Add doc for HGQ #1117
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Add doc for HGQ #1117
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vloncar
reviewed
Nov 12, 2024
docs/advanced/hgq.rst
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| .. image:: https://img.shields.io/badge/arXiv-2405.00645-b31b1b.svg | ||
| :target: https://arxiv.org/abs/2405.00645 | ||
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| High Granularity Quantization (HGQ) is a gradient-based automatic bitwidth optimization and quantization-aware training algorithm for neural networks to be deployed on FPGAs, By laveraging gradients, it allows for bitwidth optimization at arbitrary granularity, up to per-weight and per-activation level. |
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"...to be deployed on FPGAs, ...". You wanted an end of sentence there, not a comma.
docs/advanced/hgq.rst
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| :alt: Overview of HGQ | ||
| :align: center | ||
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| Conversion of models from High Granularity Quantization (HGQ) is fully supported. The HGQ models are first converted to proxy model format, which can then be parsed by hls4ml bit-accurately. Below is an example of how to create a HGQ model and converting it to hls4ml. |
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You already introduced the HGQ abbreviation, no need to write it as High Granularity Quantization (HGQ) again. Also, it would be more clear if you refered to HGQ as a library in the first sentense. Like, "Conversion of models made with HGQ library is fully supported".
In the last sentence, pick a style, either "example of how to create ... and convert..." or "example of creating ... and converting ..."
jmitrevs
requested changes
Nov 12, 2024
docs/advanced/hgq.rst
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| @@ -0,0 +1,49 @@ | |||
| ============== | |||
| High Granularity Quantization (HGQ) | |||
| ============== | |||
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Github suggests that the overline is too short.
vloncar
approved these changes
Nov 13, 2024
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Description
Added a page of doc for HGQ in advanced section
Type of change
Tests
Not applicable
Checklist
pre-commiton the files I edited or added.