Coping with Resource Fluctuations: The Run-time Reconfigurable Functional Unit Row Classifier Architecture

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{kn-ka-gl-10a,

• author = "Tobias Knieper and Paul Kaufmann and Kyrre Glette and Marco Platzner and Jim Torresen",
• title = "Coping with Resource Fluctuations: The Run-time Reconfigurable Functional Unit Row Classifier Architecture",
• booktitle = "Evolvable Systems: From Biology to Hardware: 9th International Conference, ICES 2010",
• year = "2010",
• editor = "G. Tempesti and A. M. Tyrrell and J. F. Miller",
• series = "LNCS",
• volume = "6274",
• pages = "250--261",
• address = "York, UK",
• month = sep # " 6-8",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-642-15323-5",
• DOI = "doi:10.1007/978-3-642-15323-5_22",
• 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