An FPGA implementation of decision tree classification

Ramanathan Narayanan*, Daniel Honbo, Gokhan Memik, Alok Nidhi Choudhary, Joseph Zambreno

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

45 Scopus citations


Data mining techniques are a rapidly emerging class of applications that have widespread use in several fields. One important problem in data mining is Classification, which is the task of assigning objects to one of several predefined categories. Among the several solutions developed, Decision Tree Classification (DTC) is a popular method that yields high accuracy while handling large datasets. However, DTC is a computationally intensive algorithm, and as data sizes increase, its running time can stretch to several hours. In this paper, we propose a hardware implementation of Decision Tree Classification. We identify the compute-intensive kernel (Gini Score computation) in the algorithm, and develop a highly efficient architecture, which is further optimized by reordering the computations and by using a bitmapped data structure. Our implementation on a Xilinx Virtex-II Pro FPGA platform (with 16 Gini units) provides up to 5.58× performance improvement over an equivalent software implementation.

Original languageEnglish (US)
Title of host publicationProceedings - 2007 Design, Automation and Test in Europe Conference and Exhibition, DATE 2007
Number of pages6
StatePublished - 2007
Event2007 Design, Automation and Test in Europe Conference and Exhibition - Nice Acropolis, France
Duration: Apr 16 2007Apr 20 2007

Publication series

NameProceedings -Design, Automation and Test in Europe, DATE
ISSN (Print)1530-1591


Other2007 Design, Automation and Test in Europe Conference and Exhibition
CityNice Acropolis

ASJC Scopus subject areas

  • Engineering(all)

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