Go to here for official releases on Github.

v0.2.0:

  • 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.


v0.1.6:

  • 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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