  Recent advances in state-of-the-art ultra-low power embedded devices for
machine learning (ML) have permitted a new class of products whose key features
enable ML capabilities on microcontrollers with less than 1 mW power
consumption (TinyML). TinyML provides a unique solution by aggregating and
analyzing data at the edge on low-power embedded devices. However, we have only
recently been able to run ML on microcontrollers, and the field is still in its
infancy, which means that hardware, software, and research are changing
extremely rapidly. Consequently, many TinyML frameworks have been developed for
different platforms to facilitate the deployment of ML models and standardize
the process. Therefore, in this paper, we focus on bench-marking two popular
frameworks: Tensorflow Lite Micro (TFLM) on the Arduino Nano BLE and CUBE AI on
the STM32-NucleoF401RE to provide a standardized framework selection criterion
for specific applications.
