Detail publikace

Evolutionary Approach to Approximate Digital Circuits Design

Originální název

Evolutionary Approach to Approximate Digital Circuits Design

Anglický název

Evolutionary Approach to Approximate Digital Circuits Design

Jazyk

en

Originální abstrakt

In approximate computing, the requirement of perfect functional behavior can be relaxed because some applications are inherently error resilient. Approximate circuits, which fall into the approximate computing paradigm, are designed in such a way that they do not fully implement the logic behavior given by the specification and hence their accuracy can be exchanged for lower area, delay or power consumption.  In order to automate the design process, we propose to evolve approximate digital circuits which show a minimal error for a supplied amount of resources. The design process which is based on Cartesian Genetic Programming (CGP) can be repeated many times in order to obtain various tradeoffs between the accuracy and area. A heuristic seeding mechanism is introduced to CGP which allows for  improving not only the quality of evolved circuits, but also reducing the time of evolution. The efficiency of the proposed method is evaluated for the gate as well as the functional level evolution. In particular, approximate multipliers and median circuits which show very good parameters in comparison with other available implementations were constructed by means of the proposed method. 

Anglický abstrakt

In approximate computing, the requirement of perfect functional behavior can be relaxed because some applications are inherently error resilient. Approximate circuits, which fall into the approximate computing paradigm, are designed in such a way that they do not fully implement the logic behavior given by the specification and hence their accuracy can be exchanged for lower area, delay or power consumption.  In order to automate the design process, we propose to evolve approximate digital circuits which show a minimal error for a supplied amount of resources. The design process which is based on Cartesian Genetic Programming (CGP) can be repeated many times in order to obtain various tradeoffs between the accuracy and area. A heuristic seeding mechanism is introduced to CGP which allows for  improving not only the quality of evolved circuits, but also reducing the time of evolution. The efficiency of the proposed method is evaluated for the gate as well as the functional level evolution. In particular, approximate multipliers and median circuits which show very good parameters in comparison with other available implementations were constructed by means of the proposed method. 

BibTex


@article{BUT119783,
  author="Zdeněk {Vašíček} and Lukáš {Sekanina}",
  title="Evolutionary Approach to Approximate Digital Circuits Design",
  annote="
In approximate computing, the requirement of perfect functional behavior can be
relaxed because some applications are inherently error resilient. Approximate
circuits, which fall into the approximate computing paradigm, are designed in
such a way that they do not fully implement the logic behavior given by the
specification and hence their accuracy can be exchanged for lower area, delay or
power consumption.  In order to automate the design process, we propose to evolve
approximate digital circuits which show a minimal error for a supplied amount of
resources. The design process which is based on Cartesian Genetic Programming
(CGP) can be repeated many times in order to obtain various tradeoffs between the
accuracy and area. A heuristic seeding mechanism is introduced to CGP which
allows for  improving not only the quality of evolved circuits, but also reducing
the time of evolution. The efficiency of the proposed method is evaluated for the
gate as well as the functional level evolution. In particular, approximate
multipliers and median circuits which show very good parameters in comparison
with other available implementations were constructed by means of the proposed
method. ",
  address="NEUVEDEN",
  chapter="119783",
  doi="10.1109/TEVC.2014.2336175",
  edition="NEUVEDEN",
  howpublished="print",
  institution="NEUVEDEN",
  number="3",
  volume="19",
  year="2015",
  month="june",
  pages="432--444",
  publisher="NEUVEDEN",
  type="journal article in Web of Science"
}