Hardware Acceleration of Sparse Oblique Decision Trees for Edge Computing

Abstract

This paper presents a hardware accelerator for sparse decision trees intended for FPGA applications. To the best of authors’ knowledge, this is the first accelerator of this type. Beside the hardware accelerator itself, a novel algorithm for induction of sparse decision trees is also presented. Sparse decision trees can be attractive because they require less memory resources and can be more efficiently processed using specialized hardware compared to traditional oblique decision trees. This can be of significant interest, particularly, in the edge-based applications, where memory and compute resources as well as power consumption are severely constrained. The performance of the proposed sparse decision tree induction algorithm as well as developed hardware accelerator are studied using standard benchmark datasets obtained from the UCI Machine Learning Repository database. The results of the experimental study indicate that the proposed algorithm and hardware accelerator are very favourably compared with some of the existing solutions.