Optimize TensorFlow Lite Micro Models for ARM Cortex-M4 with 16-bit Fixed-Point

Introduction

Optimizing TensorFlow Lite Micro quantized models for ARM Cortex-M4 inference can significantly enhance the performance and efficiency of machine learning applications on microcontrollers. This tutorial will walk you through the steps needed to optimize your models using 16-bit fixed-point arithmetic.

Prerequisites

• Basic understanding of machine learning concepts
• Familiarity with TensorFlow and TensorFlow Lite
• ARM Cortex-M4 development environment set up
• Access to a suitable IDE (e.g., Keil, IAR, or Eclipse)

Parts/Tools

• ARM Cortex-M4 microcontroller
• TensorFlow Lite Micro library
• Computer with TensorFlow installed
• Development board or simulator for testing

Steps

1. Prepare your model
   1. Train your model using TensorFlow.
   2. Convert your model to TensorFlow Lite format:

   import tensorflow as tf
   
   converter = tf.lite.TFLiteConverter.from_keras_model(your_model)
   converter.optimizations = [tf.lite.Optimize.OPTIMIZE_FOR_SIZE]
   tflite_model = converter.convert()

   3. Quantize your model
      1. Apply post-training quantization:

      converter.representative_dataset = representative_data_gen
      converter.target_spec.supported_types = [tf.int16]
      tflite_quantized_model = converter.convert()

      2. Save the quantized model:

      with open('model_quantized.tflite', 'wb') as f:
          f.write(tflite_quantized_model)

      3. Integrate the model into your application
         1. Include the TensorFlow Lite Micro library in your project.
         2. Load the quantized model:

         #include "tensorflow/lite/c/common.h"
         #include "tensorflow/lite/micro/micro_interpreter.h"
         
         const tflite::Model* model = tflite::GetModel(model_quantized_data);
         tflite::MicroInterpreter interpreter(model, tensor_arena, kTensorArenaSize, nullptr, nullptr);

         3. Run inference
            1. Prepare your input tensor:

            float input_data[INPUT_SIZE] = { /* your input values */ };
            interpreter.inputs()[0]->data.uint8 = reinterpret_cast(input_data);

            2. Invoke the interpreter:

            interpreter.Invoke();

            3. Retrieve the output tensor:

            float* output_data = interpreter.outputs()[0]->data.f;

            Troubleshooting

            • Model not loading: Check if the model path is correct and that the model is properly converted.
            • Incorrect output: Ensure that the input data is pre-processed correctly to match the model’s requirements.
            • Memory issues: Monitor the memory usage and ensure that the tensor arena is large enough for the model.

            Conclusion

            Optimizing TensorFlow Lite Micro quantized models for ARM Cortex-M4 using 16-bit fixed-point arithmetic is a powerful way to leverage machine learning in embedded systems. By following these steps, you can improve inference speed and reduce memory usage, making your applications more efficient.