Sistema embarcado reconfigurável de forma estática por programação genética utilizando hardware evolucionário híbrido
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Universidade Federal de São Carlos
Resumo
The use of technology based on Field Programmable Gate Arrays (FPGAs), a
reconfigurable technology, has become a frequent object of study. This technique is
feasible and a promising application in the development of embedded systems, however,
the difficulty in finding a flexible and efficient way to perform such an application is their
bigger problem. In this work, a virtual and reconfigurable architecture (AVR) in FPGA for
hardware applications is presented using a Genetic Programming Software on the
development of an optimal reconfiguration for this AVR, in order to build a hardware
capable of performing a given task in an embedded system. This proposal is a simple,
flexible and efficient way to achieve appropriate applications in embedded systems, when
compared to other reconfigurable hardware techniques. The representation of phenotype
of the proposed evolutionary system is based on a bi-dimensional network function
elements (EF). The GPLAB tool for MATLAB is used in Genetic Programming, and the
solution found by this procedure is converted into a memory mapping to represent the best
solution, where it is used to reconfigure the hardware. In the tests, GPLAB found results
for logic circuits in a few generations, and for image filters containing efficient solutions,
where there was little hardware occupation, especially memory, in the cases this has been
presented, with a reduced chromosome size, shows a proposal efficiency.
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Arquitetura virtual reconfigurável, FPGA, Hardware evolucionário, GPLAB, Programação genética, Virtual Reconfigurable Architecture, Field Programmable Gate Arrays, Evolvable Hardware, Genetic programming, Cartesian genetic programming, Embedded systems, Programming embedded systems, Digital Image Processing
Citação
ALMEIDA, Manoel Aranda de. Sistema embarcado reconfigurável de forma estática por programação genética utilizando hardware evolucionário híbrido. 2016. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2016. Disponível em: https://repositorio.ufscar.br/handle/20.500.14289/8000.