Machine learning at the edge: A hardware and software ecosystem
Edge computing is booming. 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.
Whether it's stand-alone IoT sensors, devices of all kinds, drones, or autonomous vehicles, there's one thing in common. 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.
In order for TinyML to work, a confluence of hardware and software is needed, creating an ecosystem built around the notion of frugal energy needs. This is a prerequisite for applications at the edge.
Today Arm, a global semiconductor IP provider known for its focus on ecosystem creation and frugal energy needs for its processors, is announcing a partnership with Neuton, a provider of an automated TinyML platform. Earlier in September, Alif Semiconductors, another Arm partner building AI chips for the edge, released new product lines.