hls4ml | Keras Ecosystem Directory

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

hls4ml - bartsia

Published by thesps over 3 years ago

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IOType : io_serial is deprecated, and superceded by new IOType: io_stream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

hls4ml -

Published by thesps almost 4 years ago

Pre-release of hls4ml version v0.5.0.

What's new:

Streaming IO layer implementations, especially of Convolutional layers, accessed through the config with io_type: io_stream. Scales CNN support to much larger models than previously possible (see paper)
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

hls4ml - aster

Published by thesps almost 4 years ago

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

hls4ml - v0.3.0

Published by thesps about 4 years ago

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

hls4ml - v0.2.0

Published by thesps over 4 years ago

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

hls4ml - v0.1.6

Published by vloncar over 4 years ago

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

hls4ml - v0.1.5

Published by jmduarte about 5 years ago

hls4ml - v0.1.2

Published by benjaminkreis over 6 years ago

Update license

hls4ml - v0.1.1

Published by jmduarte over 6 years ago

second beta version: fixed README

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