QNN OP profiling
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Nobody has claimed this yet.
module: devtools
module: qnn
triaged
- Dominant language
- Python
- Stars
- 5k
- Forks
- 1.2k
- Avg merge
- 2d 10h
- Merged PRs (30d)
- 581
Description
🚀 The feature, motivation and pitch
Is there any way to sample and analyze the time consumption of each op in the qnn backend?
Alternatives
No response
Additional context
No response
RFC (Optional)
No response
cc @Gasoonjia @cccclai @cbilgin
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No files, tests, or entry points are named. Start by locating the QNN backend and any existing profiling or timing support, then determine the scope for sampling and analyzing per-operator time. Done should mean that QNN operator-level profiling is available with a clearly defined output and validation path.
Written by the indexing model from the issue text.
Assessment
- Domain
- embedded-iot, machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100