Status and Features
Status
The latest stable release is v0.6.0. This release brings the new VivadoAccelerator backend to easily target boards like pynq-z2 and zcu102, with support for more boards like Alveo planned.
Features
A list of supported ML codes and architectures, including a summary table is below. Dependencies are given in the Setup page.
ML code support:
Keras/Tensorflow/QKeras, PyTorch, Onnx
Neural network architectures:
Fully Connected NNs (multi-layer perceptron)
Convolutional NNs (1D/2D)
Recurrent NN/LSTM, in prototyping
A summary of the on-going status of the hls4ml
tool is in the table below.
Architectures/Toolkits |
Keras/TensorFlow/QKeras |
PyTorch |
ONNX |
---|---|---|---|
MLP |
|
|
|
Conv1D/Conv2D |
|
|
|
RNN/LSTM |
|
|
|
Other feature notes:
hls4ml
is tested on Linux, and supports Vivado HLS versions 2018.2 to 2020.1. Vitis HLS is not yet supported. Windows and macOS are not supported.BDT support has moved to the Conifer package
Example Models
We also provide and documented several example models that have been implemented in hls4ml
in this Github repository.