Update README.md for v1.0.0 #1100
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7 tasks
bo3z
commented
Nov 12, 2024
docs/setup.rst
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| * Intel Quartus 20.1 to 21.4 for the synthesis for Intel FPGAs | ||
| * oneAPI 2024.1 to 2025.0 with the FPGA compiler and recent Intel/Altara Quartus for Intel/Altera FPGAs using the ``oneAPI`` backend. |
3 tasks
JanFSchulte
reviewed
Nov 18, 2024
docs/api/auto.rst
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| The approach taken by the precision inference is to set accumulator and other precisions to never truncate, using only the bitwidths of the inputs (not the values). This is quite conservative, | ||
| especially in cases where post-training quantization is used, or if the bit widths were set fairly loosely. The recommended action in that case is to edit the configuration and explicitly set | ||
| some widths in it, potentially in an iterative process after seeing what precisions are automatically set. Another option, currently implemented in :py:class:`~hls4ml.utils.config.config_from_keras_model`, |
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I think this can updated now since we added it to QONNX and pytorch as well?
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I updated this to not say that it's only for keras models.
JanFSchulte
reviewed
Nov 18, 2024
docs/api/configuration.rst
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| This will include per-layer configuration based on the model. Including the backend is recommended because some configation options depend on the backend. Note, the precisions at the | ||
| higher granularites usually default to 'auto', which means that ``hls4ml`` will try to set it automatically. Note that higher granularity settings take precendence | ||
| This will include per-layer configuration based on the model. Including the backend is recommended because some configuration options depend on the backend. Note, the precisions at the | ||
| higher granularites usually default to 'auto', which means that ``hls4ml`` will try to set it automatically (see :ref:`Automatic precision inference`). Note that higher granularity settings take precedence | ||
| over model-level settings. See :py:class:`~hls4ml.utils.config.config_from_keras_model` for more information on the various options. |
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Should we generalize this part of the documentation to not just feature the keras parser as an example?
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I attempted to do this, though feel free to edit.
jmitrevs
reviewed
Dec 4, 2024
| For more information visit the webpage: [https://fastmachinelearning.org/hls4ml/](https://fastmachinelearning.org/hls4ml/) | ||
| For more information visit the webpage: [https://fastmachinelearning.org/hls4ml/](https://fastmachinelearning.org/hls4ml/). | ||
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| For introductory material on FPGAs, HLS and ML inferences using hls4ml, check out the [video](https://www.youtube.com/watch?v=2y3GNY4tf7A&ab_channel=SystemsGroupatETHZ%C3%BCrich). |
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Does it make sense to also add this to the documentation, not just the readme?
…g/hls4ml into update-readme
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pre-commit.ci autofix |
JanFSchulte
approved these changes
Dec 9, 2024
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For a new feature or function, please create an issue first to discuss it with us before submitting a pull request.
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pre-commiton the files I edited or added.