Status and Features

Status

The latest version (built from main) is 1.1.0.dev3+g47939cb3. The stable version (released on PyPI) is 1.0.0. See the Release Notes section for a changelog.

Features

A list of supported ML frameworks, HLS backends, and neural network architectures, including a summary table is below. Dependencies are given in the Setup page.

ML framework support:

  • (Q)Keras

  • PyTorch

  • (Q)ONNX

Neural network architectures:

  • Fully connected NN (multilayer perceptron, MLP)

  • Convolutional NN

  • Recurrent NN (LSTM)

  • Graph NN (GarNet)

HLS backends:

  • Vivado HLS

  • Intel HLS

  • Vitis HLS

  • Catapult HLS

  • oneAPI (experimental)

A summary of the on-going status of the hls4ml tool is in the table below.

ML framework/HLS backend

(Q)Keras

PyTorch

(Q)ONNX

Vivado HLS

Intel HLS

Vitis HLS

Catapult HLS

oneAPI

MLP

supported

supported

supported

supported

supported

supported

supported

experimental

CNN

supported

supported

supported

supported

supported

supported

supported

experimental

RNN (LSTM)

supported

supported

N/A

supported

supported

supported

supported

experimental

GNN (GarNet)

supported

in development

N/A

N/A

N/A

N/A

N/A

N/A

Other feature notes:

  • hls4ml is tested on Linux, and supports

    • Vivado HLS versions 2018.2 to 2020.1

    • Intel HLS versions 20.1 to 21.4

    • Vitis HLS versions 2022.2 to 2024.1

    • Catapult HLS versions 2024.1_1 to 2024.2

    • oneAPI versions 2024.1 to 2025.0

  • Windows and macOS are not supported

  • BDT support has moved to the Conifer package

Example Models

We also provide and document several example hls4ml models in this GitHub repository, which is included as a submodule. You can check it out by doing git submodule update --init --recursive from the top level directory of hls4ml.