Hardware And Software Partitioning Using Genetic Algorithm In Image Processing Application
In this project, HW-SW Partitioning is used as a process to map each task of image processing application to be executed either in software (Hard Processor System, HPS) or hardware (Field Programmable Gate Array, FPGA). The framework for HW-SW Partitioning using Genetic Algorithm (GA) is developed...
| Main Author: | |
|---|---|
| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2018
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/53491/1/Hardware%20And%20Software%20Partitioning%20Using%20Genetic%20Algorithm%20In%20Image%20Processing%20Application_Loo%20Fang%20Hean_E3_2018.pdf |
| _version_ | 1797013033380216832 |
|---|---|
| author | Loo, Fang Hean |
| author_facet | Loo, Fang Hean |
| author_sort | Loo, Fang Hean |
| collection | USM |
| description | In this project, HW-SW Partitioning is used as a process to map each task of image processing application to be executed either in software (Hard Processor System, HPS) or hardware (Field Programmable Gate Array, FPGA). The framework for HW-SW
Partitioning using Genetic Algorithm (GA) is developed in MATLAB. Total ten different combinations of GA parameters are used to test the developed framework. The GA parameters such as population size, crossover percentage and mutation percentage are varied to get the optimum combination of GA parameters. Three different HW/SW Partitioned Solutions are generated and the HW resources spent by first, second, and third solutions must not exceed the constraint value, Q = 341, Q = 681, and Q = 1022 respectively. The HW resource spent in HW/SW Partitioned Solution 1 (Q = 341) is 77.97% lesser than pure hardware solution. It is 4.65% faster than pure hardware solution and
26.6% faster than pure software solution. The HW resource spent in HW/SW Partitioned Solution 2 (Q = 681) is 50.29% lesser than pure hardware solution. It is 8.51% faster than pure hardware solution and 29.61% faster than pure software solution. The HW resource spent in HW/SW Partitioned Solution 3 (Q = 1022) is 45.01% lesser than pure hardware solution. It is 10.09% faster than pure hardware solution and 30.83% faster than pure
software solution. Future work of this project is to implement the HW-SW partitioned solution in Altera DE1-SoC. |
| first_indexed | 2024-03-06T15:56:18Z |
| format | Monograph |
| id | usm.eprints-53491 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:56:18Z |
| publishDate | 2018 |
| publisher | Universiti Sains Malaysia |
| record_format | dspace |
| spelling | usm.eprints-534912022-07-20T08:58:28Z http://eprints.usm.my/53491/ Hardware And Software Partitioning Using Genetic Algorithm In Image Processing Application Loo, Fang Hean T Technology TK Electrical Engineering. Electronics. Nuclear Engineering In this project, HW-SW Partitioning is used as a process to map each task of image processing application to be executed either in software (Hard Processor System, HPS) or hardware (Field Programmable Gate Array, FPGA). The framework for HW-SW Partitioning using Genetic Algorithm (GA) is developed in MATLAB. Total ten different combinations of GA parameters are used to test the developed framework. The GA parameters such as population size, crossover percentage and mutation percentage are varied to get the optimum combination of GA parameters. Three different HW/SW Partitioned Solutions are generated and the HW resources spent by first, second, and third solutions must not exceed the constraint value, Q = 341, Q = 681, and Q = 1022 respectively. The HW resource spent in HW/SW Partitioned Solution 1 (Q = 341) is 77.97% lesser than pure hardware solution. It is 4.65% faster than pure hardware solution and 26.6% faster than pure software solution. The HW resource spent in HW/SW Partitioned Solution 2 (Q = 681) is 50.29% lesser than pure hardware solution. It is 8.51% faster than pure hardware solution and 29.61% faster than pure software solution. The HW resource spent in HW/SW Partitioned Solution 3 (Q = 1022) is 45.01% lesser than pure hardware solution. It is 10.09% faster than pure hardware solution and 30.83% faster than pure software solution. Future work of this project is to implement the HW-SW partitioned solution in Altera DE1-SoC. Universiti Sains Malaysia 2018-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53491/1/Hardware%20And%20Software%20Partitioning%20Using%20Genetic%20Algorithm%20In%20Image%20Processing%20Application_Loo%20Fang%20Hean_E3_2018.pdf Loo, Fang Hean (2018) Hardware And Software Partitioning Using Genetic Algorithm In Image Processing Application. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik dan Elektronik. (Submitted) |
| spellingShingle | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering Loo, Fang Hean Hardware And Software Partitioning Using Genetic Algorithm In Image Processing Application |
| title | Hardware And Software Partitioning Using Genetic Algorithm In Image Processing Application |
| title_full | Hardware And Software Partitioning Using Genetic Algorithm In Image Processing Application |
| title_fullStr | Hardware And Software Partitioning Using Genetic Algorithm In Image Processing Application |
| title_full_unstemmed | Hardware And Software Partitioning Using Genetic Algorithm In Image Processing Application |
| title_short | Hardware And Software Partitioning Using Genetic Algorithm In Image Processing Application |
| title_sort | hardware and software partitioning using genetic algorithm in image processing application |
| topic | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering |
| url | http://eprints.usm.my/53491/1/Hardware%20And%20Software%20Partitioning%20Using%20Genetic%20Algorithm%20In%20Image%20Processing%20Application_Loo%20Fang%20Hean_E3_2018.pdf |
| work_keys_str_mv | AT loofanghean hardwareandsoftwarepartitioningusinggeneticalgorithminimageprocessingapplication |