Conifer translates trained Boosted Decision Trees to FPGA firmware for extreme low latency inference.
Conifer is on the Python Package Index, so install it like:
pip install conifer
Conifer converts from popular BDT training frameworks, and can emit code projects in different FPGA languages. Available converters:
- scikit-learn
- xgboost
- ONNX - giving access to other training libraries such as lightGBM and CatBoost with ONNXMLTools
- TMVA
Available backends:
- Xilinx HLS - for best results use latest Vitis HLS, but Vivado HLS is also supported (conifer uses whichever is on your
$PATH) - VHDL - a direct-to-VHDL implementation, deeply pipelined for high clock frequencies
- C++ - intended for bit-accurate emulation on CPU with a single include header file
See our paper in JINST: "Fast inference of Boosted Decision Trees in FPGAs for particle physics".
Conifer originated as a development for hls4ml, and borrows heavily from the code and ideas developed for it.
from sklearn.ensemble import GradientBoostingClassifier
# Train a BDT
clf = GradientBoostingClassifier().fit(X_train, y_train)
# Create a conifer config dictionary
cfg = conifer.backends.xilinxhls.auto_config()
# Change the bit precision (print the config to see everything modifiable)
cfg['Precision'] = 'ap_fixed<12,4>'
# Create the conifer model
model = conifer.model(clf, conifer.converters.sklearn,
conifer.backends.xilinxhls, cfg)
# Write the HLS project and compile the C++-Python bridge
model.compile()
# Run bit-accurate prediction on the CPU
y_hls = model.decision_function(X)
y_skl = clf.decision_function(X)
# Synthesize the model for the target FPGA
model.build()
Check the examples directory for examples to get started with, and the BDT part of the hls4ml tutorial.