Genetic Algorithm Implementation In MPSoC

Abstract

Multiprocessor designs are the best substitute for single-core designs, but the new architecture has any kind of architectural problems associated with it. The main problems are the tools and techniques needed to maximize multiprocessors and develop new techniques to produce powerful architecture associated. To overcome the above problems, one of the best techniques is to combine the techniques of planning and management of memory in computer systems. Here, we introduce a genetic algorithm to do the same. This algorithm finds the best solution by performing three operations, namely, mutation, crossover and the fitness function for which the planning of activities on multiple processors is done with the use of adequate memory. By implementing this algorithm in different tasks, the total delay is reduced and an increase is also obtained in terms of performance. The implementation was made with Xilinx.