Publication detail

Parallel Multi-Objective Evolutionary Design of Approximate Circuits

HRBÁČEK, R.

Original Title

Parallel Multi-Objective Evolutionary Design of Approximate Circuits

Type

conference paper

Language

English

Original Abstract

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.

Keywords

Cartesian Genetic Programming, Parallel Evolutionary Al- gorithms, Multi-objective Optimization, Cluster, Combina- tional Circuit Design, Approximate Circuits

Authors

HRBÁČEK, R.

RIV year

2015

Released

11. 7. 2015

Publisher

Association for Computing Machinery

Location

New York

ISBN

978-1-4503-3472-3

Book

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

Pages from

687

Pages to

694

Pages count

8

URL

https://www.fit.vut.cz/research/publication/10815/

BibTex

@inproceedings{BUT119822,
  author="Radek {Hrbáček}",
  title="Parallel Multi-Objective Evolutionary Design of Approximate Circuits",
  booktitle="GECCO '15 Proceedings of the 2015 conference on Genetic and evolutionary computation",
  year="2015",
  pages="687--694",
  publisher="Association for Computing Machinery",
  address="New York",
  doi="10.1145/2739480.2754785",
  isbn="978-1-4503-3472-3",
  url="https://www.fit.vut.cz/research/publication/10815/"
}