Machine Learning Applications for Embedded Devices Using TinyML and C++

Advancements in the fields of Internet of Things (IoT) and Industry 4.0, for example, require the usage of ML and AI models for several purposes than it would otherwise be impossible to address them. Despite of this, the embedded devices used in almost IoT-based (or Industry 4.0-based) applications have limited computing resources to run the ML or AI models we know so far with agility.
Taking this scenario into account, TinyML is a ML tool that has the ability to perform on-device analysis for different sensing modalities such as audio, vision and voice. ML models based on the TinyML tool have low power, memory and computational requirements. These features make it an ideal choice for deploying ML and IA models in the IoT framework. Of course, this optimisation is possible thanks to the C++ programming language, in which this tool is written.