Compile errors in Tensorflow Lite Micro framework when trying to integrate Tensorflow Lite Micro to my ESP32 Arduino project

Overcoming Compile Errors in Tensorflow Lite Micro Integration with ESP32 Arduino Projects

Integrating Tensorflow Lite Micro with ESP32 Arduino projects can be a rewarding experience, but it's not without its challenges. One common obstacle that developers encounter is compile errors, which can hinder the successful execution of their projects.

In this blog post, we'll delve into the most prevalent compile errors that arise when interfacing Tensorflow Lite Micro with ESP32 Arduino and provide comprehensive solutions to resolve them.

Error 1: Undefined Reference to 'TfLiteRegistration'

This error occurs when the function TfLiteRegistration is referenced in your code but is not defined anywhere. It typically indicates a missing header file inclusion.

Solution:

To resolve this error, ensure that you've included the necessary header file "tensorflow/lite/micro/all_ops_resolver.h" in your code. This header file defines the TfLiteRegistration structure, which is essential for registering custom operators in your Tensorflow Lite Micro model. Here's an example of how to include the header file:

#include "tensorflow/lite/micro/all_ops_resolver.h"

Error 2: Undefined Reference to 'tflite::MicroErrorReporter'

This error occurs when the class tflite::MicroErrorReporter is referenced in your code but is not defined anywhere. It signifies the absence of the header file that defines this class.

Solution:

The solution to this error lies in including the header file "tensorflow/lite/micro/micro_error_reporter.h" in your code. This header file contains the definition of the tflite::MicroErrorReporter class, which is crucial for printing error messages and logs during inference.

#include "tensorflow/lite/micro/micro_error_reporter.h"

Error 3: Undefined Reference to 'tflite::MicroInterpreter'

This error arises when the class tflite::MicroInterpreter is referenced in your code but is not defined anywhere. It indicates the absence of the header file that defines this class.

Solution:

To resolve this error, you need to include the header file "tensorflow/lite/micro/micro_interpreter.h" in your code. This header file provides the definition of the tflite::MicroInterpreter class, which is essential for executing Tensorflow Lite Micro models.

#include "tensorflow/lite/micro/micro_interpreter.h"

Error 4: Undefined Reference to 'tflite::InitializeRuntimeTensor'

This error occurs when the function tflite::InitializeRuntimeTensor is referenced in your code but is not defined anywhere. This function is used to allocate memory for runtime tensors in a Tensorflow Lite Micro model.

Solution:

To fix this error, make sure that you've included the header file "tensorflow/lite/micro/system_setup.h" in your code. This header file contains the definition of the tflite::InitializeRuntimeTensor function, which is necessary for setting up the runtime environment for your Tensorflow Lite Micro model.

#include "tensorflow/lite/micro/system_setup.h"

Error 5: Undefined Reference to '__ubsan_handle_out_of_bounds'

This error is specific to the Arduino development environment and arises when the __ubsan_handle_out_of_bounds function is referenced in your code. It's related to a compiler flag that enables runtime bounds checking.

Solution:

To resolve this error, you can disable the runtime bounds checking flag in the Arduino IDE. To do so, navigate to Tools > Board > ESP32 Arduino > Project Properties. Under the Build Options section, uncheck the "Enable Runtime Bounds Checking" checkbox. This will disable the __ubsan_handle_out_of_bounds function and allow your code to compile successfully.

Conclusion

In this blog post, we've addressed the most common compile errors that occur when integrating Tensorflow Lite Micro with ESP32 Arduino projects. By following the comprehensive solutions provided, you can effectively overcome these errors and ensure the successful execution of your projects.

Remember to carefully review your code, include the necessary header files, and configure your development environment correctly to avoid these errors. With a solid understanding of the root cause of each error and the appropriate solutions, you can confidently navigate the challenges of Tensorflow Lite Micro integration and unlock the full potential of your ESP32 Arduino projects.