An FPGA Implementation of Decision Tree Classification

Abstract

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 5.58times performance improvement over an equivalent software implementation