How to deploy artificial intelligence algorithms on STM32?

Deploying artificial intelligence (AI) algorithms on STM32 microcontrollers involves converting trained models into optimized, efficient code that can run on the constrained resources of STM32 devices (limited memory, CPU power, and energy). Here's a step-by-step guide to help you through the process:

1. Choose the Right STM32 Board

Not all STM32 microcontrollers are suitable for AI. Choose one with sufficient RAM, Flash, and compute power. Popular choices for AI include:

• STM32H7 series (high-performance)

• STM32F7 series (performance)

• STM32L4 series (ultra-low-power AI applications)

• STM32MP1 (microprocessor-based, Linux-capable)

2. Train Your AI Model

Use a standard ML framework (on your PC) to develop and train your model:

• TensorFlow / TensorFlow Lite

• PyTorch (convert to ONNX or TFLite)

• Keras

Ensure your model is relatively small (tinyML-compatible).

3. Convert Model to Suitable Format

Once trained, convert the model to a format compatible with STM32 tools:

Common formats:

• TensorFlow Lite (.tflite) – Use with STM32Cube.AI

• ONNX – Can be converted to TFLite or used with third-party tools

4. Use STM32Cube.AI

STM32Cube.AI is ST's toolchain to convert and optimize neural network models for STM32 devices.

Steps:

1. Install STM32CubeMX (includes STM32Cube.AI plugin)

2. Create a new STM32 project in STM32CubeMX

3. Add X-CUBE-AI middleware

4. Import your .tflite or .onnx model

5. Configure memory and peripherals

6. Generate C code

STM32Cube.AI will:

• Analyze the model

• Optimize it (quantization, pruning, etc.)

• Generate efficient C code for inference

5. Integrate Inference Code

• STM32Cube.AI generates functions like ai_network_run() that you can call in your embedded app.

• You’ll need to handle input/output buffers (normalized image/audio/etc.)

6. Deploy and Test

• Compile and flash the firmware using STM32CubeIDE or Keil/IAR.

• Test with real-world inputs.

• Use debugging tools like STM32CubeMonitor-AI to validate the model's performance and accuracy on hardware.

7. Optimize for Performance

• Use fixed-point or quantized models (8-bit preferred).

• Enable hardware acceleration (some STM32s have DSP, FPU, or AI accelerator).

• Use DMA for I/O and avoid blocking calls.

• Reduce model complexity (e.g., fewer layers, smaller filters).

Example Use Cases

• Keyword spotting (e.g., wake word detection)

• Gesture recognition using accelerometers

• Image classification with low-res grayscale images

• Predictive maintenance using vibration sensors

Additional Tools

• NanoEdge AI Studio (STMicroelectronics): For anomaly detection and edge AI without needing deep ML knowledge.

• CMSIS-NN: Optimized neural network kernels for Cortex-M processors (alternative to STM32Cube.AI for more control).