Paper Submission
| 3/2018 - 11 |
Implementation of High Speed Tangent Sigmoid Transfer Function Approximations for Artificial Neural Network Applications on FPGAKOYUNCU, I.   |
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Author keywords
real-time systems, field programmable gate arrays, artificial neural networks, approximation methods, transfer functions
References keywords
neural(15), fpga(9), chaotic(9), artificial(9), network(7), networks(6), implementation(6), koyuncu(5), hardware(4), computing(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-08-31
Volume 18, Issue 3, Year 2018, On page(s): 79 - 86
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.03011
Web of Science Accession Number: 000442420900011
SCOPUS ID: 85052056771
Abstract
Tangent Sigmoid (TanSig) Transfer Function (TSTF) is one of the nonlinear functions used in Artificial Neural Networks (ANNs). As TSTF includes exponential function operations, hardware-based implementation of this function is difficult. Thus, various methods have been proposed in the literature for the hardware implementation of TSTF. In this study, four different TSTF approaches on FPGA have been implemented using 32-bit IEEE 7541985 floating point number standard, and their performance analyses and FPGA chip statistics are presented. The Van der Pol system ANN application was carried out using four different FPGA-based TSTF units presented. The Multilayer feed-forward neural network structure was used in the study. The FPGA chip statistics and sensitivity analyses were carried out by applying each TSTF structure to the exemplary ANN. The maximum operating frequency of ANNs designed on FPGA using the four different TSTF units varied between 184362 MHz. The CORDIC-LUT-based ANN on FPGA was able to calculate 1 billion results in 3.284 s. According to the Van der Pol system ANN application carried out on FPGA, the CORDIC-LUT-based approach most closely reflected the reference ANN results. This study has a reference and key research for real-time artificial neural network applications used of tangent sigmoid one of the nonlinear transfer functions. |
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[1] Implementation of Dormand-Prince based chaotic oscillator designs in different IQ-Math number standards on FPGA, KOYUNCU, İsmail, ŞEKER, Halil İbrahim, Sakarya University Journal of Science, ISSN 1301-4048, 2019.
Digital Object Identifier: 10.16984/saufenbilder.505497 [CrossRef]
[2] Hyperjerk multiscroll oscillators with megastability: Analysis, FPGA implementation and a novel ANN-ring-based True Random Number Generator, Tuna, Murat, Karthikeyan, Anitha, Rajagopal, Karthikeyan, Alcin, Murat, Koyuncu, İsmail, AEU - International Journal of Electronics and Communications, ISSN 1434-8411, Issue , 2019.
Digital Object Identifier: 10.1016/j.aeue.2019.152941 [CrossRef]
[3] A novel high speed Artificial Neural Network-based chaotic True Random Number Generator on Field Programmable Gate Array, Alcin, Murat, Koyuncu, Ismail, Tuna, Murat, Varan, Metin, Pehlivan, Ihsan, International Journal of Circuit Theory and Applications, ISSN 0098-9886, Issue 3, Volume 47, 2019.
Digital Object Identifier: 10.1002/cta.2581 [CrossRef]
[4] Artificial Neural Networks based thermodynamic and economic analysis of a hydrogen production system assisted by geothermal energy on Field Programmable Gate Array, Yilmaz, Ceyhun, Koyuncu, Ismail, Alcin, Murat, Tuna, Murat, International Journal of Hydrogen Energy, ISSN 0360-3199, Issue 33, Volume 44, 2019.
Digital Object Identifier: 10.1016/j.ijhydene.2019.05.049 [CrossRef]
[5] Dynamical analysis, sliding mode synchronization of a fractional-order memristor Hopfield neural network with parameter uncertainties and its non-fractional-order FPGA implementation, Rajagopal, Karthikeyan, Tuna, Murat, Karthikeyan, Anitha, Koyuncu, İsmail, Duraisamy, Prakash, Akgul, Akif, The European Physical Journal Special Topics, ISSN 1951-6355, Issue 10, Volume 228, 2019.
Digital Object Identifier: 10.1140/epjst/e2019-900005-8 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.