Hardware Software Partitioning Using Particle Swarm Optimization (PSO) In Image Processing Application
In this project, car plate identification will be implemented in hardware-software partitioning by using PSO algorithm. The framework for hardware-software partitioning using PSO algorithm in MATLAB is developed. The performance between the solution in hardware-software partitioning and the solution...
| প্রধান লেখক: | |
|---|---|
| বিন্যাস: | Monograph |
| ভাষা: | English |
| প্রকাশিত: |
Universiti Sains Malaysia
2018
|
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | http://eprints.usm.my/53520/1/Hardware%20Software%20Partitioning%20Using%20Particle%20Swarm%20Optimization%20%28PSO%29%20In%20Image%20Processing%20Application_Tan%20Jia%20Zheng_E3_2018.pdf |
| _version_ | 1825906899218857984 |
|---|---|
| author | Tan, Jia Zheng |
| author_facet | Tan, Jia Zheng |
| author_sort | Tan, Jia Zheng |
| collection | USM |
| description | In this project, car plate identification will be implemented in hardware-software partitioning by using PSO algorithm. The framework for hardware-software partitioning using PSO algorithm in MATLAB is developed. The performance between the solution in hardware-software partitioning and the solution without partitioning is investigated. The image processing’s formulas are verified in Visual Studio. Then the coding is written in Verilog for hardware and C language for software to obtain the execution time and resources consumption. Then the data will be processed in MATLAB using PSO algorithm to determine the optimal result in partitioning. The PSO algorithm parameters such as the number of iteration and number of particles are varied to obtain the optimum value for the parameters. Three different constraints value, C=1022, C=681 and C=341 are take into consideration to generate an optimum solution. The solution for C=1022 use 55% of the total hardware resources (1362) in pure hardware. It is 1.11 times faster than
pure hardware and 1.45 times faster than pure software. The solution for C=681 use 49.7% of the total hardware resources in pure hardware and it is 1.09 times faster than pure hardware and 1.42 times faster than pure software. The solution for C=341 use 22.03% of the total hardware resources in pure hardware and it is 1.05 times faster than pure hardware and 1.36 times faster than pure software. Performance in hardware-software partitioning is higher or better compare to pure hardware and pure software. Hardware-software partitioning has fast in processing speed and use less in hardware resources.
Future work of this project is to implement the hardware-software partitioning solution in Altera DE1-SoC. |
| first_indexed | 2024-03-06T15:56:23Z |
| format | Monograph |
| id | usm.eprints-53520 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:56:23Z |
| publishDate | 2018 |
| publisher | Universiti Sains Malaysia |
| record_format | dspace |
| spelling | usm.eprints-535202022-07-22T06:54:59Z http://eprints.usm.my/53520/ Hardware Software Partitioning Using Particle Swarm Optimization (PSO) In Image Processing Application Tan, Jia Zheng T Technology TK Electrical Engineering. Electronics. Nuclear Engineering In this project, car plate identification will be implemented in hardware-software partitioning by using PSO algorithm. The framework for hardware-software partitioning using PSO algorithm in MATLAB is developed. The performance between the solution in hardware-software partitioning and the solution without partitioning is investigated. The image processing’s formulas are verified in Visual Studio. Then the coding is written in Verilog for hardware and C language for software to obtain the execution time and resources consumption. Then the data will be processed in MATLAB using PSO algorithm to determine the optimal result in partitioning. The PSO algorithm parameters such as the number of iteration and number of particles are varied to obtain the optimum value for the parameters. Three different constraints value, C=1022, C=681 and C=341 are take into consideration to generate an optimum solution. The solution for C=1022 use 55% of the total hardware resources (1362) in pure hardware. It is 1.11 times faster than pure hardware and 1.45 times faster than pure software. The solution for C=681 use 49.7% of the total hardware resources in pure hardware and it is 1.09 times faster than pure hardware and 1.42 times faster than pure software. The solution for C=341 use 22.03% of the total hardware resources in pure hardware and it is 1.05 times faster than pure hardware and 1.36 times faster than pure software. Performance in hardware-software partitioning is higher or better compare to pure hardware and pure software. Hardware-software partitioning has fast in processing speed and use less in hardware resources. Future work of this project is to implement the hardware-software partitioning solution in Altera DE1-SoC. Universiti Sains Malaysia 2018-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/53520/1/Hardware%20Software%20Partitioning%20Using%20Particle%20Swarm%20Optimization%20%28PSO%29%20In%20Image%20Processing%20Application_Tan%20Jia%20Zheng_E3_2018.pdf Tan, Jia Zheng (2018) Hardware Software Partitioning Using Particle Swarm Optimization (PSO) 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 Tan, Jia Zheng Hardware Software Partitioning Using Particle Swarm Optimization (PSO) In Image Processing Application |
| title | Hardware Software Partitioning Using Particle Swarm Optimization (PSO) In Image Processing Application |
| title_full | Hardware Software Partitioning Using Particle Swarm Optimization (PSO) In Image Processing Application |
| title_fullStr | Hardware Software Partitioning Using Particle Swarm Optimization (PSO) In Image Processing Application |
| title_full_unstemmed | Hardware Software Partitioning Using Particle Swarm Optimization (PSO) In Image Processing Application |
| title_short | Hardware Software Partitioning Using Particle Swarm Optimization (PSO) In Image Processing Application |
| title_sort | hardware software partitioning using particle swarm optimization pso in image processing application |
| topic | T Technology TK Electrical Engineering. Electronics. Nuclear Engineering |
| url | http://eprints.usm.my/53520/1/Hardware%20Software%20Partitioning%20Using%20Particle%20Swarm%20Optimization%20%28PSO%29%20In%20Image%20Processing%20Application_Tan%20Jia%20Zheng_E3_2018.pdf |
| work_keys_str_mv | AT tanjiazheng hardwaresoftwarepartitioningusingparticleswarmoptimizationpsoinimageprocessingapplication |