This guide covers hardware selection, model optimization, toolchain operations, code implementation, and debugging techniques, using the STM32 series microcontrollers as an example:
1.Hardware Selection and Configuration
(1)Clarify Requirements
Computational Requirements:
Simple classification tasks (e.g., binary classification of sensor data):Cortex-M0+/M3 (e.g., STM32G0/F1) are sufficient.
Complex tasks (image recognition, speech processing): Choose models with hardware acceleration (e.g., STM32H7S3 ‘s FMAC unit or STM32N6 ‘s NPU).
Memory Requirements:
Model weights + input/output buffers must be less than Flash/RAM capacity. For example, the size of the model after INT8 quantization should typically be controlled to within 100KB (suitable for STM32F4 ‘s 512KB Flash).
(2) Example Hardware Configuration STM32H743VIT6 (high-performance scenario):
Clock frequency: 550 MHz, 2MB Flash, 1MB RAM, supports double-precision floating-point operations.
Enable hardware acceleration: Use `HAL_CRC_Init()` to call the CRC unit for accelerated verification, or use FMAC (Filter Math Accelerator) to optimize convolution calculations.
STM32F746ZG (balanced):
216 MHz, 1MB Flash, 320KB RAM, integrated LCD controller, suitable for AI applications requiring a display.
2.Model Lightweighting and Optimization Details
(1) Quantization (Quantization) TensorFlow Lite quantization process:
python
import tensorflow as tf
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT] Default quantization (weights INT8, activation functions FP32)
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] Full INT8 quantization
tflite_quant_model = converter.convert()
with open(‘model_int8.tflite’, ‘wb’) as f:
f.write(tflite_quant_model)
Post-quantization calibration:
Calibrate quantization errors using a real dataset to avoid accuracy collapse (to be done on the PC side).
(2) Pruning (Pruning) using the TensorFlow Model Optimization Toolkit:
python
import tensorflow_model_optimization as tfmot
prune_low_magnitude = tfmot.sparsity.keras.prune_low_magnitude
model = prune_low_magnitude(model, pruning_schedule=…)
model.compile(…)
model.fit(…)
stripped_model = tfmot.sparsity.keras.strip_pruning(model)
(3) Optimize model structure by replacing large operators:
Replace standard convolution with Depthwise Convolution.
Reduce the number of nodes in fully connected layers (e.g., from 128 to 64).
Use lightweight architectures such as MobileNetV2 or SqueezeNet.
3.Detailed Explanation of STM32Cube.AI Toolchain
(1) Installation and Configuration of STM32CubeMX:
Download and install from the ST official website, selecting the Cube.AI plugin.
Importing the Model:
-
Create a project in CubeMX, selecting the target MCU (e.g., STM32H743).
-
Right-click on the project → `Add/Import Network` → Select the converted TFLite model (`.tflite` file).
(2) Model Conversion Parameters Memory Optimization Options:
Select `Use Static Memory Allocation` (to avoid dynamic memory fragmentation).
Set `Tensor Arena Size` (adjust according to model requirements, usually reserving input/output buffers).
Hardware Acceleration:
If using an NPU (e.g., STM32N6), select `Use Hardware Accelerator`.
For STM32H7, enable `FMAC` or `Cordic` unit to accelerate mathematical operations.
(3) Generate Key Files for Code Structure:
`network.c`: Model inference logic.
`network_data.c`: Model weights (stored in Flash).
API Call Example:
c
#include “network.h”
ai_handle network = ai_network_create(&network_params);
ai_buffer* input = ai_network_inputs_get(network);
ai_buffer* output = ai_network_outputs_get(network);
// Fill input data (e.g., sensor data)
memcpy(input->data, sensor_data, input->size);
ai_network_run(network); // Execute inference
float prediction = ((float*)output->data)[0]; // Get output
4. Code Integration and Debugging
(1) Memory Management for Model Weights Storage
Modify the linker script (`.ld` file) to allocate weights to Flash:
ld
.ai_network_weights (READ_ONLY) : {
KEEP(*(.network_weights))
} > FLASH
Input/output buffers:
Use static arrays allocated in a contiguous area of RAM:
c
AI_ALIGNED(4) static uint8_t input_buf[INPUT_SIZE]; // 4 byte alignment
AI_ALIGNED(4) static uint8_t output_buf[OUTPUT_SIZE];
(2) Performance Optimization Enable Hardware FPU (if supported):
Enable the `FPU` option in CubeMX, and enable in code:
c
SCB->CPACR |= (0xF << 20); // Enable Cortex-M7 ‘s FPU
Use DMA to transfer data:
Transfer sensor data to the input buffer via DMA to reduce CPU usage.
(3) Debugging Techniques Print Intermediate Layer Outputs:
Insert debugging code in `network.c` to output intermediate layer results:
c
ai_i32 layer_id = 0; // Layer 0
ai_buffer* layer_output = ai_network_layer_outputs_get(network, layer_id);
printf(“Layer 0 output: %f\n”, ((float*)layer_output->data)[0]);
Performance Analysis:
Use STM32CubeIDE ‘s SystemView tool to analyze inference time and CPU load.
5. Power Consumption Optimization Dynamic Frequency Adjustment
c
HAL_RCC_DeInit(); // Reset clock
__HAL_RCC_SYSCLK_CONFIG(RCC_SYSCLKSOURCE_HSI); // Switch to low-speed clock (e.g., 16 MHz)
Low Power Mode:
Enter STOP mode during inference gaps:
c
HAL_PWR_EnterSTOPMode(PWR_LOWPOWERREGULATOR_ON, PWR_STOPENTRY_WFI);
6. Validation and Testing Unit Testing
Use Python on the PC side to generate test vectors and compare with the microcontroller output:
python
import numpy as np
test_input = np.random.rand(1, 224, 224, 3).astype(np.float32) Input sample
expected_output = tf_model.predict(test_input) // PC inference result
Convert `test_input` to a binary file and send it to the microcontroller via UART to compare output errors.
Real-time Data Validation:
Connect actual sensors (e.g., camera or microphone) to validate the model’s robustness in real scenarios.
Common Issues and Solutions Insufficient Memory:
Reduce model input size (e.g., from 224×224 to 96×96), or use more aggressive quantization (e.g., binarization).
Slow Inference Speed:
Enable hardware acceleration (NPU/FMAC), or optimize model structure (reduce the number of layers).
Accuracy Decrease After Quantization:
Increase the number of representative dataset samples during calibration, or use mixed quantization (keeping some layers as FP16).
By following these detailed steps, you can systematically complete the entire process from model training to microcontroller deployment. It is recommended to debug step by step in conjunction with ST’s official examples (such as the `X-CUBE-AI` ‘s `Hello World` example).