TinyML (Tiny Machine Learning) Transforms Edge Computing

Typically, you need high computing power to deploy and run a machine learning model. GPU (Graphics Processing Unit) is designed to perform floating-point operations, unlike CPU, which fulfills more diverse tasks. GPUs help implement machine learning algorithms because of their ability to perform complex mathematical calculations. Microcontrollers do not contain enough resources to run the typical machine learning algorithms. The computing power of microcontrollers is much lower than GPUs, which is why a standard ML algorithm is not executable on such resource-constraint hardware.