Feature/nxa15600/eiex 1037 improve mapping algorithm

[irtrukhina]

Summary

Improve NXP Neutron operator-to-Edge profiling map

1. Improve TFLite-to-Neutron mapping algorithm

   • Filter tensor I/O to dynamic (Variable) tensors only, excluding constants
   • Group Neutron subgraphs into linearly-connected execution chains based on matching dynamic I/O tensor names
   • Match each Neutron chain to a corresponding TFLite operator chain by comparing boundary inputs and outputs

2. Fix mapping for batch > 1

   • Enable dumpMicrocode in the converter log to include the Microinstructions section
   • Remap NeutronOperator locations and kernel counts using the actual CALLARGS sequence from the microcode, which accounts for batch repetitions and converter-injected helper kernels (e.g. MemCpy)

3. Fix profiling time scaling

   • Neutron profiling timestamps are measured in clock cycles; divide by the Neutron NPU frequency to convert to seconds

Test plan

Tests included

cc @robert-kalmar @JakeStevens @digantdesai @rascani

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/22614

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

✅ No Failures

As of commit 36a70f6 with merge base 7dc8641 ():
💚 Looks good so far! There are no failures yet. 💚

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[linux-foundation-easycla]

• ❌ - login: @irtrukhina / name: Irina Korchakova. The commit (2fef5a9, 36a70f6, e636e40) is not authorized under a signed CLA. Please click here to be authorized. For further assistance with EasyCLA, please visit our EasyCLA portal and chat with our support bot.

[github-actions]

This PR needs a release notes: label

If your change should be included in the release notes (i.e. would users of this library care about this change?), please use a label starting with release notes:. This helps us keep track and include your important work in the next release notes.

To add a label, you can comment to pytorchbot, for example
@pytorchbot label "release notes: none"

For more information, see
https://github.com/pytorch/pytorch/wiki/PyTorch-AutoLabel-Bot#why-categorize-for-release-notes-and-how-does-it-work.