LGBM2VHDL: Mapping of LightGBM Models to FPGA

Gradient boosting (GB) is an effective and widely used type of ensemble machine-learning method. The opportunity to transform the trained GB models to the hardware level represents the potential for significant acceleration of many applications and their availability as embedded systems. In this work, we have therefore developed the LGBM2VHDL tool for the automated mapping of models trained by the LightGBM library to circuits described by VHDL. Compared to existing tools, we have used an architecture that is better suited for large-scale GB models involving up to thousands of decision trees. We have further optimized the architecture using two newly proposed techniques. By applying these techniques to the tested models, the amount of memory required was significantly reduced to almost half of the original resources, and the amount of basic configurable blocks was reduced by up to 4 times on average. The developed tool is available as open-source.