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 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