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Novel and highly efficient reconfigurable implementation of data mining classification tree

Chrysos Grigorios, Dagritzikos Panagiotis, Papaefstathiou Ioannis, Dollas Apostolos

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URI: http://purl.tuc.gr/dl/dias/7C5014ED-9FDF-4759-9E74-5CCA44408DA1
Year 2011
Type of Item Conference Publication
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Bibliographic Citation G. Chrysos, P. Dagritzikos, I. Papaefstathiou and A. Dollas, "Novel and highly efficient reconfigurable implementation of data mining classification tree," in International Conference on Field Programmable Logic and Applications, 2011, pp. 411-416. doi: 10.1109/FPL.2011.82 https://doi.org/10.1109/FPL.2011.82
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Summary

The available e-data throughout the Web are growing at such a high rate that data mining on the web is considered the biggest challenge of information technology. As a result it is crucial to find new and innovative ways for classifying and mining those huge amounts of data. In this paper we present an implementation of a state-of-the-art data mining algorithm on a modern FPGA. This is one of the first approaches utilizing the resources of an FPGA to accelerate certain very CPU intensive data-mining/data classification schemes and our real-world results from actual runs on hardware demonstrate that it is a highly promising one. In particular, our FPGA-based system achieves, depending on the data classified, a speedup from 4x and up to 50x (on average 25x) when compared with a state-of-the art multi-core CPU, including I/O overhead.

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