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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
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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 | ||
| Appears in Collections |
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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