Machine learning at the edge: A hardware and software ecosystem

The idea of taking compute out of the data center, and bringing it as close as possible to where data is generated, is seeing lots of traction. Estimates for edge computing growth are in the 40% CAGR, $50 billion area. Increasingly, data generated at the edge are used to feed applications powered by machine learning models. TinyML is a fast-growing field of machine learning technologies and applications that enable machine learning to work at the edge. It includes hardware, algorithms and software capable of performing on-device sensor data analytics at extremely low power, hence enabling a variety of always-on use-cases.