Detail publikace

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

MINAŘÍK, M. SEKANINA, L.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Sigmoid, Linear genetic programming, HW/SW co-design

Autoři

MINAŘÍK, M.; SEKANINA, L.

Vydáno

19. 4. 2017

Nakladatel

Springer International Publishing

Místo

Berlin

ISBN

978-3-319-55696-3

Kniha

20th European Conference on Genetic Programming, EuroGP 2017

Edice

Lecture Notes in Computer Science

Strany od

343

Strany do

358

Strany počet

16

URL

https://www.fit.vut.cz/research/publication/11298/

BibTex

@inproceedings{BUT135902,
  author="Miloš {Minařík} and Lukáš {Sekanina}",
  title="On Evolutionary Approximation of Sigmoid Function for HW/SW Embedded Systems",
  booktitle="20th European Conference on Genetic Programming, EuroGP 2017",
  year="2017",
  series="Lecture Notes in Computer Science",
  volume="10196",
  pages="343--358",
  publisher="Springer International Publishing",
  address="Berlin",
  doi="10.1007/978-3-319-55696-3\{_}22",
  isbn="978-3-319-55696-3",
  url="https://www.fit.vut.cz/research/publication/11298/"
}