Summary

Advances in MCUs and AI/ML algorithms have made it possible to have models small enough to be run on IoT devices. tinyML enables us to develop ML applications that can make inferences on data coming from sensors right in the field without the need for a round-trip to a backend system or cloud. There are several frameworks that we can employ for this purpose. TensorFlow is a platform that provides all the necessary tools and libraries to develop ML applications.

Data collection, ML model design and optimization, and inference are the stages in the tinyML pipeline. TensorFlow Lite for Microcontrollers (TFLM) is the framework in TensorFlow to optimize and use ML models on constrained IoT devices. We developed a simple tinyML application that uses a sine-wave model to make inferences and draws its predictions on the screen. Espressif empowers us with two models to develop voice applications: WakeNet and MultiNet. We learned how to use these models in an application that activates...