A list of suppported ML codes and architectures, including a summary table is below. Dependences are given in a dedicated page.
ML code support:
- Keras/Tensorflow, PyTorch, scikit-learn
- Planned: xgboost
Neural network architectures:
- Fully Connected NNs (multi-layer perceptron)
- Boosted Decision Trees
- Convolutional NNs (1D/2D), in beta testing
- Recurrent NN/LSTM, in prototyping
A summary of the on-going status of the
hls4ml tool is in the table below.
Other random feature notes:
- There is a known Vivado HLS issue where the large loop unrolls create memory issues during synthesis. We are working to solve this issue but you may see errors related to this depending on the memory of your machine. Please feel free to email the
hls4mlteam if you have any further questions.
We also provide and documented several example models that have been implemented in
hls4ml in this Github repository.