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Machine learning in FPGAs using HLS

Project description


DOI PyPI version Supported Python versions

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!


Documentation & Tutorial

For more information visit the webpage:

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]

Getting Started

Creating an HLS project

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

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

#Print out the report if you want'my-hls-test')

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