Setup and Quick Start

Getting started with hls4ml is very easy. There are several installation options available and once installed, it takes only a few lines of code to run your first synthesis.

Installation

The latest release of hls4ml can be installed with pip:

pip install hls4ml

If you want to use our profiling toolbox, you might need to install extra dependencies:

pip install hls4ml[profiling]

Warning

Previously, versions of hls4ml were made available on conda-forge. These are outdated and should NOT be used. Installing with pip is currently the only supported method.

Development version

hls4ml is rapidly evolving and many experimental features and bugfixes are available on the development branch. Development version can be installed directly from git:

pip install git+https://github.com/fastmachinelearning/hls4ml@main

Dependencies

Note

As of version 1.1.0+, all conversion frontend specific packages are optional. Only install the packages you need.

The hls4ml library requires python 3.10 or later, and depends on a number of Python packages and external tools for synthesis and simulation. Python dependencies are automatically managed by pip or conda.

The following Python packages are all optional and are only required if you intend to use the corresponding converter.

  • Keras is required by the Keras converter.

    • TensorFlow (version 2.8 to 2.14) is required by the Keras v2 converter (keras v2 is included in TensorFlow).

    • Keras <https://pypi.org/project/keras/> 3.0 or above is required by the Keras v3 converter. Keras v3 supports multiple backends for training and inference, and the conversion is not tied any specific backend. Notice that Keras v3 may not coexist with Keras v2 in the same Python environment.

  • ONNX (version 1.4.0 and newer) is required by the ONNX converter.

  • PyTorch is required by the PyTorch converter.

  • Quantization support

Running C simulation from Python requires a C++11-compatible compiler. On Linux, a GCC C++ compiler g++ is required. Any version from a recent Linux should work. On MacOS, the clang-based g++ is enough. For the oneAPI backend, one must have oneAPI installed, along with the FPGA compiler, to run C/SYCL simulations.

To run FPGA synthesis, installation of following tools is required:

  • Xilinx Vivado HLS 2018.2 to 2020.1 for synthesis for Xilinx FPGAs using the Vivado backend.

  • Vitis HLS 2022.2 or newer is required for synthesis for Xilinx FPGAs using the Vitis backend.

  • Intel Quartus 20.1 to 21.4 for the synthesis for Intel/Altera FPGAs using the Quartus backend.

  • oneAPI 2024.1 to 2025.0 with the FPGA compiler and recent Intel/Altera Quartus for Intel/Altera FPGAs using the oneAPI backend.

Catapult HLS 2024.1_1 or 2024.2 can be used to synthesize both for ASICs and FPGAs.

Quick Start

For basic concepts to understand the tool, please visit the Concepts chapter. Here we give line-by-line instructions to demonstrate the general workflow.

import hls4ml
import tensorflow as tf
from tensorflow.keras.layers import Dense

# Construct a basic keras model
model = tf.keras.models.Sequential()
model.add(Dense(64, input_shape=(16,), name='Dense', kernel_initializer='lecun_uniform', kernel_regularizer=None))
model.add(Activation(activation='elu', name='Activation'))
model.add(Dense(32, name='Dense2', kernel_initializer='lecun_uniform', kernel_regularizer=None))
model.add(Activation(activation='elu', name='Activation2'))

# This is where you would train the model in a real-world scenario

# Generate an hls configuration from the keras model
config = hls4ml.utils.config_from_keras_model(model)

# You can print the config to see some default parameters
print(config)

# Convert the model to an hls project using the config
hls_model = hls4ml.converters.convert_from_keras_model(
   model=model,
   hls_config=config,
   backend='Vitis'
)

Once converted to an HLS project, you can connect the project into the Python runtime and use it to run predictions on a numpy array:

import numpy as np

# Compile the hls project and link it into the Python runtime
hls_model.compile()

# Generate random input data
X_input = np.random.rand(100, 16)

# Run the model on the input data
hls_prediction = hls_model.predict(X_input)

After that, you can use Vitis HLS to synthesize the model:

# Use Vitis HLS to synthesize the model
# This might take several minutes
hls_model.build()

# Optional: print out the report
hls4ml.report.read_vivado_report('my-hls-test')

Done! You’ve built your first project using hls4ml! To learn more about our various API functionalities, check out our tutorials here.

If you want to configure your model further, check out our Configuration page.

Existing examples

  • Training codes and examples of resources needed to train the models can be found in the tutorial.

  • Examples of model files and weights can be found in example_models directory.

Uninstalling

To uninstall hls4ml:

pip uninstall hls4ml

If installed with conda, remove the package with:

conda remove hls4ml