Just a few years ago, it was assumed that machine learning (ML) — and even deep learning (DL) — could only be performed on high-end hardware, with training and inference at the edge executed by gateways, edge servers, or data centers. It was a valid assumption at the time because the trend toward distributing computational resources between the cloud and the edge was in its early stages. But this scenario has changed dramatically thanks to intensive research and development efforts made by industry and academia.

The result is that today, processors capable of delivering many trillions of operations per second (TOPS) are not required to perform ML. In an increasing number of cases, the latest microcontrollers, some with embedded ML accelerators, can bring ML to edge devices.

Not only can these devices perform ML, they can do it well, at low cost, with very low power consumption, connecting to the cloud only when absolutely necessary. In short, microcontrollers with integrated ML accelerators represent the next step in bringing computing to sensors such as microphones, cameras, and those monitoring environmental conditions, that generate the data upon which all the benefits of IoT are realized.

For more information visit  https://www.eetimes.com/deep-learning-on-mcus-is-the-future-of-edge-computing/