Genetic Algorithm Implementation In MPSoC

Authors(3) :-Jenitha A, Dr. R. Elumalai, Dr. S. Sujitha

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.

Authors and Affiliations

Jenitha A
Research Scholar, Vishvesvaraya Technological University, Belagavi, India
Dr. R. Elumalai
Department of Electrical and Electronics Engineering, New Horizon College of Engineering Bangalore, India
Dr. S. Sujitha
Department of Electrical and Electronics Engineering, New Horizon College of Engineering Bangalore, India

Multiprocessor, Crossover, Mutation, Fitness Function.

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Publication Details

Published in : Volume 4 | Issue 5 | March-April 2018
Date of Publication : 2018-04-14
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 272-276
Manuscript Number : CSEIT184534
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Jenitha A, Dr. R. Elumalai, Dr. S. Sujitha, "Genetic Algorithm Implementation In MPSoC ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 5, pp.272-276, March-April-2018.
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