+
+
+┌───────────────────────────────────────────────────┐
+│ │
+│ Host Development │
+│ (e.g., Linux or macOS+Docker) │
+│ │
+│ [Model export / compilation with ExecuTorch] │
+│ │
+│ ┌───────────────────┐ ┌───────────┐ │
+│ │ │ │ │ │
+│ │ executor_runner │ │ .pte │ │
+│ │ (ELF binary) │ │ (model) │ │
+│ │ │ │ │ │
+│ └───────────┬───────┘ └─────┬─────┘ │
+│ │ │ │
+└─────────────────┼──────────────────────┼──────────┘
+ │ SCP/serial transfer │
+ │ │
+ ▼ ▼
+┌───────────────────────────────────────────────────┐
+│ │
+│ NXP i.MX93 Embedded Board │
+│ │
+│ │
+│ ┌───────────────────────────────────────────┐ │
+│ │ executor_runner (runtime binary) │ │
+│ │ │ │
+│ │ ┌───────────────────────────────┐ │ │
+│ │ │ Load .pte (model) │ │ │
+│ │ └───────────────┬───────────────┘ │ │
+│ │ │ │ │
+│ │ ▼ │ │
+│ │ ┌───────────────────────────────┐ │ │
+│ │ │ Initialize hardware (CPU/NPU) │ │ │
+│ │ └───────────────┬───────────────┘ │ │
+│ │ │ │ │
+│ │ ▼ │ │
+│ │ ┌───────────────────────────────┐ │ │
+│ │ │ Perform inference │ │ │
+│ │ └───────────────┬───────────────┘ │ │
+│ │ │ │ │
+│ │ ▼ │ │
+│ │ ┌───────────────────────────────┐ │ │
+│ │ │ Output results │ │ │
+│ │ └───────────────────────────────┘ │ │
+│ └───────────────────────────────────────────┘ │
+│ │
+└───────────────────────────────────────────────────┘
+
+ExecuTorch runtime deployment to an embedded system
+
+
+## Accept the Arm End User License Agreement
+
+```bash
+export ARM_FVP_INSTALL_I_AGREE_TO_THE_CONTAINED_EULA=True
+```
+
+## Set Up the Arm Build Environment
+
+This example builds the [MobileNet V2](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/) computer vision model. The model is a convolutional neural network (CNN) that extracts visual features from an image. It is used for image classification and object detection. The actual Python code for the MobileNet V2 model is in the `executorch` repo: [executorch/examples/models/mobilenet_v2/model.py](https://github.com/pytorch/executorch/blob/main/examples/models/mobilenet_v2/model.py).
+
+You can read a detail explanation of the build steps here: [ARM Ethos-U Backend](https://docs.pytorch.org/executorch/stable/backends-arm-ethos-u.html).
+
+1. Run the steps to set up the build environment:
+
+ ```bash
+ ./examples/arm/setup.sh \
+ --target-toolchain arm-none-eabi-gcc
+ ```
+
+2. Update your environment:
+ ```bash
+ source examples/arm/ethos-u-scratch/setup_path.sh
+ ```
+
+## Build the ExecuTorch .pte
+Now you will build the `.pte` file, that will be used on the NXP board.
+
+1. Build the [MobileNet V2](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/) ExecuTorch `.pte` runtime file using [aot_arm_compiler](https://github.com/pytorch/executorch/blob/2bd96df8de07bc86f2966a559e3d6c80fc324896/examples/arm/aot_arm_compiler.py):
+
+ ```bash
+ python3 -m examples.arm.aot_arm_compiler \
+ --model_name="mv2" \
+ --quantize \
+ --delegate \
+ --debug \
+ --target ethos-u55-256
+
+ ```
+
+{{% notice Note %}}
+| Flag | Meaning |
+| ------------------------ | --------------------------------------------------- |
+| `--model_name="mv2"` | Example model: MobileNetV2 (small, efficient) |
+| `--quantize` | Enables int8 quantization (required for Ethos-NPUs) |
+| `--delegate` | Enables offloading layers to the Ethos backend |
+| `--debug` | Verbose build output |
+| `--target ethos-u55-256` | Targets the Ethos-U55 |
+
+The `--quantize` flag uses one input example, so the resulting model will likely have poor classification performance.
+{{% /notice %}}
+
+3. Check that the `mv2_arm_delegate_ethos-u55-256.pte` file was generated:
+
+ ```bash
+ ls mv2_arm_delegate_ethos-u55-256.pte
+ ```
+
+## Troubleshooting
+**`setup.sh`**
+- If you see the following error in the `setup.sh` output:
+ ```bash { output_lines = "1-2" }
+ Failed to build tosa-tools-v0.80
+ ERROR: Could not build wheels for tosa-tools-v0.80, which is required to install pyproject.toml-based projects
+ ```
+ then:
+ 1. Increase the swap space to 8 GB:
+ ```bash
+ fallocate -l 8G /swapfile
+ chmod 600 /swapfile
+ mkswap /swapfile
+ swapon /swapfile
+ ```
+ - [optional] Deallocate the swap space after you complete this learning path:
+ ```bash
+ swapoff /swapfile
+ rm /swapfile
+ ```
+
+ {{% notice macOS %}}
+ Increase the "Memory Limit" in Docker settings to 12 GB:
+ 
+
+ {{% /notice %}}
+
+ 2. Re-run `setup.sh`
+ ```bash
+ ./examples/arm/setup.sh --i-agree-to-the-contained-eula
+ ```
+
+- If you see the following error in the `setup.sh` output:
+ ```bash { output_lines = "1-2" }
+ Failed to build tosa-tools
+ ERROR: Failed to build installable wheels for some pyproject.toml based projects (tosa-tools)
+ ```
+ then do the below troubleshooting steps.
+ 1. Install any missing build tools:
+ ```bash
+ apt update && apt install -y \
+ cmake \
+ build-essential \
+ ninja-build \
+ python3-dev \
+ libboost-all-dev
+ ```
+ 2. Re-run `setup.sh`
+ ```bash
+ ./examples/arm/setup.sh --i-agree-to-the-contained-eula
+ ```
+- If you see the following error in the `setup.sh` output:
+ ```bash { output_lines = "1-8" }
+ ...
+ ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+ tosa-tools 0.80.2.dev1+g70ed0b4 requires jsonschema, which is not installed.
+ tosa-tools 0.80.2.dev1+g70ed0b4 requires flatbuffers==23.5.26, but you have flatbuffers 24.12.23 which is incompatible.
+ tosa-tools 0.80.2.dev1+g70ed0b4 requires numpy<2, but you have numpy 2.3.1 which is incompatible.
+ ...
+ ```
+ then just re-run `setup.sh`
+ ```bash
+ ./examples/arm/setup.sh --i-agree-to-the-contained-eula
\ No newline at end of file
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/9-build-executorch-runner-for-cm33.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/9-build-executorch-runner-for-cm33.md
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@@ -0,0 +1,251 @@
+---
+title: Build the executor_runner firmware
+weight: 10
+
+### FIXED, DO NOT MODIFY
+layout: learningpathall
+---
+
+## Set up MCUXpresso for VS Code
+
+Install the MCUXpresso extension in VS Code:
+
+{{< tabpane code=false >}}
+{{< tab header="Windows/Linux" >}}
+1. Open VS Code and press `Ctrl+Shift+X` to open Extensions
+2. Search for "MCUXpresso for VS Code"
+3. Click **Install** on the NXP extension
+{{< /tab >}}
+{{< tab header="macOS" >}}
+1. Open VS Code and press `Cmd+Shift+X` to open Extensions
+2. Search for "MCUXpresso for VS Code"
+3. Click **Install** on the NXP extension
+{{< /tab >}}
+{{< /tabpane >}}
+
+Configure the ARM toolchain path:
+
+{{< tabpane code=false >}}
+{{< tab header="Windows/Linux" >}}
+1. Open Settings with `Ctrl+,`
+2. Search for **MCUXpresso: Toolchain**
+3. Set the toolchain path to: `/opt/arm-gnu-toolchain-14.2.rel1-x86_64-arm-none-eabi/bin`
+{{< /tab >}}
+{{< tab header="macOS" >}}
+1. Open Settings with `Cmd+,`
+2. Search for **MCUXpresso: Toolchain**
+3. Set the toolchain path to: `/opt/arm-gnu-toolchain-14.2.rel1-x86_64-arm-none-eabi/bin`
+{{< /tab >}}
+{{< /tabpane >}}
+
+Install the MCUXpresso SDK for FRDM-MIMX93:
+
+{{< tabpane code=false >}}
+{{< tab header="Windows/Linux" >}}
+1. Open Command Palette: `Ctrl+Shift+P`
+2. Type: **MCUXpresso: Install MCUXpresso SDK**
+3. Search for "FRDM-MIMX93" or select **MCIMX93-EVK**
+4. Select the latest SDK and click **Install**
+{{< /tab >}}
+{{< tab header="macOS" >}}
+1. Open Command Palette: `Cmd+Shift+P`
+2. Type: **MCUXpresso: Install MCUXpresso SDK**
+3. Search for "FRDM-MIMX93" or select **MCIMX93-EVK**
+4. Select the latest SDK and click **Install**
+{{< /tab >}}
+{{< /tabpane >}}
+
+{{% notice Note %}}
+If the FRDM-MIMX93 development board is not listed in the current MCUXpresso SDK catalog, you can alternatively select **MCIMX93-EVK** as they share the same i.MX93 SoC with Cortex-M33 core architecture. The SDK compatibility ensures seamless development across both platforms.
+{{% /notice %}}
+
+## Clone the executor_runner repository
+
+Clone the ready-to-build executor_runner project:
+
+```bash
+git clone https://github.com/fidel-makatia/Executorch_runner_cm33.git
+cd Executorch_runner_cm33
+```
+
+The repository contains the complete runtime source code and build configuration for Cortex-M33.
+
+## Copy ExecuTorch libraries
+
+The executor_runner requires prebuilt ExecuTorch libraries with Ethos-U NPU support from your Docker container.
+
+Find your ExecuTorch build container:
+
+```bash { output_lines = "2-3" }
+docker ps -a
+CONTAINER ID IMAGE COMMAND CREATED STATUS
+abc123def456 executorch "/bin/bash" 2 hours ago Exited
+```
+
+Copy the libraries:
+
+```bash
+docker cp abc123def456:/home/ubuntu/executorch/cmake-out/lib/. ./executorch/lib/
+docker cp abc123def456:/home/ubuntu/executorch/. ./executorch/include/executorch/
+```
+
+Replace `abc123def456` with your actual container ID.
+
+{{% notice Note %}}
+In some Docker containers, the `cmake-out` folder might not exist. If you don't see the libraries, run the following command to build them:
+
+```bash
+./examples/arm/run.sh --build-only
+```
+
+The libraries will be generated in `arm_test/cmake-out`.
+{{% /notice %}}
+
+Verify the libraries:
+
+```bash { output_lines = "2-5" }
+ls -lh executorch/lib/
+-rw-r--r-- 1 user user 2.1M libexecutorch.a
+-rw-r--r-- 1 user user 856K libexecutorch_core.a
+-rw-r--r-- 1 user user 1.3M libexecutorch_delegate_ethos_u.a
+```
+
+## Configure the project for FRDM-MIMX93
+
+Open the project in VS Code:
+
+```bash
+code .
+```
+
+Initialize the MCUXpresso project:
+
+{{< tabpane code=false >}}
+{{< tab header="Windows/Linux" >}}
+1. Press `Ctrl+Shift+P` to open Command Palette
+2. Type: **MCUXpresso: Import Repository**
+3. Select the current folder
+4. Choose **MIMX9352_cm33** as the target processor
+{{< /tab >}}
+{{< tab header="macOS" >}}
+1. Press `Cmd+Shift+P` to open Command Palette
+2. Type: **MCUXpresso: Import Repository**
+3. Select the current folder
+4. Choose **MIMX9352_cm33** as the target processor
+{{< /tab >}}
+{{< /tabpane >}}
+
+VS Code generates the MCUXpresso configuration.
+
+## Configure memory settings
+
+The Cortex-M33 has 108KB of RAM. The default memory configuration allocates:
+- 16KB for the method allocator (activation tensors)
+- 8KB for the scratch allocator (temporary operations)
+
+These settings are in `CMakeLists.txt`:
+
+```cmake
+target_compile_definitions(${MCUX_SDK_PROJECT_NAME} PRIVATE
+ ET_ARM_BAREMETAL_METHOD_ALLOCATOR_POOL_SIZE=0x4000 # 16KB
+ ET_ARM_BAREMETAL_SCRATCH_TEMP_ALLOCATOR_POOL_SIZE=0x2000 # 8KB
+ ET_MODEL_PTE_ADDR=0x80100000 # DDR address for model
+)
+```
+
+{{% notice Note %}}
+If you see "region RAM overflowed" errors during build, reduce these pool sizes. For example, change to 0x2000 (8KB) and 0x1000 (4KB) respectively.
+{{% /notice %}}
+
+## Build the firmware
+
+Configure the build system:
+
+{{< tabpane code=false >}}
+{{< tab header="Windows/Linux" >}}
+1. Press `Ctrl+Shift+P`
+2. Type: **CMake: Configure**
+3. Select **ARM GCC** as the kit
+4. Choose **Debug** or **Release**
+{{< /tab >}}
+{{< tab header="macOS" >}}
+1. Press `Cmd+Shift+P`
+2. Type: **CMake: Configure**
+3. Select **ARM GCC** as the kit
+4. Choose **Debug** or **Release**
+{{< /tab >}}
+{{< /tabpane >}}
+
+Build the project:
+
+Press `F7` or:
+
+{{< tabpane code=false >}}
+{{< tab header="Windows/Linux" >}}
+1. Press `Ctrl+Shift+P`
+2. Type: **CMake: Build**
+{{< /tab >}}
+{{< tab header="macOS" >}}
+1. Press `Cmd+Shift+P`
+2. Type: **CMake: Build**
+{{< /tab >}}
+{{< /tabpane >}}
+
+Watch the build output:
+
+```output
+[build] Scanning dependencies of target executorch_runner_cm33.elf
+[build] [ 25%] Building CXX object source/arm_executor_runner.cpp.obj
+[build] [ 50%] Building CXX object source/arm_memory_allocator.cpp.obj
+[build] [ 75%] Linking CXX executable executorch_runner_cm33.elf
+[build] [100%] Built target executorch_runner_cm33.elf
+[build] Build finished with exit code 0
+```
+
+Verify the build succeeded:
+
+```bash { output_lines = "2" }
+ls -lh build/executorch_runner_cm33.elf
+-rwxr-xr-x 1 user user 601K executorch_runner_cm33.elf
+```
+
+Check memory usage to ensure it fits in the Cortex-M33:
+
+```bash { output_lines = "2-3" }
+arm-none-eabi-size build/executorch_runner_cm33.elf
+ text data bss dec hex filename
+ 52408 724 50472 103604 19494 executorch_runner_cm33.elf
+```
+
+The total RAM usage (data + bss) is approximately 51KB, well within the 108KB limit.
+
+## Troubleshooting
+
+**ARM toolchain not found:**
+
+Add the toolchain to your PATH:
+
+```bash
+export PATH=/opt/arm-gnu-toolchain-14.2.rel1-x86_64-arm-none-eabi/bin:$PATH
+```
+
+**Cannot find ExecuTorch libraries:**
+
+Verify the libraries were copied correctly:
+
+```bash
+ls executorch/lib/libexecutorch*.a
+```
+
+If missing, re-copy from the Docker container.
+
+**Region RAM overflowed:**
+
+Edit `CMakeLists.txt` and reduce the memory pool sizes:
+
+```cmake
+ET_ARM_BAREMETAL_METHOD_ALLOCATOR_POOL_SIZE=0x2000 # 8KB
+ET_ARM_BAREMETAL_SCRATCH_TEMP_ALLOCATOR_POOL_SIZE=0x1000 # 4KB
+```
+
+Then rebuild with `F7`.
\ No newline at end of file
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_index.md
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+---
+title: Observing Ethos-U on a Physical Device, Built on Arm
+
+minutes_to_complete: 120
+
+who_is_this_for: This is an introductory topic for developers and data scientists new to Tiny Machine Learning (TinyML), who want to observe ExecuTorch performance on a physical device.
+
+learning_objectives:
+ - Identify suitable physical Arm-based devices for TinyML applications.
+ - Optionally, configure physical embedded devices.
+ - Deploy a TinyML ExecuTorch model to NXP's FRDM i.MX 93 applicaiton processor (board).
+
+prerequisites:
+ - Purchase of a NXP [FRDM i.MX 93](https://www.nxp.com/design/design-center/development-boards-and-designs/frdm-i-mx-93-development-board:FRDM-IMX93) board.
+ - A USB Mini-B to USB Type-A cable, or a USB Mini-B to USB Type-C cable.
+ - Basic knowledge of Machine Learning concepts.
+ - A computer running Linux or macOS.
+ - VS Code
+
+author: Waheed Brown, Fidel Makatia Omusilibwa
+
+### Tags
+skilllevels: Introductory
+subjects: ML
+armips:
+ - Cortex-A
+ - Cortex-M
+ - Ethos-U
+
+operatingsystems:
+ - Linux
+ - macOS
+
+tools_software_languages:
+ - Baremetal
+ - Python
+ - PyTorch
+ - ExecuTorch
+ - Arm Compute Library
+ - GCC
+
+further_reading:
+ - resource:
+ title: TinyML Brings AI to Smallest Arm Devices
+ link: https://newsroom.arm.com/blog/tinyml
+ type: blog
+ - resource:
+ title: Arm Machine Learning Resources
+ link: https://www.arm.com/developer-hub/embedded-and-microcontrollers/ml-solutions/getting-started
+ type: documentation
+ - resource:
+ title: Arm Developers Guide for Cortex-M Processors and Ethos-U NPU
+ link: https://developer.arm.com/documentation/109267/0101
+ type: documentation
+
+
+
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1 # _index.md always has weight of 1 to order correctly
+layout: "learningpathall" # All files under learning paths have this same wrapper
+learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---
diff --git a/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_next-steps.md b/content/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/_next-steps.md
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+---
+# ================================================================================
+# FIXED, DO NOT MODIFY THIS FILE
+# ================================================================================
+weight: 21 # Set to always be larger than the content in this path to be at the end of the navigation.
+title: "Next Steps" # Always the same, html page title.
+layout: "learningpathall" # All files under learning paths have this same wrapper for Hugo processing.
+---
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