7. Docs: QAT performance analysis and model name corrections#12
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Document the six major overhead sources in quantization-aware training vs float training: FakeQuantize nodes, observer statistics, power-of-2 scale snapping, soft-quantize variants, backward pass complexity, and torch.compile ordering. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Replace outdated TimeSeries_Generic_* names with current registry names: - Classification: CLS_*_NPU, CLS_ResAdd_3k, CLS_ResCat_3k - Regression: REGR_* - Anomaly Detection: AD_* - Forecasting: FCST_* Updated across DEVICE_TASK_SUPPORT.md and all example config.yaml and documentation files. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Merge in TINYML-ALGO/tinyml-agent-skills from 2026/pranav_a to main * commit 'ff02a0e95ef4841fb883ba841d353a125d4e4447': restructuring agent skill
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de8af16d Pull request #45: https://jira.itg.ti.com/browse/TINYML_ALGO-698 REVERT: e48ef1a Pull request #14: TINYML_ALGO-711: fixing readme REVERT: 16fc6a6 TINYML_ALGO-711: fixing readme REVERT: e3639d2 Pull request #13: removing pycache REVERT: f8bb3b7 removing pycache REVERT: dd38428 Pull request #12: restructuring agent skill REVERT: ff02a0e restructuring agent skill REVERT: d26c6a5 Pull request #11: fixing tiny ml name REVERT: 640ffd3 fixing tiny ml name REVERT: 4ee3a19 Pull request #10: 2026/pranav a REVERT: be83fc6 minor fixes REVERT: e3a5700 removed assets, included autoMP quant REVERT: 1af575a Pull request #9: correcting npu devices list REVERT: 31e9eb1 correcting npu devices list REVERT: 59b209b Pull request #8: improving readme REVERT: 8c3260b improving readme REVERT: 668916f Pull request #7: improving readme REVERT: 68686b3 improving readme REVERT: 814316e Pull request #6: fixes to readme and marketplace json REVERT: e4bc0b4 fixes to readme and marketplace json REVERT: 6a64208 Pull request #5: fixes to readme REVERT: 0f9c868 fixes to readme REVERT: 52f95ff Pull request #4: 2026/pranav a REVERT: 443295d fixes to readme REVERT: 1881112 fixes to readme and marketplace json REVERT: 229ab57 Pull request #3: 2026/pranav a REVERT: 6519104 minor readme fix REVERT: 38e9f9f minor readme fix REVERT: db81f81 Pull request #2: minor readme fix REVERT: 1c0737a minor readme fix REVERT: 0a0c02d Pull request #1: minor readme fix REVERT: b682335 minor readme fix REVERT: 062eb39 Initial Commit git-subtree-dir: tinyml-agent-skills git-subtree-split: de8af16d9e23de3e9bda3d811a0ebdece1178260
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Summary
Two documentation improvements:
1. QAT Training Performance Analysis (
quantization.rst)Adds a new section explaining why QAT (Quantization-Aware Training) is significantly slower than float training, with a detailed breakdown of each overhead factor:
2N+1extra forward/backward ops per batch per layerceil2_tensorGPU syncs — power-of-2 scale snapping forces device-to-host synchronizationtorch.compileordering — QAT model runs in eager mode because FX graph rewrite discards compiled versionIncludes a summary table and source file references for each factor.
2. Model Name Corrections (
DEVICE_TASK_SUPPORT.md)Updated model names in the device/task support matrix to match the current model registry (e.g.,
TimeSeries_Generic_2k→CLS_2k).Files Changed (2 files)
docs/source/features/quantization.rsttinyml-modelmaker/DEVICE_TASK_SUPPORT.md