DoReFa quantizer with higher number of MACs/Ops, Grouped convs as custom ops on LCE 0.7.0
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- Dominant language
- C++
- Stars
- 258
- Forks
- 35
- PR merge metrics
- No merged PRs in 30d
Description
Hello, I have a couple of questions regarding quantizer options for Larq and LCE.
I am designing a BNN using the DoReFa quantizer, however, I noticed a very high number of estimated MACs and Ops when converting the model for ARM64. Changing the quantizer to "ste_sign" dramatically lowered the number of MACs and Ops.
I was wondering if there is a way to use the DoReFa quantizer for training without the serious overhead of operations when converting and running the model for inference in LCE? Is the "ste_sign" quantizer the only viable option for efficient inference?
Thank you for the excellent work and for your attention.
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Research direction
The issue names no file, test, or entry point to inspect. Start by clarifying whether this requests implementation of DoReFa inference support or an explanation of current quantizer behavior; the work is complete only when the supported approach and its inference impact are documented or an agreed implementation scope is defined.
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Assessment
- Tech stack
- cpp, tensorflow
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100