Scalable Hardware Architecture for fast Gradient Boosted Tree Training

Abstract

Gradient Boosted Tree is a powerful machine learning method that supports both classification and regression, and is widely used in fields requiring high-precision prediction, particularly for various types of tabular data sets. Owing to the recent increase in data size, the number of attributes, and the demand for frequent model updates, a fast and efficient training is required. FPGA is suitable for acceleration with power efficiency because it can realize a domain specific hardware architecture; however it is necessary to flexibly support many hyper-parameters to adapt to various dataset sizes, dataset properties, and system limitations such as memory capacity and logic capacity. We introduce a fully pipelined hardware implementation of Gradient Boosted Tree training and a design framework that enables a versatile hardware system description with high performance and flexibility to realize highly parameterized machine learning models. Experimental results show that our FPGA implementation achieves a 11- to 33-times faster performance and more than 300-times higher power efficiency than a state-of-the-art GPU accelerated software implementation.