Presently there are billions of IoT devices in use ranging from alarm systems, smart fridges and clocks to thermostats. Having many devices communication over the networks simultaneously may lead to saturation of the bandwidth. This has generated interest in a new paradigm for computing: the data-centric paradigm. Instead of bringing data to the computing power, we bring the computing power to the data.

This means that there is huge potential for performing machine learning on small embedded systems like microcontrollers. This brings some challenges as most microcontrollers have limitations in terms of both memory and computing capacity. Enter TinyML.

In this session you will learn about what TinyML is, what kind of problems it is suitable for and a demonstration of a practical example of developing, testing, training and deploying a TinyML model onto a physical hardware. I will be demonstrating the workflow for working with TinyML, demonstrating how we can convert a trained TensorFlow model to TensorFlow Lite and how we can deploy the TinyML onto hardware.