This week I had the pleasure to sit down with Ross Sabolcik, Vice President and General Manager of Industrial and Commercial IoT products at Silicon Labs, to discuss IoT, machine learning, and what is possible with the latest hardware and software advances in both.
The discussion was part of a packed agenda put on by Silicon Labs to showcase all of the new products, technology, and initiatives it and its ecosystem of partners have been busy developing over the past year to drive the IoT. While no doubt their big focus was on the impending Matter 1.0 specification and Silicon Labs’ SoC support for this much-anticipated smarthome interoperability standard, there were also exciting new developments from Silicon Labs on the AI/ML front as well.
Over the summer, Silicon Labs introduced its EFR32MG24 that includes a hardware-based AI accelerator core. SensiML was proud to be an early alpha partner of Silicon Labs to integrate support for this AI accelerator and offer developers the means to rapidly create predictive sensor models that benefit from the power, performance, and latency reduction benefits of this new hardware.
Whether for smarthome applications, predictive maintenance, factory automation process control, or anomaly detection, there are any number of use cases where intelligent adaptive edge predictive algorithms can be used to provide new insight without the issues typically faced with relying solely on centralized cloud AI for data analytics.
Watch the video of my conversation with Ross below to hear more about the benefits of AI at the edge, key use cases, and how Silicon Labs and SensiML are working together to advance IoT smart sensing with ML dev workflows designed to take the complexity out of gaining sensor insight.
Missed the Silicon Labs Works With conference?
Register to watch on-demand AI/ML video material from Silicon Labs’ Works With conference including SensiML’s workshop
“Building Multi-Sensor Algorithms for the IoT Edge