Conference paper

MRÁZEK Vojtěch and VAŠÍČEK Zdeněk. Evolutionary Design of Large Approximate Adders Optimized for Various Error Criteria. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '18). Kyoto: Association for Computing Machinery, 2018, pp. 294-295. ISBN 978-1-4503-5764-7.
Publication language:english
Original title:Evolutionary Design of Large Approximate Adders Optimized for Various Error Criteria
Title (cs):Evoluční návrh velkých aproximačních sčítaček optimalizovaných pro různé chyby
Pages:294-295
Proceedings:Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '18)
Conference:Genetic and Evolutionary Computations Conference 2018
Place:Kyoto, JP
Year:2018
ISBN:978-1-4503-5764-7
DOI:10.1145/3205651.3205678
Publisher:Association for Computing Machinery
Keywords
Approximate computing, genetic algorithm, equivalence checking
Annotation
As a promising approach to the design of energy efficient circuits, approximate circuits and approximate circuit design methodologies have attracted a significant attention of researchers as well as industry. Compared to the traditional design methods, it has been demonstrated that evolutionary approaches are able to discover approximate circuits exhibiting a good trade-off between the energy consumption and circuit quality. In this work, evolutionary design of large approximate adders is addressed. In order to improve scalability, the quality of the candidate solutions is analysed using a formal approach based on Binary Decision Diagrams. Compared to the common approach based on a parallel circuit simulator, the proposed method is able to evaluate 2-3 orders of magnitude more generations.
BibTeX:
@INPROCEEDINGS{
   author = {Vojt{\v{e}}ch Mr{\'{a}}zek and Zden{\v{e}}k
	Va{\v{s}}{\'{i}}{\v{c}}ek},
   title = {Evolutionary Design of Large Approximate Adders
	Optimized for Various Error Criteria},
   pages = {294--295},
   booktitle = {Proceedings of the Genetic and Evolutionary Computation
	Conference Companion (GECCO '18)},
   year = {2018},
   location = {Kyoto, JP},
   publisher = {Association for Computing Machinery},
   ISBN = {978-1-4503-5764-7},
   doi = {10.1145/3205651.3205678},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en?id=11637}
}

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