aster

What's new:

• Support for GarNet layer (see paper)
• Input layer precision added to config generator utility
• New 'SkipOptimizers' config option. Now you can run all Optimizers by default (as in v0.3.0) but subtract any specified by 'SkipOptimizers' e.g. hls_config['SkipOptimizers'] = ['fuse_consecutive_batch_normalization']
• Print out the latency report from Cosimulation

Bugfixes:

• Fixes related to tensorflow 2.3: new Functional API, changes to handling of Input layer
• Fix error with config generator utility and activation layers gor granularity='name'
• Fix issue with reloading of emulation library after configuration change
• Fix to handling of layers with use_bias=False and merged Dense and BatchNormalization

v0.3.0

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

v0.2.0

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

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

Merge pull request #141 from vloncar/precision

Per-layer precision and reuse factor

v0.1.2

Update license

v0.1.1

second beta version: fixed README