A High-Performance and Flexible FPGA Inference Accelerator for Decision Forests Based on Prior Feature Space Partitioning

Recent studies have demonstrated the potential of FPGAs for accelerating the inference computation of decision forests (DFs). However, designing a high-performance architecture that is flexible enough to be adopted in various scenarios of FPGA resource requirements remains a challenge. To address this, we propose a DF inference method that makes a transformation from traversing trees into traversing feature spaces. Specifically, as a preprocessing step, we partition each feature space into multiple regions based on thresholds. The inference task for an input data point is then conducted by (1) determining which region in each feature space the data point belongs to and (2) combining the inference information in these regions. The regularity of the computation allows us to design a DF inference architecture, called FT-DFP (Feature-space Traversing Decision Forest Processor), that can be flexibly configured for different performance and FPGA resource usage requirements. We prototype FT-DFP on a low-end FPGA (Artix-7) board and evaluate it using four real-world datasets. The evaluation results show that (1) the flexibility of FT-DFP allows us to fit a wide variety of DF models into low-end FPGA devices with limited resources; (2) FT-DFP's performance is comparable to the best of existing accelerators implemented on high-end FPGA devices and 3.04x higher than Hummingbird, a state-of-the-art GPU-optimized implementation, running on a high-end GPU; and (3) FT-DFP is 130.96 x more energy-efficient than Hummingbird.