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Working with AI is always fun. In this guide we will be using Tensorflow's tflite library to run on NUCLEO-WB55RG development board. The Tflite doesn't support the STM32 chipset in this Nucleo board out of the box. In this blog I will describe how I ported the tflite to run on this Nucleo board. The procedure can be adapted to another chipset if required with very few tweeks. The NUCLEO-WB55RG is chosen because of its large memory footprint, M4 Arm Cortex performance with FPU support. 

Some of the key features of TFLite include:

• Small footprint: TFLite is optimized for devices with limited resources, and its binary size is smaller compared to the full TensorFlow library.
• Hardware acceleration: TFLite supports a wide range of hardware accelerators, including GPUs, DSPs, and NPUs (Neural Processing Units), to improve the performance of machine learning models on devices.
• Interpreter: TFLite uses an interpreter to execute machine learning models, which allows for flexibility in terms of the devices and platforms it can run on.
• Cross-platform compatibility: TFLite can run on a variety of platforms, including Android, iOS, Linux, and microcontrollers, making it easy to deploy machine learning models on a wide range of devices.
• Model conversion: TFLite provides tools for converting pre-trained TensorFlow models to TFLite models, which can be used on mobile and embedded devices.
• Support for a wide range of models: TFLite supports a wide range of machine learning models, including image classification, object detection, and text-to-speech, among others.

The NUCLEO-WB55RG is a development board for the STM32WB55 microcontroller from STMicroelectronics. It is part of the STM32 Nucleo development board series, which provides a flexible and cost-effective way for developers to evaluate and prototype with the STM32WB55 microcontroller.

Some of the key features of the NUCLEO-WB55RG include:

• On-board STM32WB55 microcontroller: The development board is equipped with a STM32WB55 microcontroller, which has a Cortex-M4 main core and a Cortex-M0+ co-processor, and built-in support for Bluetooth 5 and 802.15.4 wireless standards.
• Expansion connectors: The board has a variety of expansion connectors, including a Arduino Uno V3 connector and a morpho connector, which allows for easy connection to various external devices and sensors.
• On-board debugging: The NUCLEO-WB55RG has an on-board ST-LINK/V2-1 debugger, which provides a convenient way to debug and program the microcontroller.
• Power options: The board can be powered through the USB connector or an external source, and has a voltage regulator that allows for input voltage between 7V to 12V.
• Software support: The NUCLEO-WB55RG is supported by the STM32CubeWB software package, which provides a wide range of software examples, libraries, and tools for developing with the STM32WB55 microcontroller.
• Compatibility: The board is also compatible with a wide range of development environments, including IAR, Keil, and GCC.
• Physical form factor: The board has a compact form factor of 69.9 x 53.3mm, it is easy to handle and take it along with you in your development journey.

Porting TensorFlow Lite (TFLite) to STM32 microcontrollers involves several steps, including:

• Building TFLite for the ARM Cortex-M architecture used by STM32 microcontrollers.
• Porting TFLite's dependencies, such as the flatbuffers library, to the STM32 platform.
• Adapting TFLite's C++ code to run on the STM32 platform, including any necessary changes to memory management and threading.
• Testing the ported TFLite on STM32 microcontroller
• It's important to note that TensorFlow Lite is not a fully self-contained library, it still relies on a number of other libraries to run, such as the FlatBuffers library, and may also require additional libraries or tools depending on the specific STM32 microcontroller and development environment being used.