Decision tree ensemble hardware accelerators for embedded applications

Abstract

This paper presents four different architectures for the hardware acceleration of axis-parallel, oblique and non-linear decision tree ensemble classifier systems. Hardware architectures for the implementation of a number of ensemble combination rules are also presented. The proposed architectures are optimized for size, making them particularly interesting for embedded applications where the size of the system is critical constraint. Proposed architectures are suitable for the implementation using FPGA and ASIC technology. Experiment results obtained using 29 datasets from the standard UCI Machine Learning Repository database suggest that the FPGA implementations offer significant improvement in the classification time in comparison with the pure software implementations.