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Summary: Pico2 demo of neural network (MNIST)
Test Plan: 1. Setup Conda 2. examples/arm/setup.sh --i-agree-to-the-contained-eula; source examples/ arm/ethos-u-scratch/setup_path.sh 3. cd examples/raspberry_pi/pico2 4. python export_mlp_mnist.py; ./build_firmware_pico.sh --model=balanced_tiny_mlp_mnist.pte Reviewers: Subscribers: Tasks: Tags:
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‎examples/raspberry_pi/pico2/CMakeLists.txt‎

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@@ -64,11 +64,14 @@ set(MODEL_STAMP "${CMAKE_CURRENT_BINARY_DIR}/model_pte.stamp")
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add_custom_command(
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OUTPUT ${MODEL_STAMP}
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COMMAND python3 ${EXECUTORCH_ROOT}/executorch/examples/rpi/pte_to_array.py
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--model ${INPUT_MODEL} --file ${MODEL_PTE_C}
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COMMAND
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python3
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${EXECUTORCH_ROOT}/executorch/examples/raspberry_pi/pico2/pte_to_array.py
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--model ${INPUT_MODEL} --file ${MODEL_PTE_C}
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COMMAND ${CMAKE_COMMAND} -E touch ${MODEL_STAMP}
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DEPENDS ${INPUT_MODEL}
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${EXECUTORCH_ROOT}/executorch/examples/rpi/pte_to_array.py
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DEPENDS
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${INPUT_MODEL}
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${EXECUTORCH_ROOT}/executorch/examples/raspberry_pi/pico2/pte_to_array.py
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COMMENT "Injecting PTE data from '${INPUT_MODEL}' into model_pte.c"
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VERBATIM
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)

‎examples/raspberry_pi/pico2/README.md‎

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# Overview
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This document outlines the steps required to run a simple Add Module on the Pico2 microcontroller using executorch.
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## (Pre-requisistes) Prepare the Environment for Arm
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This document outlines the steps required to run a simple MNIST digit recognition neural network on the Pico2 microcontroller using ExecuTorch.
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1. Setup executorch development environment, Also see <a href="https://docs.pytorch.org/executorch/main/tutorial-arm-ethos-u.html#software"/> for instructions on setting up the environment for Arm.
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2. Make sure you have the toolchain configured correctly.
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## Demo Model: Hand-crafted MNIST Classifier
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The included `export_mlp_mnist.py` creates a demonstration model with hand-crafted weights (not production-trained). This tiny MLP recognizes digits 0, 1, 4, and 7 using manually designed feature detectors.
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Note: This is a proof-of-concept. For production use, train your model on real MNIST data.
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## Bring Your Own Model
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This demo demonstrates ExecuTorch's ability to bring your own PyTorch model and deploy it to Pico2 with one simple script. The complete pipeline works from any PyTorch model to a runnable binary:
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### Train your PyTorch model
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Export using `torch.export()` and `to_edge()`
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Build firmware with one command
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Deploy directly to Pico2
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#### Important Caveats:
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- Memory constraints - Models must fit in 520KB SRAM
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- Missing operators - Some ops may not be supported
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- Selective builds - Include only operators your model uses
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## Memory Constraints & Optimization
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- Critical: Pico2 has limited memory:
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- 520KB SRAM (on-chip static RAM)
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- 4MB QSPI Flash (onboard storage)
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### Always apply optimization techniques on large models that do not fit in Pico2 memory:
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Large models will not fit. Keep your `.pte` files small!
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- Quantization (INT8, INT4)
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- Model pruning
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- Operator fusion
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- Selective builds (include only needed operators)
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For more details , refer to the [ExecuTorch Quantization Optimization Guide](https://docs.pytorch.org/executorch/1.0/quantization-optimization.html), [Model Export & Lowering](https://docs.pytorch.org/executorch/1.0/using-executorch-export.html) and [Selective Build support](https://docs.pytorch.org/executorch/1.0/kernel-library-selective-build.html)
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## (Prerequisites) Prepare the Environment for Arm
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Setup executorch development environment. Also see instructions for setting up the environment for Arm.
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Make sure you have the toolchain configured correctly. Refer to this [setup](https://docs.pytorch.org/executorch/1.0/backends-arm-ethos-u.html#development-requirements) for more details.
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```bash
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which arm-none-eabi-gcc
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--> return something like executorch/examples/arm/ethos-u-scratch/arm-gnu-toolchain-13.3.rel1-x86_64-arm-none-eabi/bin/arm-none-eabi-gcc
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# Should return: executorch/examples/arm/ethos-u-scratch/arm-gnu-toolchain-13.3.rel1-x86_64-arm-none-eabi/bin/arm-none-eabi-gcc
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```
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## Build Pico2 Firmware with Executorch
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## Build Pico2 Firmware with ExecuTorch
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This involves two steps:
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This step involves two sub steps
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### Generate your model:
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```bash
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cd examples/raspberry_pi/pico2
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python export_mlp_mnist.py # Creates balanced_tiny_mlp_mnist.pte
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```
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1. Cross Compile Executorch for Arm Cortex M, Pico2 target
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2. Build the firmware with the input model provided (If not provided, it will use the default_model.pte)
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### Build firmware:
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Use the following command to build the firmware:
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``` bash
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executorch/examples/rpi/build_firmware_pico.sh --model=<path_to_model.pte>
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```bash
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# In the dir examples/raspberry_pi/pico2
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build_firmware_pico.sh --model=balanced_tiny_mlp_mnist.pte # This creates executorch_pico.uf2, a firmware image for Pico2
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```
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### Flash Firmware
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Hold the BOOTSEL button on the Pico2 and connect it to your computer; it will mount as RPI-RP2. Copy the executorch_pico.uf2 file to this drive.
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Hold the BOOTSEL button on Pico2 and connect to your computer. It mounts as `RPI-RP2`. Copy `executorch_pico.uf2` to this drive.
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### Verify Execution
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Check that the Pico2's LED blinks 10 times at 500 ms interval to confirm successful firmware execution.
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The Pico2's LED should blink 10 times at 500 ms intervals, indicating successful firmware execution. If connected via serial, you should see:
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The Pico2 LED blinks 10 times at 500ms intervals for successful execution. Via serial terminal, you'll see:
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```bash
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Method loaded [forward]
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Output: 13.000000, 136.000000, 24.000000, 131.000000
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...
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...
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PREDICTED: 4 (Expected: 4) ✅ CORRECT!
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==================================================
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=== Digit 7 ===
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############################
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############################
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####
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####
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###
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Input stats: 159 white pixels out of 784 total
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Running neural network inference...
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✅ Neural network results:
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Digit 0: 370.000
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Digit 1: 0.000
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Digit 2: -3.000
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Digit 3: -3.000
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Digit 4: 860.000
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Digit 5: -3.000
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Digit 6: -3.000
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Digit 7: 1640.000 ← PREDICTED
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Digit 8: -3.000
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Digit 9: -3.000
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� PREDICTED: 7 (Expected: 7) ✅ CORRECT!
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==================================================
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🎉 All tests complete! PyTorch neural network works on Pico2!
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```
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### Debugging via Serial Terminal
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On macOS or Linux, open a serial terminal with:
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On macOS/Linux:
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```bash
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screen /dev/tty.usbmodem1101 115200
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```
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Replace `/dev/tty.usbmodem1101` with your device path. If LED blinks 10 times at 100ms intervals, check logs for errors, but if it blinks 10 times at 500ms intervals, it is successful!
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Replace /dev/tty.usbmodem1101 with your device path. This terminal shows program logs and errors. If
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the LED blinks 10 times at 100 ms intervals, your program hit an error state—check the logs here.
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These steps complete running the simple model on Pico2 using ExecuTorch.
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Result: A complete PyTorch → ExecuTorch → Pico2 demo neural network deployment! 🚀

‎examples/raspberry_pi/pico2/build_firmware_pico.sh‎

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# build_firmware_pico.sh
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# Simple script to cross-compile ExecuTorch and build Pico2 firmware with optional model input
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set -e
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set -euo pipefail
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# Paths
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ROOT_DIR="$(cd "$(dirname "$0")/../.." && pwd)" # examples/rpi/ -> root dir
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PICO2_DIR="${ROOT_DIR}/examples/rpi/pico2"
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ROOT_DIR="$(cd "$(dirname "$0")/../../.." && pwd)" # examples/raspberry_pi/ -> root dir
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PICO2_DIR="${ROOT_DIR}/examples/raspberry_pi/pico2"
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BUILD_DIR="${PICO2_DIR}/build"
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EXECUTORCH_BUILD_DIR="${ROOT_DIR}/cmake-out"
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