Automatic Design of Arbitrary-Size Approximate Sorting Networks with Error Guarantee
Created by W.Langdon from
gp-bibliography.bib Revision:1.8051
- @InProceedings{Mrazek:2016:PATMOS,
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author = "Vojtech Mrazek and Zdenek Vasicek",
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title = "Automatic Design of Arbitrary-Size Approximate Sorting
Networks with Error Guarantee",
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booktitle = "International Workshop on Power And Timing Modeling,
Optimization and Simulation",
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year = "2016",
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editor = "Ricardo Reis and Aida Todri-Sanial",
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pages = "221--228",
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address = "Bremen, Germany",
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month = sep # " 21-23",
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, approximate computing, sorting
networks",
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URL = "http://www.fit.vutbr.cz/~vasicek/pubs.php?id=11175",
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DOI = "doi:10.1109/PATMOS.2016.7833691",
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size = "8 pages",
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abstract = "Despite the fact that hardware sorters offer great
performance, they become expensive as the number of
inputs increases. In order to address the problem of
high-performance and power-efficient computing, we
propose a scalable method for construction of
power-efficient sorting networks suitable for hardware
implementation. The proposed approach exploits the
error resilience which is present in many real-world
applications such as digital signal processing,
biological computing and large-scale scientific
computing. The method is based on recursive
construction of large sorting networks using smaller
instances of approximate sorting networks. The design
process is tunable and enables to achieve desired
tradeoffs between the accuracy and power consumption or
implementation cost. A search-based design method is
used to obtain approximate sorting networks. To measure
and analyse the accuracy of approximate networks, three
data-independent quality metrics are proposed. Namely,
guarantee of error probability, worst-case error and
error distribution are discussed. A significant
improvement in the implementation cost and power
consumption was obtained. For example, 20percent
reduction in power consumption was achieved by
introducing a small error in 256-input sorter. The
difference in rank is proved to be not worse than 2
with probability at least 99percent. In addition to
that, it is guaranteed that the worst-case difference
is equal to 6.",
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notes = "http://www.item.uni-bremen.de/patmos/
",
- }
Genetic Programming entries for
Vojtech Mrazek
Zdenek Vasicek
Citations