TinyML with Arduino Nano RP2040 Connect Course Tiny Machine Learning (TinyML), designed for battery-powered devices, is broadly defined as an expanding area of machine learning technologies and applications that includes hardware (dedicated integrated circuits), algorithms, and software that can perform sensor data analysis on-device with very low power consumption, typically in the milliwatt range and below. This eliminates the need to send data to the cloud for classification, thus providing greater security. Also, power-hungry processors are replaced with a small microcontroller. Of course, there are limitations. These limitations arise from limited hardware resources, clock speed, etc. However, there are several application areas that do not require a lot of computation and a machine learning-based solution is desirable. In this case, TinyML comes into play. It can be used to detect anomalies in a factory’s machinery, predict maintenance needs for equipment, in the healthcare field, etc. The scope of TinyML’s application is wide and has a bright future.

The main objective of this course is to introduce TinyML development from data collection, model training, testing and deployment. A low-cost Arduino nano RP2040 connect board with 265 KB RAM and 16 MB flash along with accelerometer, gyroscope, microphone, temperature sensor and built-in wireless connectivity module (WiFi+Bluetooth) is used in this course and all the examples shown here have been tested on this board.