Adapting the Microcontroller for AI in the Endpoint

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eetimes.com, Feb. 19, 2020 – 

What do you get if you cross AI with the IoT? The AIoT is the simple answer, but you also get a huge new application area for microcontrollers, enabled by advances in neural network techniques that mean machine learning is not limited to the world of supercomputers any longer. These days, smartphone application processors can (and do) perform AI inference for image processing, recommendation engines, and other complex features.

Bringing this kind of capability to the humble microcontroller represents a huge opportunity. Imagine a hearing aid that can use AI to filter background noise from conversations, smart home appliances that can recognise the user's face and switch to their personalized settings, and AI-enabled sensor nodes that can run for years on the tiniest of batteries. Processing the data at the endpoint offers latency, security and privacy advantages that can't be ignored.

However, achieving meaningful machine learning with microcontroller-level devices is not an easy task. Memory, a key criteria for AI calculations, is often severely limited, for example. But data science is advancing quickly to reduce model size, and device and IP vendors are responding by developing tools and incorporating features tailored for the demands of modern machine learning.

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