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European Conference on Genetic Programming

EuroGP 2017: Genetic Programming pp 343–358Cite as

On Evolutionary Approximation of Sigmoid Function for HW/SW Embedded Systems

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10196))

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 re-discovered 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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Acknowledgments

This work was supported by the Czech science foundation project GA16-17538S.

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Authors and Affiliations

  1. Faculty of Information Technology, IT4Innovations Centre of Excellence, Brno University of Technology, Brno, Czech Republic

    Milos Minarik & Lukas Sekanina

Authors
  1. Milos Minarik
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  2. Lukas Sekanina
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Corresponding author

Correspondence to Milos Minarik .

Editor information

Editors and Affiliations

  1. University College Dublin , Dublin, Ireland

    James McDermott

  2. Universidade Nova de Lisboa , Lisbon, Portugal

    Mauro Castelli

  3. Brno University of Technology , Brno, Czech Republic

    Lukas Sekanina

  4. Vrije Universiteit Amsterdam , Amsterdam, The Netherlands

    Evert Haasdijk

  5. University of Cádiz , Cádiz, Spain

    Pablo García-Sánchez

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Minarik, M., Sekanina, L. (2017). On Evolutionary Approximation of Sigmoid Function for HW/SW Embedded Systems. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2017. Lecture Notes in Computer Science(), vol 10196. Springer, Cham. https://doi.org/10.1007/978-3-319-55696-3_22

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  • DOI: https://doi.org/10.1007/978-3-319-55696-3_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55695-6

  • Online ISBN: 978-3-319-55696-3

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