Coping with Resource Fluctuations: The Run-time Reconfigurable Functional Unit Row Classifier Architecture
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{kn-ka-gl-10a,
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author = "Tobias Knieper and Paul Kaufmann and Kyrre Glette and
Marco Platzner and Jim Torresen",
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title = "Coping with Resource Fluctuations: The Run-time
Reconfigurable Functional Unit Row Classifier
Architecture",
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booktitle = "Evolvable Systems: From Biology to Hardware: 9th
International Conference, ICES 2010",
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year = "2010",
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editor = "G. Tempesti and A. M. Tyrrell and J. F. Miller",
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series = "LNCS",
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volume = "6274",
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pages = "250--261",
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address = "York, UK",
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month = sep # " 6-8",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-15323-5",
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DOI = "doi:10.1007/978-3-642-15323-5_22",
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abstract = "The evolvable hardware paradigm facilitates the
construction of autonomous systems that can adapt to
environmental changes and degrading effects in the
computational resources. Extending these scenarios, we
study the capability of evolvable hardware classifiers
to adapt to intentional run-time fluctuations in the
available resources, i.e., chip area, in this work. To
that end, we leverage the Functional Unit Row (FUR)
architecture, a coarse-grained reconfigurable
classifier, and apply it to two medical benchmarks, the
Pima and Thyroid data sets from the UCI Machine
Learning Repository. We show that FUR's classification
performance remains high during changes of the chip
area in use and that performance drops are quickly
compensated for. Additionally, we demonstrate that FUR
recovery capability benefits from extra resources.",
- }
Genetic Programming entries for
Tobias Knieper
Paul Kaufmann
Kyrre Harald Glette
Marco Platzner
Jim Torresen
Citations