Status and Features

Status

The latest version (built from main) is 1.2.0.dev3+g2366f9bb. The stable version (released on PyPI) is 1.1.0. 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 the following platforms. Newer versions might work just fine, but try at your own risk.

    • Vivado HLS versions 2018.2 to 2020.1

    • Intel HLS versions 20.1 to 21.4, versions > 21.4 have not been tested.

    • Vitis HLS versions 2022.2 to 2024.1. Versions <= 2022.1 are known not to work.

    • Catapult HLS versions 2024.1_1 to 2024.2

    • oneAPI versions 2024.1 to 2025.0

  • hls4ml supports Linux and requires python >=3.10. hlsml does not require a specific Linux distribution version and we recommended to follow the requirements of the HLS tool you are using.

  • Windows and macOS are not supported. Setting up hls4ml on these platforms, for example using the Windows Subsystem for Linux (WSL) should be possible, but we do not provide support for such use cases.

  • 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.