Using FPGA Devices to Accelerate Tree-Based Genetic                  Programming: A Preliminary Exploration with Recent                  Technologies 
Created by W.Langdon from
gp-bibliography.bib Revision:1.8612
- @InProceedings{Crary:2023:EuroGP,
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  author =       "Christopher Crary and Wesley Piard and Greg Stitt and 
Caleb Bean and Benjamin Hicks",
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  title =        "Using {FPGA} Devices to Accelerate Tree-Based Genetic
Programming: A Preliminary Exploration with Recent
Technologies",
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  booktitle =    "EuroGP 2023: Proceedings of the 26th European
Conference on Genetic Programming",
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  year =         "2023",
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  month =        "12-14 " # apr,
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  editor =       "Gisele Pappa and Mario Giacobini and Zdenek Vasicek",
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  series =       "LNCS",
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  volume =       "13986",
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  publisher =    "Springer Verlag",
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  address =      "Brno, Czech Republic",
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  pages =        "182--197",
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  organisation = "EvoStar, Species",
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  keywords =     "genetic algorithms, genetic programming, Tree-based
genetic programming, Field-programmable gate arrays,
Hardware acceleration, DEAP, TensorGP, Operon",
- 
  isbn13 =       "978-3-031-29572-0",
- 
  URL =          " https://rdcu.be/c8UYC", https://rdcu.be/c8UYC",
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  DOI =          " 10.1007/978-3-031-29573-7_12", 10.1007/978-3-031-29573-7_12",
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  code_url =     " https://github.com/christophercrary/conference-eurogp-2023", https://github.com/christophercrary/conference-eurogp-2023",
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  size =         "16 pages",
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  abstract =     "we explore the prospect of accelerating tree-based
genetic programming (TGP) by way of modern
field-programmable gate array (FPGA) devices, which is
motivated by the fact that FPGAs can sometimes leverage
larger amounts of data/function parallelism, as well as
better energy efficiency, when compared to
general-purpose CPU/GPU systems. we introduce a
fixed-depth, tree-based architecture capable of
evaluating type-consistent primitives that can be fully
unrolled and pipelined. The current primitive
constraints preclude arbitrary control structures, but
they allow for entire programs to be evaluated every
clock cycle. Using a variety of floating-point
primitives and random programs, we compare to the
recent TensorGP tool executing on a modern 8 nm GPU,
and we show that our accelerator implemented on a 14 nm
FPGA achieves an average speedup of 43 times. When
compared to the popular baseline tool DEAP executing",
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  notes =        "Part of \cite{Pappa:2023:GP} EuroGP'2023 held in
conjunction with EvoCOP2023, EvoMusArt2023 and
EvoApplications2023",
- }
Genetic Programming entries for 
Christopher C Crary
Wesley P Piard
Greg Stitt
Caleb Bean
Benjamin Hicks
Citations
