Publication Details
LGBM2VHDL: Mapping of LightGBM Models to FPGA
Kořenek Jan, doc. Ing., Ph.D. (DCSY)
Čejka Tomáš, doc. Ing., Ph.D. (SSDIT)
Gradient Boosting; LightGBM; Hardware acceleration; 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.
@inproceedings{BUT193289,
author="Tomáš {Martínek} and Jan {Kořenek} and Tomáš {Čejka}",
title="LGBM2VHDL: Mapping of LightGBM Models to FPGA",
booktitle="2024 IEEE 32nd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)",
year="2024",
pages="97--103",
publisher="IEEE Computer Society",
address="Orlando, FL",
doi="10.1109/FCCM60383.2024.00020",
isbn="979-8-3503-7243-4"
}