Improving Energy Efficiency of Field-Coupled Nanocomputing Circuits by Evolutionary Synthesis
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Ribeiro:2018:CEC,
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author = "Marco A. Ribeiro and Iago A. Carvalho and
Jeferson F. Chaves and Gisele L. Pappa and Omar P. {Vilela Neto}",
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booktitle = "2018 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Improving Energy Efficiency of Field-Coupled
Nanocomputing Circuits by Evolutionary Synthesis",
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year = "2018",
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abstract = "Moore's law provoked decades of advances in computer's
performance due to transistor's evolution. Despite all
success in its improvement, current technology is
reaching its physical limits and some replacements are
the focus of investigations, such as the Field-Coupled
Nanocomputing devices. These devices achieve
information transfer and computation via local field
interactions, reaching ultra-low power consumption.
Nevertheless, there exists a hard energy limit related
to the Laws of Thermodynamics that bounds any digital
evaluation. To reduce the impact of this restriction,
we propose a fitness function to improve the energy
efficiency on a given circuit implementation. We embed
our fitness function on an evolutionary method known as
Cartesian Genetic Programming to iteratively modify the
circuit, searching for a new valid configuration that
dissipates less energy. To assess our method, we use it
on pre-optimized circuits from benchmarks and compare
our results with the ones from the classic Cartesian
Genetic Programming. Based on the outcome, we show that
our method outperforms the latter, achieving, on
average, 15percent gain in energy efficiency.",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
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DOI = "doi:10.1109/CEC.2018.8477723",
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month = jul,
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notes = "Also known as \cite{8477723}",
- }
Genetic Programming entries for
Marco A Ribeiro
Iago A Carvalho
Jeferson F Chaves
Gisele L Pappa
Omar Paranaiba Vilela Neto
Citations