Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System

Dengan kos alat pengesan yang semakin rendah, masa depan sistem pedati pengikut autonomi akan dilengkapi dengan lebih banyak alat pengesan. Ini menjadi cabaran rekabentuk dalam mengendalikan data besar dan kerumitan perkukuhan. Kebanyakan sistem yang sedia ada menggunakan papan mikropengawal yang me...

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Main Author:	Liew, Yeong Tat_
Format:	Thesis
Language:	English
Published:	2015
Subjects:	T Technology TK7800-8360 Electronics
Online Access:	http://eprints.usm.my/40704/1/Investigation_on_Mlp_Artificial_Neural_Network_Using_FPGA_For_Autonomous_Cart_Follower_System.pdf
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id	my-usm-ep.40704
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spelling	my-usm-ep.407042018-06-06T06:59:46Z Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System 2015-07 Liew, Yeong Tat_ T Technology TK7800-8360 Electronics Dengan kos alat pengesan yang semakin rendah, masa depan sistem pedati pengikut autonomi akan dilengkapi dengan lebih banyak alat pengesan. Ini menjadi cabaran rekabentuk dalam mengendalikan data besar dan kerumitan perkukuhan. Kebanyakan sistem yang sedia ada menggunakan papan mikropengawal yang mempunyai prestasi yang terhad dan pengembangan tidak mungkin tanpa penggantian yang lebih baru. Projek ini mencadangkan perlaksanaan alternatif sistem pedati pengikut autonomi dengan model rangkaian neural MLP menggunakan FPGA. Sistem pedati pengikut autonomi yang mengguakan papan mikropengawal telah diubah suai untuk menggunakan papan FPGA dan dilaksanakan melalui Sistem pada Chip (SOC). System rangkaian neural dilatih dalam simulasi dengan vektor latihan yang dikumpul daripada sistem pedati pengikut autonomi yang sedia ada. System rangkaian neural kemudian dilaksanakan sebagai perkukuhan dalam SOC itu. Dalam pemerhatian, jejak perkukuhan model rangkaian neural kekal saiz kecil tanpa mengira saiz rangkaian neural. Hasil kajian menunjukkan bahawa dengan penggunaan sumber tambahan sebanyak 40%, penambahbaikan sistem secara keseluruhan sebanyak 27 kali dicapai dengan penggunaan blok pecutan perkakasan di SOC, berbanding dengan SOC tanpa penggunaan blok pecutan perkakasan. ________________________________________________________________________________________________________________________ The future of the autonomous cart follower system will equipped with lots of sensory data, due to the ever lower cost of sensory device. This provides design challenge on handling large data and firmware complexity. Most of the existing systems are implemented via usage of microcontroller board, which has limited performance and expansion is not possible without replacement of newer board. The project proposes an alternative approach of running the autonomous cart follower systems on neural network model using Field Programmable Gates Array (FPGA). A microcontroller based autonomous cart follower systems is modified to use the FPGA board and implemented via the System on Chip (SOC) approach. The neural network is trained offline in simulation tools with training vector collected from running the existing autonomous cart follower systems. The trained neural network model then implemented as software code in the SOC. By observation the firmware footprint of the neural network model remains small size regardless of the neural network size. The result shows that with 40% more additional resource utilization, the overall system improvement of 27 times is achieved with the usage of hardware acceleration block in SOC compared to SOC without hardware acceleration. 2015-07 Thesis http://eprints.usm.my/40704/ http://eprints.usm.my/40704/1/Investigation_on_Mlp_Artificial_Neural_Network_Using_FPGA_For_Autonomous_Cart_Follower_System.pdf application/pdf en public masters Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik Dan Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology TK7800-8360 Electronics
spellingShingle	T Technology TK7800-8360 Electronics Liew, Yeong Tat_ Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System
description	Dengan kos alat pengesan yang semakin rendah, masa depan sistem pedati pengikut autonomi akan dilengkapi dengan lebih banyak alat pengesan. Ini menjadi cabaran rekabentuk dalam mengendalikan data besar dan kerumitan perkukuhan. Kebanyakan sistem yang sedia ada menggunakan papan mikropengawal yang mempunyai prestasi yang terhad dan pengembangan tidak mungkin tanpa penggantian yang lebih baru. Projek ini mencadangkan perlaksanaan alternatif sistem pedati pengikut autonomi dengan model rangkaian neural MLP menggunakan FPGA. Sistem pedati pengikut autonomi yang mengguakan papan mikropengawal telah diubah suai untuk menggunakan papan FPGA dan dilaksanakan melalui Sistem pada Chip (SOC). System rangkaian neural dilatih dalam simulasi dengan vektor latihan yang dikumpul daripada sistem pedati pengikut autonomi yang sedia ada. System rangkaian neural kemudian dilaksanakan sebagai perkukuhan dalam SOC itu. Dalam pemerhatian, jejak perkukuhan model rangkaian neural kekal saiz kecil tanpa mengira saiz rangkaian neural. Hasil kajian menunjukkan bahawa dengan penggunaan sumber tambahan sebanyak 40%, penambahbaikan sistem secara keseluruhan sebanyak 27 kali dicapai dengan penggunaan blok pecutan perkakasan di SOC, berbanding dengan SOC tanpa penggunaan blok pecutan perkakasan. ________________________________________________________________________________________________________________________ The future of the autonomous cart follower system will equipped with lots of sensory data, due to the ever lower cost of sensory device. This provides design challenge on handling large data and firmware complexity. Most of the existing systems are implemented via usage of microcontroller board, which has limited performance and expansion is not possible without replacement of newer board. The project proposes an alternative approach of running the autonomous cart follower systems on neural network model using Field Programmable Gates Array (FPGA). A microcontroller based autonomous cart follower systems is modified to use the FPGA board and implemented via the System on Chip (SOC) approach. The neural network is trained offline in simulation tools with training vector collected from running the existing autonomous cart follower systems. The trained neural network model then implemented as software code in the SOC. By observation the firmware footprint of the neural network model remains small size regardless of the neural network size. The result shows that with 40% more additional resource utilization, the overall system improvement of 27 times is achieved with the usage of hardware acceleration block in SOC compared to SOC without hardware acceleration.
format	Thesis
qualification_level	Master's degree
author	Liew, Yeong Tat_
author_facet	Liew, Yeong Tat_
author_sort	Liew, Yeong Tat_
title	Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System
title_short	Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System
title_full	Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System
title_fullStr	Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System
title_full_unstemmed	Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System
title_sort	investigation on mlp artificial neural network using fpga for autonomous cart follower system
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik Dan Elektronik
publishDate	2015
url	http://eprints.usm.my/40704/1/Investigation_on_Mlp_Artificial_Neural_Network_Using_FPGA_For_Autonomous_Cart_Follower_System.pdf
_version_	1747820806453329920

Investigation on Mlp Artificial Neural Network Using FPGA For Autonomous Cart Follower System

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