Coping with resource fluctuations: the run-time reconfigurable functional unit row classifier architecture

Authors:
Tobias Knieper;Paul Kaufmann;Kyrre Glette;Marco Platzner;Jim Torresen
Affiliations:
University of Paderborn, Department of Computer Science, Paderborn, Germany;University of Paderborn, Department of Computer Science, Paderborn, Germany;University of Oslo, Department of Informatics, Oslo, Norway;University of Paderborn, Department of Computer Science, Paderborn, Germany;University of Oslo, Department of Informatics, Oslo, Norway
Venue:
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
Year:
2010

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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 utilized chip area and that performance drops are quickly compensated for. Additionally, we demonstrate that FUR's recovery capability benefits from extra resources.