Publication Details

Parallel Optimization of Transistor Level Circuits using Cartesian Genetic Programming

MRÁZEK, V.; VAŠÍČEK, Z. Parallel Optimization of Transistor Level Circuits using Cartesian Genetic Programming. In GECCO Companion '17 Proceedings of the Companion Publication of the 2017 on Genetic and Evolutionary Computation Conference. Berlin: Association for Computing Machinery, 2017. p. 1849-1856. ISBN: 978-1-4503-4939-0.
Czech title
Paralelní optimalizace obvodů popsaných na úrovni tranzistorů pomocí kartézského genetického programování
Type
conference paper
Language
English
Authors
Keywords

Evolutionary optimization, transistor-level, parallel systems, digital circuits 

Abstract

The aim of the paper is to introduce a new parallel approach to evolutionary optimization of digital circuits described on transistor level. The evolutionary optimization is guided by the fitness function employing a simulator of candidate circuits. A new discrete simulator was introduced to achieve a good trade-off between precision and cost of circuit evaluations. The simulator is based on event-driven simulation. Precise numeric SPICE simulator is regularly called to validate simulation results. To increase the speed of evolution, three parallel approaches were proposed: (i) thread level parallelism, (ii) multiple computing nodes which collectively communicate and distribute the best solution, and (iii) client-server architecture eliminating a limited count of SPICE simulator instances.

Published
2017
Pages
1849–1856
Proceedings
GECCO Companion '17 Proceedings of the Companion Publication of the 2017 on Genetic and Evolutionary Computation Conference
ISBN
978-1-4503-4939-0
Publisher
Association for Computing Machinery
Place
Berlin
DOI
UT WoS
000625865500312
EID Scopus
BibTeX
@inproceedings{BUT144422,
  author="Vojtěch {Mrázek} and Zdeněk {Vašíček}",
  title="Parallel Optimization of Transistor Level Circuits using Cartesian Genetic Programming",
  booktitle="GECCO Companion '17 Proceedings of the Companion Publication of the 2017 on Genetic and Evolutionary Computation Conference",
  year="2017",
  pages="1849--1856",
  publisher="Association for Computing Machinery",
  address="Berlin",
  doi="10.1145/3067695.3084212",
  isbn="978-1-4503-4939-0",
  url="https://www.fit.vut.cz/research/publication/11377/"
}
Back to top