Hls4ml

What's new:
- API expansion:
- 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 as a submodule with an API to retrieve them
- New Softmax implementations
- Minor fixes: weights exported at higher precision, concatenate layer shape corrected

What's new:
- `tf_to_hls`: convert tensorflow protobuf (`.pb`) models to HLS projects
- Support for Keras model `.h5` files (extending existing support for `.json` architecture + `.h5` weights format)
- Support larger Conv1D / 2D layers
- Support for binary and ternary layers from QKeras
- API enhancements for addition of custom layer and new backends
- Keras and HLS model profiling tool
- `hls4ml report` command to gather HLS build reports
- `hls4ml build -l` command to run logic synthesis
- Fused Batch Normalization and Dense layer optimization pass

- 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

Update license

second beta version: fixed README