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

## New Contributors * @siorpaes made their first contribution in https://github.com/fastmachinelearning/hls4ml/pull/424 * @jmitrevs made their first contribution in https://github.com/fastmachinelearning/hls4ml/pull/403 * @anders-wind made their first contribution in https://github.com/fastmachinelearning/hls4ml/pull/302 * @KOVI89alipes made their first contribution in https://github.com/fastmachinelearning/hls4ml/pull/318 * @maksgraczyk made their first contribution in https://github.com/fastmachinelearning/hls4ml/pull/323 * @Keb-L made their first contribution in https://github.com/fastmachinelearning/hls4ml/pull/332 * @ConsVin made their first contribution in https://github.com/fastmachinelearning/hls4ml/pull/307 * @nicologhielmetti made their first contribution in https://github.com/fastmachinelearning/hls4ml/pull/298

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  • IOType : io_serial is deprecated, and superceded by new IOType: io_stream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