Go to here for official releases on Github.


  • Installing from PyPI
  • Create configuration dictionary from model object
  • Run 'C Simulation' from Python with hls_model.predict(X)
  • Trace model layer output with hls_model.trace(X)
  • Write HLS project, run synthesis flow from Python
  • QKeras support: convert models trained using layers and quantizers from QKeras
  • Example models moved to separate repo, added API to retrieve them
  • New Softmax implementations
  • Minor fixes: weights exported at higher precision, concatenate layer shape corrected


  • tf_to_hls tool for converting tensorflow models (protobufs .pb)
  • Support for larger Conv1D/2D layers
  • Support for binary and ternary layers from QKeras.
  • API enhancements (custom layers, multiple backends)
  • Profiling support
  • hls4ml reportcommand to gather HLS build reports, hls4ml build -l for Logic Synthesis
  • Support for all-in-one Keras's .h5 files (obtained with Keras's save() function, without the need for separate .json and .h5 weight file).
  • Fused Batch Normalisation into Dense layer optimsation.


  • Support for larger Dense layers (enabled with Strategy: Resource in the configuration file)
  • Binary/Ternary NN refinements
  • Built-in optimization framework
  • Optional C/RTL validation

v0.1.5: Per-layer precision and reuse factor

v0.1.3: Adding PyTorch support

v0.1.2: First beta release

  • some bug fixes for pipelining and support for layer types

v0.0.2: first alpha release

  • full translation of DNNs from Keras
  • an example Conv1D exists
  • parallel mode is supported (serial mode, not yet)

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