Being able to deploy machine learning applications at the edge bears the promise of unlocking a multi-billion dollar market. For that to happen, hardware and software must work in tandem. Arm’s partner ecosystem exemplifies this, with hardware and software vendors like Alif and Neuton working together.

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.

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