Status and Features

Status

The latest version (built from main) is 0.9.0.dev93+gc320f507. The stable version (released on PyPI) is 0.8.1. 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 (limited)

  • (Q)ONNX (in development)

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 (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

MLP

supported

limited

in development

supported

supported

experimental

CNN

supported

limited

in development

supported

supported

experimental

RNN (LSTM)

supported

N/A

in development

supported

supported

N/A

GNN (GarNet)

supported

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

  • 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.