Evolutionary design of complex approximate combinational circuits

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{Vasicek:2016:GPEM,

• author = "Zdenek Vasicek and Lukas Sekanina",
• title = "Evolutionary design of complex approximate combinational circuits",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2016",
• volume = "17",
• number = "2",
• pages = "169--192",
• month = jun,
• keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, approximate circuit, Binary decision diagram, Fitness function",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-015-9257-1",
• size = "24 pages",
• abstract = "Functional approximation is one of the methods allowing designers to approximate circuits at the level of logic behaviour. By introducing a suitable functional approximation, power consumption, area or delay of a circuit can be reduced if some errors are acceptable in a particular application. As the error quantification is usually based on an arithmetic error metric in existing approximation methods, these methods are primarily suitable for the approximation of arithmetic and signal processing circuits. This paper deals with the approximation of general logic (such as pattern matching circuits and complex encoders) in which no additional information is usually available to establish a suitable error metric and hence the error of approximation is expressed in terms of Hamming distance between the output values produced by a candidate approximate circuit and the accurate circuit. We propose a circuit approximation method based on Cartesian genetic programming in which gate-level circuits are internally represented using directed acyclic graphs. In order to eliminate the well-known scalability problems of evolutionary circuit design, the error of approximation is determined by binary decision diagrams. The method is analysed in terms of computational time and quality of approximation. It is able to deliver detailed Pareto fronts showing various compromises between the area, delay and error. Results are presented for 16 circuits (with 27-50 inputs) that are too complex to be approximated by means of existing evolutionary circuit design methods.",

}

Genetic Programming entries for Zdenek Vasicek Lukas Sekanina

Citations