conifer 0.4

BDT Inference for FPGAs

Conifer translates trained Boosted Decision Trees to FPGA firmware for extreme low latency inference.

Installation

Conifer is on the Python Package Index, so install it like:

pip install conifer

About

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.

Usage

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.