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Summary : Add Pico2 Tutorial
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‎docs/source/embedded-section.md‎

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- {doc}`tutorial-arm-ethos-u` — Export a simple PyTorch model for the ExecuTorch Ethos-U backend
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- {doc}`raspberry_pi_llama_tutorial` — Deploy a LLaMA model on a Raspberry Pi
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- {doc}`pico2_tutorial` — Deploy a demo MNIST model on the Raspberry Pi Pico 2
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```{toctree}
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:hidden:
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embedded-backends
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tutorial-arm-ethos-u
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raspberry_pi_llama_tutorial
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pico2_tutorial

‎docs/source/pico2_tutorial.md‎

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# Pico2: A simple MNIST Tutorial
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Deploy your PyTorch models directly to Raspberry Pi Pico2 microcontroller with ExecuTorch and Infer.
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## What You'll Build
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A 28×28 MNIST digit classifier running on memory constrained, low power microcontrollers:
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- Input: ASCII art digits (0, 1, 4, 7)
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- Output: Real-time predictions via USB serial
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- Memory: <400KB total footprint
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## Prerequisites
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- [Environment Setup section](https://docs.pytorch.org/executorch/1.0/using-executorch-building-from-source.html)
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- Refer to this link on how to accept 'EULA' agreement and setup toolchain [link](https://docs.pytorch.org/executorch/1.0/backends-arm-ethos-u.html#development-requirements)
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- Verify ARM toolchain
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```bash
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which arm-none-eabi-gcc # --> arm/ethos-u-scratch/arm-gnu-toolchain-13.3.rel1-x86_64-arm-none-eabi/bin/
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```
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## Step 1: Generate pte from given example Model
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- Use the [provided example model](https://github.com/pytorch/executorch/blob/main/examples/raspberry_pi/pico2/export_mlp_mnist.py)
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```bash
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python export_mlp_mnist.py # Creates balanced_tiny_mlp_mnist.pte
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```
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- **Note:** This is hand-crafted MNIST Classifier (proof-of-concept), and not production trained.This tiny MLP recognizes digits 0, 1, 4, and 7 using manually designed feature detectors.
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## Step 2: Build Firmware for Pico2
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```bash
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# Generate model
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python export_mlp_mnist.py # Creates balanced_tiny_mlp_mnist.pte
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# Build Pico2 firmware (one command!)
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./executorch/examples/rpi/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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Output: **executorch_pico.uf2** firmware file (examples/raspberry_pi/pico2/build/)
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**Note:** 'build_firmware_pico.sh' script converts given model pte to hex array and generates C code for the same via this helper [script](https://github.com/pytorch/executorch/blob/main/examples/raspberry_pi/pico2/pte_to_array.py). This C code is then compiled to generate final .uf2 binary which is then flashed to Pico2.
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## Step 3: Flash to Pico2
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Hold BOOTSEL button on Pico2
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Connect USB → Mounts as ^RPI-RP2^ drive
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Drag & drop ^executorch_pico.uf2^ file
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Release BOOTSEL → Pico2 reboots with your model
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## Step 4: Verify Deployment
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**Success indicators:**
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- LED blinks 10× at 500ms → Model running ✅
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- LED blinks 10× at 100ms → Error, check serial ❌
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**View predictions:**
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```bash
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# Connect serial terminal
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screen /dev/tty.usbmodem1101 115200
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# Expected output:
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Something like:
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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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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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## Memory Optimization Tips
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### Pico2 Constraints
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- 520KB SRAM (runtime memory)
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- 4MB Flash (model storage)
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- Keep models small:
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### Common Issues
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- "Memory allocation failed" → Reduce model size and use quantization
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- "Operator missing" → Use selective build: ^--operators=add,mul,relu^
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- "Import error" → Check ^arm-none-eabi-gcc^ toolchain setup.
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In order to resolve some of the issues above, refer to the following guides:
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- [ExecuTorch Quantization Optimization Guide](https://docs.pytorch.org/executorch/1.0/quantization-optimization.html)
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- [Model Export & Lowering](https://docs.pytorch.org/executorch/1.0/using-executorch-export.html) and
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- [Selective Build support](https://docs.pytorch.org/executorch/1.0/kernel-library-selective-build.html)
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### Firmware Size Analysis
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```bash
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cd <root of executorch repo>
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ls -al examples/raspberry_pi/pico2/build/executorch_pico.elf
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```
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- **Overall section sizes**
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```bash
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arm-none-eabi-size -A examples/raspberry_pi/pico2/build/executorch_pico.elf
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```
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- **Detailed section breakdown**
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```bash
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arm-none-eabi-objdump -h examples/raspberry_pi/pico2/build/executorch_pico.elf
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```
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- **Symbol sizes (largest consumers)**
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```bash
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arm-none-eabi-nm --print-size --size-sort --radix=d examples/raspberry_pi/pico2/build/executorch_pico.elf | tail -20
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```
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### Model Memory Footprint
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- **Model data specifically**
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```bash
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arm-none-eabi-nm --print-size --size-sort --radix=d examples/raspberry_pi/pico2/build/executorch_pico.elf | grep -i model
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```
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- **Check what's in .bss (uninitialized data)**
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```bash
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arm-none-eabi-objdump -t examples/raspberry_pi/pico2/build/executorch_pico.elf | grep ".bss" | head -10
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```
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- **Memory map overview**
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```bash
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arm-none-eabi-readelf -l examples/raspberry_pi/pico2/build/executorch_pico.elf
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```
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## Next Steps
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### Scale up your deployment
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- Train real models → Replace hand-crafted weights
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- Optimize further → INT8 quantization, pruning
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- Deploy fleet → Mass production ready
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### Happy Inference!
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**Result:** PyTorch model → Pico2 deployment in 4 simple steps 🚀
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Total tutorial time: ~15 minutes
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**Conclusion:** Real-time inference on memory constrained, low power microcontrollers, a complete PyTorch → ExecuTorch → Pico2 demo MNIST deployment

‎examples/raspberry_pi/pico2/README.md‎

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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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The included `export_mlp_mnist.py` (in examples/raspberry_pi/pico2) 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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## Bring Your Own Model and Deploy
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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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- Use existing demo model (examples/raspberry_pi/pico2/export_mlp_mnist.py) or bring your own model
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- Build firmware with one command and pass the model file (.pte) as an argument
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- Deploy directly to Pico2
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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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#### Important Caveats:
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- Memory constraints - Models must fit in 520KB SRAM
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- Memory constraints - Models must fit in 520KB SRAMn (Pico2)
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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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- Selective builds - Include only operators your model uses if you want to reduce binary size
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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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- 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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For more details , refer to the following guides:
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- [ExecuTorch Quantization Optimization Guide](https://docs.pytorch.org/executorch/1.0/quantization-optimization.html)
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- [Model Export & Lowering](https://docs.pytorch.org/executorch/1.0/using-executorch-export.html) and
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- [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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Make sure you have the toolchain configured correctly. Refer to this [setup](https://docs.pytorch.org/executorch/main/backends-arm-ethos-u.html#development-requirements) for more details.
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```bash
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### Verify Execution
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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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### Debugging via Serial Terminal
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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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Result: A complete PyTorch → ExecuTorch → Pico2 demo neural network deployment! 🚀
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Result: A complete PyTorch → ExecuTorch → Pico2 demo MNIST deployment! 🚀

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