What It Really Takes for a General-Purpose MCU to Function as a Complete AI SOC

Innovative AI-enabled MCUs are transforming endpoint devices by integrating advanced AI capabilities while addressing critical challenges in power and space. This article explores the future of wearable technology and the engineering solutions that make it possible.

- Mark Rootz

The application of AI to endpoint devices can transform their value. In the medical arena, wearable AI devices promise to take the detection and diagnosis of serious conditions such as atrial fibrillation out of the clinic and into the patient's everyday world for monitoring. And by applying AI algorithms, hearing aids can be transformed from a simple amplifier into an intelligent vocal discriminator, isolating the voice of the person of interest, while cancelling all other noise and voices, or muting them in the sonic background.

These AI capabilities could multiply the value of almost every type of wearable and portable battery-powered product. And at the endpoint, AI functions can often not be performed in the cloud for reasons of power, latency, privacy, wireless reach, security and cost. Such devices need local AI processing capability.

But the successful local deployment of AI in these products must find a way past the severe design constraints of space and power. Wearable devices such as earbuds, rings, smart glasses, and patient monitors have small enclosures which can accommodate few components and only a small battery.

Pre-AI, many of these types of products (although endowed with fewer features and physically larger) were based on general purpose microcontrollers or microprocessors which could integrate the main functions. Integration helps the designer meet their goals for space and power while reducing component count and board footprint.

In the AI era, integration of system functions into a general-purpose MCU remains a highly desirable way to save space and power. But an AI MCU must integrate even more functions than earlier conventional MCUs, while at the same time providing the ultra-low power consumption that enables devices with small batteries to run AI at the endpoint without sacrificing run-time between charges.