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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU123658" target="_blank" >RIV/00216305:26230/17:PU123658 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.fit.vutbr.cz/research/pubs/all.php?id=11298" target="_blank" >http://www.fit.vutbr.cz/research/pubs/all.php?id=11298</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-55696-3_22" target="_blank" >10.1007/978-3-319-55696-3_22</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20206 - Computer hardware and architecture

Projekt

<a href="/cs/project/GA16-17538S" target="_blank" >GA16-17538S: Přibližná ekvivalence pro aproximativní počítání</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

20th European Conference on Genetic Programming, EuroGP 2017

ISBN

978-3-319-55696-3

ISSN

—

e-ISSN

—

Počet stran výsledku

16

Strana od-do

343-358

Název nakladatele

Springer International Publishing

Místo vydání

Berlin

Místo konání akce

Amsterdam

Datum konání akce

19. 4. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000413012200022