Update Cortex-M backend status from proof of concept to beta (pytorch#18690)

As discussed in 4/1/26 partner sync.

---------

Co-authored-by: Claude <noreply@anthropic.com>

Author: 2 people

backends/cortex_m/README.md

@@ -1,7 +1,7 @@
 # Cortex-M Backend
 
 > [!NOTE]
-> WIP. This is a temporary/placeholder backend for Cortex-M CPUs. It is not intended to be used in production, but rather as a proof of concept. Things will change without notice.
+> Beta. This backend has been validated with a set of small models (e.g. MLPerf Tiny, MobileNetV2) and provides broad operator coverage through CMSIS-NN accelerated kernels with portable-ops fallback.
 
 ## Overview
 

docs/source/backends/arm-cortex-m/arm-cortex-m-overview.md

@@ -1,7 +1,7 @@
 # Arm Cortex-M Backend
 
 :::{note}
-This backend is a work-in-progress proof of concept. It is not intended for production use, and APIs may change without notice.
+This backend is in **beta**. It has been validated with a set of small models (e.g. MLPerf Tiny, MobileNetV2) and provides broad operator coverage through CMSIS-NN accelerated kernels with portable-ops fallback.
 :::
 
 The Arm® Cortex®-M backend accelerates quantized model execution on Arm Cortex-M CPUs using [CMSIS-NN](https://arm-software.github.io/CMSIS-NN/latest/) optimized kernels. Unlike delegate-based backends, it operates as an operator library: quantized subgraphs are replaced with CMSIS-NN accelerated kernels during the pass-lowering stage, while unsupported operators fall back to portable fp32 kernels.

examples/arm/cortex_m_mv2_example.ipynb

@@ -10,7 +10,7 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "# Cortex-M backend flow example - Running MobileNetV2\n\nThis guide demonstrates the full flow for running a module on Arm Cortex-M using ExecuTorch. \n**Note that this backend is currently WIP and things may change / break without notice.**\n\nBefore you begin:\n1. (In a clean virtual environment with a compatible Python version) Install executorch using `./install_executorch.sh`\n2. Install Arm cross-compilation toolchain and simulators using `examples/arm/setup.sh --i-agree-to-the-contained-eula`\n3. Add Arm cross-compilation toolchain and simulators to PATH using `source examples/arm/arm-scratch/setup_path.sh` \n4. Install the following pip packages: `pip install matplotlib datasets`\n\nWith all commands executed from the base `executorch` folder.\n\n\n\n*Some scripts in this notebook produce long output logs: Configuring the 'Customizing Notebook Layout' settings to enable 'Output:scrolling' and setting 'Output:Text Line Limit' makes this more manageable*\n"
+   "source": "# Cortex-M backend flow example - Running MobileNetV2\n\nThis guide demonstrates the full flow for running a module on Arm Cortex-M using ExecuTorch. \n\nBefore you begin:\n1. (In a clean virtual environment with a compatible Python version) Install executorch using `./install_executorch.sh`\n2. Install Arm cross-compilation toolchain and simulators using `examples/arm/setup.sh --i-agree-to-the-contained-eula`\n3. Add Arm cross-compilation toolchain and simulators to PATH using `source examples/arm/arm-scratch/setup_path.sh` \n4. Install the following pip packages: `pip install matplotlib datasets`\n\nWith all commands executed from the base `executorch` folder.\n\n\n\n*Some scripts in this notebook produce long output logs: Configuring the 'Customizing Notebook Layout' settings to enable 'Output:scrolling' and setting 'Output:Text Line Limit' makes this more manageable*\n"
   },
   {
    "cell_type": "markdown",
@@ -179,4 +179,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}