Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function

Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of...

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Main Author:	Syahrulanuar, Ngah
Format:	Thesis
Language:	English
Published:	2021
Subjects:	Q Science (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/38461/1/Field%20programmable%20gate%20array%20based%20sigmoid%20function%20implementation%20using%20differential%20lookup%20table%20and%20second%20order%20nonlinear%20function.ir.pdf
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id	my-ump-ir.38461
record_format	uketd_dc
spelling	my-ump-ir.384612023-08-25T02:14:58Z Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function 2021-08 Syahrulanuar, Ngah Q Science (General) QA75 Electronic computers. Computer science Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of neurons. In order to overcome this, ANN is implemented into hardware namely field-programmable-gate-array (FPGA). However, implementing the ANN into a field-programmable gate array (FPGA) has led to a new problem related to the sigmoid function implementation. Often used as the activation function for ANN, a sigmoid function cannot be directly implemented in FPGA. Owing to its accuracy, the lookup table (LUT) has always been used to implement the sigmoid function in FPGA. In this case, obtaining the high accuracy of LUT is expensive particularly in terms of its memory requirements in FPGA. Second-order nonlinear function (SONF) is an appealing replacement for LUT due to its small memory requirement. Although there is a trade-off between accuracy and memory size. Taking the advantage of the aforementioned approaches, this thesis proposed a combination of SONF and a modified LUT namely differential lookup table (dLUT). The deviation values between SONF and sigmoid function are used to create the dLUT. SONF is used as the first step to approximate the sigmoid function. Then it is followed by adding or deducting with the value that has been stored in the dLUT as a second step as demonstrated via simulation. This combination has successfully reduced the deviation value. The reduction value is significant as compared to previous implementations such as SONF, and LUT itself. Further simulation has been carried out to evaluate the accuracy of the ANN in detecting the object in an indoor environment by using the proposed method as a sigmoid function. The result has proven that the proposed method has produced the output almost as accurately as software implementation in detecting the target in indoor positioning problems. Therefore, the proposed method can be applied in any field that demands higher processing and high accuracy in sigmoid function output 2021-08 Thesis http://umpir.ump.edu.my/id/eprint/38461/ http://umpir.ump.edu.my/id/eprint/38461/1/Field%20programmable%20gate%20array%20based%20sigmoid%20function%20implementation%20using%20differential%20lookup%20table%20and%20second%20order%20nonlinear%20function.ir.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Computing Rohani, Abu Bakar
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Rohani, Abu Bakar
topic	Q Science (General) Q Science (General)
spellingShingle	Q Science (General) Q Science (General) Syahrulanuar, Ngah Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function
description	Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of neurons. In order to overcome this, ANN is implemented into hardware namely field-programmable-gate-array (FPGA). However, implementing the ANN into a field-programmable gate array (FPGA) has led to a new problem related to the sigmoid function implementation. Often used as the activation function for ANN, a sigmoid function cannot be directly implemented in FPGA. Owing to its accuracy, the lookup table (LUT) has always been used to implement the sigmoid function in FPGA. In this case, obtaining the high accuracy of LUT is expensive particularly in terms of its memory requirements in FPGA. Second-order nonlinear function (SONF) is an appealing replacement for LUT due to its small memory requirement. Although there is a trade-off between accuracy and memory size. Taking the advantage of the aforementioned approaches, this thesis proposed a combination of SONF and a modified LUT namely differential lookup table (dLUT). The deviation values between SONF and sigmoid function are used to create the dLUT. SONF is used as the first step to approximate the sigmoid function. Then it is followed by adding or deducting with the value that has been stored in the dLUT as a second step as demonstrated via simulation. This combination has successfully reduced the deviation value. The reduction value is significant as compared to previous implementations such as SONF, and LUT itself. Further simulation has been carried out to evaluate the accuracy of the ANN in detecting the object in an indoor environment by using the proposed method as a sigmoid function. The result has proven that the proposed method has produced the output almost as accurately as software implementation in detecting the target in indoor positioning problems. Therefore, the proposed method can be applied in any field that demands higher processing and high accuracy in sigmoid function output
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Syahrulanuar, Ngah
author_facet	Syahrulanuar, Ngah
author_sort	Syahrulanuar, Ngah
title	Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function
title_short	Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function
title_full	Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function
title_fullStr	Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function
title_full_unstemmed	Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function
title_sort	field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Computing
publishDate	2021
url	http://umpir.ump.edu.my/id/eprint/38461/1/Field%20programmable%20gate%20array%20based%20sigmoid%20function%20implementation%20using%20differential%20lookup%20table%20and%20second%20order%20nonlinear%20function.ir.pdf
_version_	1783732291197992960

Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function

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