A Reconfigurable Custom Machine for Accelerating Cellular Genetic Algorithms
In this work we present a reconfigurable and scalable custom processor array for solving optimization problems using cellular genetic algorithms (cGAs), based on a regular fabric of processing nodes and local memories. Cellular genetic algorithms are a variant of the well-known genetic algorithm tha...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidade do Porto
2016-07-01
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| Series: | U.Porto Journal of Engineering |
| Subjects: | |
| Online Access: | https://journalengineering.fe.up.pt/article/view/64 |
| _version_ | 1828531654295027712 |
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| author | P. V. Santos José Carlos Alves João Canas Ferreira |
| author_facet | P. V. Santos José Carlos Alves João Canas Ferreira |
| author_sort | P. V. Santos |
| collection | DOAJ |
| description | In this work we present a reconfigurable and scalable custom processor array for solving optimization problems using cellular genetic algorithms (cGAs), based on a regular fabric of processing nodes and local memories. Cellular genetic algorithms are a variant of the well-known genetic algorithm that can conveniently exploit the coarse-grain parallelism afforded by this architecture. To ease the design of the proposed computing engine for solving different optimization problems, a high-level synthesis design flow is proposed, where the problem-dependent operations of the algorithm are specified in C++ and synthesized to custom hardware. A spectrum allocation problem was used as a case study and successfully implemented in a Virtex-6 FPGA device, showing relevant figures for the computing acceleration. |
| first_indexed | 2024-12-11T22:41:46Z |
| format | Article |
| id | doaj.art-f13feda01acf4a3a85789b94478562ce |
| institution | Directory Open Access Journal |
| issn | 2183-6493 |
| language | English |
| last_indexed | 2024-12-11T22:41:46Z |
| publishDate | 2016-07-01 |
| publisher | Universidade do Porto |
| record_format | Article |
| series | U.Porto Journal of Engineering |
| spelling | doaj.art-f13feda01acf4a3a85789b94478562ce2022-12-22T00:47:47ZengUniversidade do PortoU.Porto Journal of Engineering2183-64932016-07-012221310.24840/2183-6493_002.002_000264A Reconfigurable Custom Machine for Accelerating Cellular Genetic AlgorithmsP. V. Santos0José Carlos Alves1João Canas Ferreira2INESC TECUniversidade do PortoUniversidade do PortoIn this work we present a reconfigurable and scalable custom processor array for solving optimization problems using cellular genetic algorithms (cGAs), based on a regular fabric of processing nodes and local memories. Cellular genetic algorithms are a variant of the well-known genetic algorithm that can conveniently exploit the coarse-grain parallelism afforded by this architecture. To ease the design of the proposed computing engine for solving different optimization problems, a high-level synthesis design flow is proposed, where the problem-dependent operations of the algorithm are specified in C++ and synthesized to custom hardware. A spectrum allocation problem was used as a case study and successfully implemented in a Virtex-6 FPGA device, showing relevant figures for the computing acceleration.https://journalengineering.fe.up.pt/article/view/64MicroelectronicsElectrical Engineering |
| spellingShingle | P. V. Santos José Carlos Alves João Canas Ferreira A Reconfigurable Custom Machine for Accelerating Cellular Genetic Algorithms U.Porto Journal of Engineering Microelectronics Electrical Engineering |
| title | A Reconfigurable Custom Machine for Accelerating Cellular Genetic Algorithms |
| title_full | A Reconfigurable Custom Machine for Accelerating Cellular Genetic Algorithms |
| title_fullStr | A Reconfigurable Custom Machine for Accelerating Cellular Genetic Algorithms |
| title_full_unstemmed | A Reconfigurable Custom Machine for Accelerating Cellular Genetic Algorithms |
| title_short | A Reconfigurable Custom Machine for Accelerating Cellular Genetic Algorithms |
| title_sort | reconfigurable custom machine for accelerating cellular genetic algorithms |
| topic | Microelectronics Electrical Engineering |
| url | https://journalengineering.fe.up.pt/article/view/64 |
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