A Fast Multi-Objective Genetic Algorithm for Hardware-Software Partitioning In Embedded System Design
نویسندگان
چکیده
This paper proposes a novel Multi-Objective Evolutionary Algorithm for hardware software partitioning of embedded systems. Customized genetic algorithms (GA) have been effectively used for solving complex optimization problems (NP Hard) but are mainly applied to optimize a particular solution with respect to a single objective. Many real world problems in embedded systems have multiple objective functions like area, performance, power, latency etc., which are to be maximized or minimized at the early stage of the design process. Hardwaresoftware partitioning of embedded systems involves partitioning the system specification into hardware and software implementations with the goal to find a set of implementations that satisfy a number of constraints on cost and performance. In this paper a novel multiobjective algorithm called elitist non-dominated sorting genetic algorithm (NSGA-II) is applied to search for multiple optimal solutions, the knowledge of which helps the designer to compare and choose a compromised optimal solution for which hardware/software design can be implemented. The algorithm was implemented in C programming language. The application and adaptation of the NSGA-II algorithm and Weighted-Sum genetic Algorithm (WSGA) was analyzed for a well known 8point FFT algorithm which can also be extended for 16-point FFT etc. From the simulation results NSGA-II was found to perform better than WSGA.
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