chul Hee.
Satellite Data Compression, Communications, and Processing IX. Vol. 8871 SPIE, 2013. 887107.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Fast implementation of neural network classification
AU - Seo, Guiwon
AU - Ok, Jiheon
AU - Lee, Chul Hee
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84887934901&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887934901&partnerID=8YFLogxK
U2 - 10.1117/12.2026666
DO - 10.1117/12.2026666
M3 - Conference contribution
AN - SCOPUS:84887934901
SN - 9780819497215
VL - 8871
BT - Satellite Data Compression, Communications, and Processing IX
PB - SPIE
ER -