On Evolutionary Approximation of Sigmoid Function for HW/SW Embedded Systems
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- @InProceedings{Minarik:2017:EuroGP,
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author = "Milos Minarik and Lukas Sekanina",
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title = "On Evolutionary Approximation of Sigmoid Function for
HW/SW Embedded Systems",
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booktitle = "EuroGP 2017: Proceedings of the 20th European
Conference on Genetic Programming",
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year = "2017",
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month = "19-21 " # apr,
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editor = "Mauro Castelli and James McDermott and
Lukas Sekanina",
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series = "LNCS",
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volume = "10196",
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publisher = "Springer Verlag",
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address = "Amsterdam",
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pages = "343--358",
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organisation = "species",
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keywords = "genetic algorithms, genetic programming: Poster",
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isbn13 = "978-3-319-55695-6",
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DOI = "doi:10.1007/978-3-319-55696-3_22",
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abstract = "Providing machine learning capabilities on low cost
electronic devices is a challenging goal especially in
the context of the Internet of Things paradigm. In
order to deliver high performance machine intelligence
on low power devices, suitable hardware accelerators
have to be introduced. In this paper, we developed a
method enabling to evolve a hardware implementation
together with a corresponding software controller for
key components of smart embedded systems. The proposed
approach is based on a multi-objective design space
exploration conducted by means of extended linear
genetic programming. The approach was evaluated in the
task of approximate sigmoid function design which is an
important component of hardware implementations of
neural networks. During these experiments, we
automatically rediscovered some approximate sigmoid
functions known from the literature. The method was
implemented as an extension of an existing platform
supporting concurrent evolution of hardware and
software of embedded systems.",
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notes = "Part of \cite{Castelli:2017:GP} EuroGP'2017 held
inconjunction with EvoCOP2017, EvoMusArt2017 and
EvoApplications2017",
- }
Genetic Programming entries for
Milos Minarik
Lukas Sekanina
Citations