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rascani merged 2 commits into from
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Arm backend: Document validated TinyML models for Cortex-M #19078

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Arm backend: Document validated TinyML models for Cortex-M
rascani Apr 22, 2026
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Merge branch 'main' into cortex-m-validated-models-docs
rascani Apr 27, 2026
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61 changes: 61 additions & 0 deletions docs/source/backends/arm-cortex-m/arm-cortex-m-overview.md
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Expand Up @@ -164,3 +164,64 @@ backends/arm/scripts/run_fvp.sh --elf=build/arm_executor_runner --target=ethos-u
```

For a complete end-to-end walkthrough including dataset setup, calibration, and result validation, see the [Cortex-M MobileNetV2 notebook](https://github.com/pytorch/executorch/blob/main/examples/arm/cortex_m_mv2_example.ipynb).

## Testing with Bundled I/O

The tutorial above produces a plain `.pte`. For programmatic testing,
`aot_arm_compiler --bundleio` instead produces a bundled (`.bpte`) program
that embeds reference inputs and expected outputs; the Cortex-M test runner
loads the bundle via semihosting and self-checks its outputs against the
embedded references, emitting `Test_result: PASS` or `Test_result: FAIL`
on the UART.

The driver for this flow is `examples/arm/run.sh`, which exports the model,
builds the Cortex-M test runner, launches the Corstone-300 FVP with
semihosting enabled, and checks the bundled output. Run it from the
ExecuTorch repo root after `./install_executorch.sh`:

```bash
# One-time: install the Arm toolchain + FVP.
examples/arm/setup.sh --i-agree-to-the-contained-eula
source examples/arm/arm-scratch/setup_path.sh

# Per model: export, build, and run on the FVP in one step.
# (Quantization is the default for the cortex-m55+int8 target.)
examples/arm/run.sh \
--model_name=<model> \
--target=cortex-m55+int8 \
--bundleio
```

Replace `<model>` with any of the validated-model names in the table
below. Without `--calibration_data`, calibration falls back to the model's
`get_example_inputs()` (random data) — enough for bundled-I/O numerical
parity, but not for task-accuracy claims. On `Test_result: FAIL`, inspect
the FVP UART log for the per-tensor diff; supplying a representative
calibration dataset via `--calibration_data=<dir>` often resolves
mismatches caused by random-input calibration.

:::{important}
Bundled I/O checks INT8 **numerical parity** between the exported `.bpte`
and the eager-mode quantized model on reference inputs; it does not
validate task accuracy (VWW / KWS / ImageNet).
:::

## Validated Models

The following models are exported, INT8 quantized, lowered, and validated
end-to-end on the Corstone-300 FVP:

| Model | Task | Input shape | Source | Test |
|--------------------|------------------------------------|---------------|---------------------------------------------------|----------------------------------------------------------|
| `mv2` | Image classification | `1x3x224x224` | `examples/models/mobilenet_v2/` | `backends/cortex_m/test/models/test_mobilenet_v2.py` |
| `mv3` | Image classification | `1x3x224x224` | `examples/models/mobilenet_v3/` | `backends/cortex_m/test/models/test_mobilenet_v3.py` |
| `ds_cnn` | Keyword spotting (MLPerf Tiny) | `1x1x49x10` | `examples/models/mlperf_tiny/ds_cnn.py` | `backends/cortex_m/test/models/test_ds_cnn.py` |
| `mobilenet_v1_025` | Visual Wake Words (MLPerf Tiny) | `1x3x96x96` | `examples/models/mlperf_tiny/mobilenet_v1_025.py` | `backends/cortex_m/test/models/test_mobilenet_v1_025.py` |
| `resnet8` | Image classification (MLPerf Tiny) | `1x3x32x32` | `examples/models/mlperf_tiny/resnet8.py` | `backends/cortex_m/test/models/test_resnet8.py` |
| `deep_autoencoder` | Anomaly detection (MLPerf Tiny) | `1x640` | `examples/models/mlperf_tiny/deep_autoencoder.py` | `backends/cortex_m/test/models/test_deep_autoencoder.py` |

:::{note}
`mobilenet_v1_025` is the MLPerf Tiny Visual Wake Words benchmark
(MobileNetV1 with width multiplier 0.25) — the canonical person-detection
reference model for TinyML.
:::
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