April 30, 2026 -
MCUs with efficient AI accelerators and maturing TinyML frameworks enable more complex models on low-power devices.
By Giordana Francesca Brescia, embedded.com
Machine learning (ML) has progressively moved from the cloud to edge computing to reduce latency in decision-making, lower power consumption, and decrease the dependence on network connections, especially in battery-powered devices. In many applications, relying on the cloud can introduce delays that are incompatible with real-time systems or can lead to data privacy issues.
The latest generation of microcontrollers (MCUs) are platforms that bring artificial intelligence (AI) directly to embedded devices. Due to their widespread use and low cost, MCUs are suited for implementing local inference capabilities. This is the foundation of TinyML, which allows ML models to run on hardware that is extremely limited in terms of memory and computational capacity.
The result is a change in perspective. Systems are no longer dependent on remote infrastructures; they are autonomous, responsive, and intelligent devices, which can make decisions in real time directly in the field.