Machine learning, edge computing and 32 bits

It was a pleasure to take part in the ANNPR conference together with ScorePad. ANNPR stands for Artificial Neural Networks in Pattern Recognition, in other words pattern recognition with neural networks. Thanks again to the colleagues at our long-standing partner ZHAW for organising it so well. Working with you is fun!

For us, with our embedded focus, it gets interesting as the overlap grows between microcontrollers (32 bit at 240 MHz or less, say) and machine learning algorithms. What the microcontroller does varies a great deal with the task and the setting. Sometimes it is no more than a sensor data interface to a cloud database, which for us means collecting data, checking it for plausibility, encrypting it and sending it over the protocol of choice. The rest is then typically handled by web services or apps.

By contrast, edge computing is coming more and more to the fore: the collected data is pre-processed and only then sent to the server for final classification. A fine example is Google's Edge TPU, which grew out of the offline speech recognition in the Google Assistant on Android devices and now makes machine learning tasks feasible at the lower end of embedded performance as well.

Do you have an embedded task that touches on machine learning? Talk to us.

Wishing you plenty of good lateral thinking,

querdenker engineering