Arm backend: document Ethos-U & VGF image examples (#18156)

Document Ethos-U & VGF image examples

Signed-off-by: Usamah Zaheer <usamah.zaheer@arm.com>
(cherry picked from commit 6c02866)

Author: usamahz

backends/arm/scripts/docgen/ethos-u/backends-arm-ethos-u-overview.md.in

@@ -59,6 +59,14 @@ An example runtime application is available in [examples/arm/executor_runner](ht
 The example application is recommended to use for testing basic functionality of your lowered models, as well as a starting point for developing runtime integrations for your own targets.
 For an in-depth explanation of the architecture of the executor_runner and the steps required for doing such an integration, please refer to [Ethos-U porting guide](https://github.com/pytorch/executorch/blob/main/examples/arm/ethos-u-porting-guide.md).
 
+### Example: Image classification flow
+
+[`examples/arm/image_classification_example_ethos_u`](https://github.com/pytorch/executorch/tree/main/examples/arm/image_classification_example_ethos_u)
+contains a complete DeiT-based export and runtime walkthrough. The README shows
+how to run `model_export/export_deit.py`, build the sample firmware, and convert
+test images into C arrays so the workflow described in this guide can be tried
+end to end.
+
 ### Ethos-U memory modes
 
 The Ethos-U NPU provides two distinct memory interfaces:
@@ -147,6 +155,10 @@ ExecuTorch for the Ethos-U backend, you automatically install the compiler conta
 
 **→{doc}`/backends/arm-ethos-u/tutorials/arm-ethos-u-tutorials` — Tutorials.**
 
+**→{doc}`/backends/arm-ethos-u/U55_op_support` — Ethos-U55 supported operators.**
+
+**→{doc}`/backends/arm-ethos-u/U85_op_support` — Ethos-U85 supported operators.**
+
 
 ```{toctree}
 :maxdepth: 2
@@ -157,4 +169,6 @@ arm-ethos-u-partitioner
 arm-ethos-u-quantization
 arm-ethos-u-troubleshooting
 tutorials/arm-ethos-u-tutorials
+U55_op_support
+U85_op_support
 ```

backends/arm/scripts/docgen/vgf/backends-arm-vgf-overview.md.in

@@ -56,6 +56,21 @@ For more information on quantization, see [Quantization](arm-vgf-quantization.md
 The VGF backend can use the default ExecuTorch runner. The steps required for building and running it are explained in the [VGF Backend Tutorial](tutorials/vgf-getting-started.md). <!-- @lint-ignore -->
 The example application is recommended to use for testing basic functionality of your lowered models, as well as a starting point for developing runtime integrations for your own targets.
 
+### Example: Image classification flow
+
+[`examples/arm/image_classification_example_vgf`](https://github.com/pytorch/executorch/tree/main/examples/arm/image_classification_example_vgf)
+contains a ready-to-run DeiT image classification pipeline for VGF targets.
+The example README documents how to:
+
+- Export the quantized INT8 weights via `model_export/export_deit.py`.
+- Use the provided requirements file to install the ML SDK converter scripts
+  and produce a `.pte` artifact.
+- Build and launch the Vulkan-based runtime under `runtime/`, which loads the
+  `.pte` alongside the generated VGF blob.
+
+Following this walkthrough ensures you exercise the same lowering + runtime flow
+described in the rest of this guide but with a concrete end-to-end sample.
+
 ## Reference
 
 **→{doc}`/backends/arm-vgf/arm-vgf-partitioner` — Partitioner options.**

docs/source/backends/arm-ethos-u/arm-ethos-u-overview.md

@@ -137,6 +137,14 @@ An example runtime application is available in [examples/arm/executor_runner](ht
 The example application is recommended to use for testing basic functionality of your lowered models, as well as a starting point for developing runtime integrations for your own targets.
 For an in-depth explanation of the architecture of the executor_runner and the steps required for doing such an integration, please refer to [Ethos-U porting guide](https://github.com/pytorch/executorch/blob/main/examples/arm/ethos-u-porting-guide.md).
 
+### Example: Image classification flow
+
+[`examples/arm/image_classification_example_ethos_u`](https://github.com/pytorch/executorch/tree/main/examples/arm/image_classification_example_ethos_u)
+contains a complete DeiT-based export and runtime walkthrough. The README shows
+how to run `model_export/export_deit.py`, build the sample firmware, and convert
+test images into C arrays so the workflow described in this guide can be tried
+end to end.
+
 ### Ethos-U memory modes
 
 The Ethos-U NPU provides two distinct memory interfaces:
@@ -229,6 +237,7 @@ ExecuTorch for the Ethos-U backend, you automatically install the compiler conta
 
 **→{doc}`/backends/arm-ethos-u/U85_op_support` — Ethos-U85 supported operators.**
 
+
 ```{toctree}
 :maxdepth: 2
 :hidden:

docs/source/backends/arm-vgf/arm-vgf-overview.md

@@ -128,6 +128,21 @@ For more information on quantization, see [Quantization](arm-vgf-quantization.md
 The VGF backend can use the default ExecuTorch runner. The steps required for building and running it are explained in the [VGF Backend Tutorial](tutorials/vgf-getting-started.md). <!-- @lint-ignore -->
 The example application is recommended to use for testing basic functionality of your lowered models, as well as a starting point for developing runtime integrations for your own targets.
 
+### Example: Image classification flow
+
+[`examples/arm/image_classification_example_vgf`](https://github.com/pytorch/executorch/tree/main/examples/arm/image_classification_example_vgf)
+contains a ready-to-run DeiT image classification pipeline for VGF targets.
+The example README documents how to:
+
+- Export the quantized INT8 weights via `model_export/export_deit.py`.
+- Use the provided requirements file to install the ML SDK converter scripts
+  and produce a `.pte` artifact.
+- Build and launch the Vulkan-based runtime under `runtime/`, which loads the
+  `.pte` alongside the generated VGF blob.
+
+Following this walkthrough ensures you exercise the same lowering + runtime flow
+described in the rest of this guide but with a concrete end-to-end sample.
+
 ## Reference
 
 **→{doc}`/backends/arm-vgf/arm-vgf-partitioner` — Partitioner options.**