A package for machine learning inference in FPGAs. We create firmware implementations of machine learning algorithms using high level synthesis language (HLS). We translate traditional open-source machine learning package models into HLS that can be configured for your use-case!
If you have any questions, comments, or ideas regarding hls4ml or just want to show us how you use hls4ml, don't hesitate to reach us through the discussions tab.
For more information visit the webpage: https://fastmachinelearning.org/hls4ml/
Detailed tutorials on how to use hls4ml's various functionalities can be found here.
pip install hls4ml
To install the extra dependencies for profiling:
pip install hls4ml[profiling]
import hls4ml
#Fetch a keras model from our example repository
#This will download our example model to your working directory and return an example configuration file
config = hls4ml.utils.fetch_example_model('KERAS_3layer.json')
print(config) #You can print the configuration to see some default parameters
#Convert it to a hls project
hls_model = hls4ml.converters.keras_to_hls(config)
# Print full list of example models if you want to explore more
hls4ml.utils.fetch_example_list()Building a project with Xilinx Vivado HLS (after downloading and installing from here)
Note: Vitis HLS is not yet supported. Vivado HLS versions between 2018.2 and 2020.1 are recommended.
#Use Vivado HLS to synthesize the model
#This might take several minutes
hls_model.build()
#Print out the report if you want
hls4ml.report.read_vivado_report('my-hls-test')