Design of radix-4 single path delay fast fourier transform processor with genetic algorithms optimization

This research work involves the implementation of Single Objective Genetic Algorithm (SOGA) and Multi-objectives Genetic Algorithm (MOGA) in a 16-point Radix-4 Single Path Delay Feedback (R4SDF) pipelined Fast Fourier Transform (FFT) processor. The FFT processor is a critical block widely used in...

Full description

Saved in:
Bibliographic Details
Main Author: Pang, Jia Hong
Format: Thesis
Language:English
Published: 2011
Subjects:
Online Access:http://psasir.upm.edu.my/id/eprint/41802/1/FK%202011%20140R.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-upm-ir.41802
record_format uketd_dc
spelling my-upm-ir.418022016-03-01T09:11:32Z Design of radix-4 single path delay fast fourier transform processor with genetic algorithms optimization 2011-08 Pang, Jia Hong This research work involves the implementation of Single Objective Genetic Algorithm (SOGA) and Multi-objectives Genetic Algorithm (MOGA) in a 16-point Radix-4 Single Path Delay Feedback (R4SDF) pipelined Fast Fourier Transform (FFT) processor. The FFT processor is a critical block widely used in digital processing, which is considered as a very power consuming block in a device as many data are inputted for the FFT computation. Nowadays, the portability of the electronic devices which uses FFT processor requires being smaller size and low power consumption. One of the methods is to reduce the word length of FFT during its usage. However, the reduction of word length of FFT processor will degrade its Signal to Noise Ratio (SNR) value. The SNR value represents the accuracy of the FFT processor. The larger the word length of the FFT processor, the higher the SNR value, leading to higher Switching Activity (SA), thus increases the power consumption of the FFT processor. In this research, the Genetic Algorithms (GA) is used to optimize the word length of FFT coefficients to maintain the SNR value and reduce the SA at the same time. The genetic algorithms is proven to be a very effective method in optimization by using the way imitating natural process of living beings such as crossover, mutation and selection. The Multi Objective Genetic Algorithms (MOGA) is capable of optimizing the problem which has more than one criterion and both criterions must be treated simultaneously. In this work, the GA optimization is implemented in the twiddle factor of FFT processor. The output solutions performance of the GA optimized FFT is compared to the non-GA solutions. The target of the optimization is to reduce the FFT word length and at the same time the solutions must fulfil the requirement of SNR higher than 63 dB and SA lower the conventional FFT which is 192 times. The results show that, the SOGA capable to optimize the FFT SNR without considering the word length. The MOGA can successfully optimize the performance of FFT by SNR and reducing the word length to 13 bits. Two MOGA methods are used; they are Weighted Sum approach and Non-dominated Sorting Approach. The Weighted Sum approach is more suitable to be implemented in the optimization as it is simpler compared to Non-dominated Sorting Approach. Genetic algorithms 2011-08 Thesis http://psasir.upm.edu.my/id/eprint/41802/ http://psasir.upm.edu.my/id/eprint/41802/1/FK%202011%20140R.pdf application/pdf en public masters Universiti Putra Malaysia Genetic algorithms
institution Universiti Putra Malaysia
collection PSAS Institutional Repository
language English
topic Genetic algorithms


spellingShingle Genetic algorithms


Pang, Jia Hong
Design of radix-4 single path delay fast fourier transform processor with genetic algorithms optimization
description This research work involves the implementation of Single Objective Genetic Algorithm (SOGA) and Multi-objectives Genetic Algorithm (MOGA) in a 16-point Radix-4 Single Path Delay Feedback (R4SDF) pipelined Fast Fourier Transform (FFT) processor. The FFT processor is a critical block widely used in digital processing, which is considered as a very power consuming block in a device as many data are inputted for the FFT computation. Nowadays, the portability of the electronic devices which uses FFT processor requires being smaller size and low power consumption. One of the methods is to reduce the word length of FFT during its usage. However, the reduction of word length of FFT processor will degrade its Signal to Noise Ratio (SNR) value. The SNR value represents the accuracy of the FFT processor. The larger the word length of the FFT processor, the higher the SNR value, leading to higher Switching Activity (SA), thus increases the power consumption of the FFT processor. In this research, the Genetic Algorithms (GA) is used to optimize the word length of FFT coefficients to maintain the SNR value and reduce the SA at the same time. The genetic algorithms is proven to be a very effective method in optimization by using the way imitating natural process of living beings such as crossover, mutation and selection. The Multi Objective Genetic Algorithms (MOGA) is capable of optimizing the problem which has more than one criterion and both criterions must be treated simultaneously. In this work, the GA optimization is implemented in the twiddle factor of FFT processor. The output solutions performance of the GA optimized FFT is compared to the non-GA solutions. The target of the optimization is to reduce the FFT word length and at the same time the solutions must fulfil the requirement of SNR higher than 63 dB and SA lower the conventional FFT which is 192 times. The results show that, the SOGA capable to optimize the FFT SNR without considering the word length. The MOGA can successfully optimize the performance of FFT by SNR and reducing the word length to 13 bits. Two MOGA methods are used; they are Weighted Sum approach and Non-dominated Sorting Approach. The Weighted Sum approach is more suitable to be implemented in the optimization as it is simpler compared to Non-dominated Sorting Approach.
format Thesis
qualification_level Master's degree
author Pang, Jia Hong
author_facet Pang, Jia Hong
author_sort Pang, Jia Hong
title Design of radix-4 single path delay fast fourier transform processor with genetic algorithms optimization
title_short Design of radix-4 single path delay fast fourier transform processor with genetic algorithms optimization
title_full Design of radix-4 single path delay fast fourier transform processor with genetic algorithms optimization
title_fullStr Design of radix-4 single path delay fast fourier transform processor with genetic algorithms optimization
title_full_unstemmed Design of radix-4 single path delay fast fourier transform processor with genetic algorithms optimization
title_sort design of radix-4 single path delay fast fourier transform processor with genetic algorithms optimization
granting_institution Universiti Putra Malaysia
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/41802/1/FK%202011%20140R.pdf
_version_ 1747811888336470016