Go to here for official releases on Github.
v0.3.0
- 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
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 report
command to gather HLS build reports,hls4ml build -l
for Logic Synthesis- Support for all-in-one Keras's
.h5
files (obtained with Keras'ssave()
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)