A Discretization Method for Floating-Point Number in FPGA-based Decision Tree Accelerator
Shuang Zhao, Yipin Sun, Shuhui Chen · 2018
Decision tree is one of the most popular supervised machine learning algorithms. Due to the rapid increase in the amount of data, many application scenarios require higher classification speed. Therefore, a variety of decision tree classification acceleration algorithms based on FPGA are proposed. These methods focus on improving the classification speed by designing effective pipeline architecture. However, the impact of floating-point numbers on storage and computing resources in the hardware implementation of the decision tree is ignored. In this paper, we present a discretization method for floating-point numbers in decision tree model by converting the floating-point numbers into integers. This method reduces the storage and computing resources required by the hardware implementation of decision tree, without affecting the classification performance of the classifier.