An FPGA Framework for Genetic Algorithms: Solving the Minimum Energy Broadcast Problem
An FPGA Framework for Genetic Algorithms: Solving the Minimum Energy Broadcast Problem
dc.contributor.author | dos Santos,PV | en |
dc.contributor.author | José Carlos Alves | en |
dc.contributor.author | João Canas Ferreira | en |
dc.date.accessioned | 2018-01-05T16:29:41Z | |
dc.date.available | 2018-01-05T16:29:41Z | |
dc.date.issued | 2015 | en |
dc.description.abstract | Solving complex optimization problems with genetic algorithms (GAs) with custom computing architectures is a way to improve the execution time of this metaheuristic, which is known to consume considerable amounts of time to converge to final solutions. In this work, we present a scalable computing array architecture to accelerate the execution of cellular GAs (cGAs), a variant of genetic algorithms which can conveniently exploit the coarse- grain parallelism afforded by custom parallel processing. The proposed architecture targets Xilinx FPGAs and is used as an auxiliary processor of an embedded CPU (MicroBlaze). To handle different optimization problems, a high- level synthesis (HLS) design flow is proposed where the problem- dependent operations are specified in C++ and synthesised to custom hardware, thus requiring a minimum knowledge of digital design for FPGAs. The minimum energy broadcast (MEB) problem in wireless ad hoc networks is used as a case study. An existing software implementation of a GA to solve this problem is ported to the proposed computing array to demonstrate its effectiveness and the HLS- based design flow. Implementation results in a Virtex- 6 FPGA show significant speedups, while finding solutions with improved quality. | en |
dc.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/5555 | |
dc.identifier.uri | http://dx.doi.org/10.1109/dsd.2015.81 | en |
dc.language | eng | en |
dc.relation | 473 | en |
dc.relation | 258 | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.title | An FPGA Framework for Genetic Algorithms: Solving the Minimum Energy Broadcast Problem | en |
dc.type | conferenceObject | en |
dc.type | Publication | en |
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