Parallel Multi-Objective Evolutionary Design of Approximate Circuits

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F15%3APU116965" target="_blank" >RIV/00216305:26230/15:PU116965 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.fit.vutbr.cz/research/pubs/all.php?id=10815" target="_blank" >http://www.fit.vutbr.cz/research/pubs/all.php?id=10815</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2739480.2754785" target="_blank" >10.1145/2739480.2754785</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Parallel Multi-Objective Evolutionary Design of Approximate Circuits

Popis výsledku v původním jazyce

Evolutionary design of digital circuits has been well established in recent years. Besides correct functionality, the demands placed on current circuits include the area of the circuit and its power consumption. By relaxing the functionality requirement, one can obtain more efficient circuits in terms of the area or power consumption at the cost of an error introduced to the output of the circuit. As a result, a variety of trade-offs between error and efficiency can be found. In this paper, a multiobjective evolutionary algorithm for the design of approximate digital circuits is proposed. The scalability of the evolutionary design has been recently improved using parallel implementation of the fitness function and by employing spatially structured evolutionary algorithms. The proposed multiobjective approach uses Cartesian Genetic Programming for the circuit representation and a modified NSGA-II algorithm. Multiple isolated islands are evolving in parallel and the populations are periodically merged and new populations are distributed across the islands. The method is evaluated in the task of approximate arithmetical circuits design.

Název v anglickém jazyce

Parallel Multi-Objective Evolutionary Design of Approximate Circuits

Popis výsledku anglicky

Evolutionary design of digital circuits has been well established in recent years. Besides correct functionality, the demands placed on current circuits include the area of the circuit and its power consumption. By relaxing the functionality requirement, one can obtain more efficient circuits in terms of the area or power consumption at the cost of an error introduced to the output of the circuit. As a result, a variety of trade-offs between error and efficiency can be found. In this paper, a multiobjective evolutionary algorithm for the design of approximate digital circuits is proposed. The scalability of the evolutionary design has been recently improved using parallel implementation of the fitness function and by employing spatially structured evolutionary algorithms. The proposed multiobjective approach uses Cartesian Genetic Programming for the circuit representation and a modified NSGA-II algorithm. Multiple isolated islands are evolving in parallel and the populations are periodically merged and new populations are distributed across the islands. The method is evaluated in the task of approximate arithmetical circuits design.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA14-04197S" target="_blank" >GA14-04197S: Pokročilé metody evolučního návrhu složitých číslicových obvodů</a><br>

Návaznosti

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

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

GECCO '15 Proceedings of the 2015 conference on Genetic and evolutionary computation

ISBN

978-1-4503-3472-3

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

687-694

Název nakladatele

Association for Computing Machinery

Místo vydání

New York

Místo konání akce

Madrid

Datum konání akce

11. 7. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000358795700087