Scalable Full Hardware Logic Architecture for Gradient Boosted Tree Training

摘要

Gradient Boosted Tree is most effective and standard machine learning algorithm in many fields especially with various type of tabular dataset. Besides, recent industry field and robotics field require high-speed, power efficient and real-time training with enormous data. FPGA is effective device which enable custom domain specific approach to give acceleration as well as power efficiency. We introduce a scalable full hardware implementation of Gradient Boosted Tree training with high performance and flexibility of hyper parameterization. Experimental work shows that our hardware implementation achieved 11–33 times faster than state-of-art GPU acceleration even with small gates and low power FPGA device.