Release Notes

See here for official releases on Github.

v0.6.0 / coris

## What’s Changed * VivadoAccelerator backend: target pynq-z2 and zcu102 boards directly from hls4ml by @nicologhielmetti * Updated PyTorch and ONNX converters by @Duchstf * line_buffer Conv2D implementation for io_stream: reduced resource usage and latency by @Keb-L, @violatingcp, @vloncar * Support QConv2DBatchnorm layer from QKeras by @nicologhielmetti * Improved profiling plots - easier to compare original vs hls4ml converted models by @maksgraczyk * Better derivation of data types for QKeras models by @jmduarte, @thesps * Improved CI by @thesps * More support for models with branches, skip connections, Merge and Concatenate layers by @jmduarte, @vloncar * Support for Dense layers over multi-dimensional tensors by @vloncar * Overall improvements by @vloncar, @jmduarte, @thesps, @jmitrevs & others

Full Changelog: https://github.com/fastmachinelearning/hls4ml/compare/v0.5.0…v0.6.0

v0.5.0 / bartsia

What’s new:

Streaming IO layer implementations, especially of Convolutional layers, accessed through the config with IOType: io_stream. Scales CNN support to much larger models than previously possible (see arXiv:2101.05108)
New documentation and API reference
Further optimizations for QKeras / quantization aware training. A ‘shift’ operation is now used for po2 quantizers
Allow redefinition of weights directory for standalone project compilation
profiling for PyTorch models

Deprecated:

IOType : io_serial is deprecated, and superceded by new IOType: io_stream

Bugfixes:

Fix to Initiation Interval and different min/max latency for Strategy: Resource
Fix warnings in hls4ml command line script flow
Write yml config from Python API - for mixed API / command line flow

v0.4.0 / aster

What’s new:

Support for GarNet layer (see arXiv:2008.03601)
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 SkipOptimizer 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

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 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)