AI on IoT is moving from the cloud to the edge, running models closer to the data. Traditionally the hardware to run these models on the edge has been powerful, with GPUs or compute sticks. But what if you could run a model in only a few kilobytes of memory on a tiny micro-controller drawing less than a milliwatt of power? In this video we look at doing just that, training a wake word model in the cloud using Azure ML Studio, then compressing it to 18KB and running it on an Adafruit EdgeBadge, a small, low-powered micro-controller based device.
For more tips like this, check out the working remotely playlist at www.youtube.com/FoetronAcademy.
Also, if you need any further assistance then you can raise a support ticket (https://cloud.foetron.com/) and get it addressed.
Was this article helpful?
That’s Great!
Thank you for your feedback
Sorry! We couldn't be helpful
Thank you for your feedback
Feedback sent
We appreciate your effort and will try to fix the article