Industrial edge AI sensing solution from SensiML, onsemi

November 04, 2021 // By Rich Pell
Industrial edge AI sensing solution from SensiML, onsemi
IoT analytics toolkit developer SensiML Corporation has announced that it has teamed with semiconductor supplier onsemi on a complete machine learning solution for autonomous sensor data processing and predictive modeling.

The collaboration combines SensiML's Analytics Toolkit development software with onsemi's RSL10 Sensor Development Kit to create a platform that is offered as ideal for edge sensing applications such as industrial process control and monitoring. SensiML's ability to support AI functions in a small memory footprint, along with the advanced sensing and Bluetooth Low Energy connectivity provided by the RSL10 platform, is designed to enable sophisticated smart sensing without the need for cloud analytics of highly dynamic raw sensor data.

"Other AutoML solutions for the edge rely only on neural network classification models with only rudimentary AutoML provisions, yielding suboptimal code for a given application," says Chris Rogers, SensiML's CEO. "Our comprehensive AutoML model search includes not only neural networks, but also an array of classic machine learning algorithms, as well as segmenters, feature selection, and digital signal conditioning transforms to provide the most compact model to meet an application's performance need."

The RSL10 Sensor Development Kit combines the RSL10 radio with a full range of environmental and inertial motion sensors onto a tiny form-factor board that interfaces readily with the SensiML Toolkit. Developers using the RSL10-based platform and the SensiML software together can easily add low latency local AI predictive algorithms to their industrial wearables, robotics, process control, or predictive maintenance applications regardless of their expertise in data science and AI.

The resulting auto-generated code enables smart sensing embedded endpoints that transform raw sensor data into critical insight events right where they occur and can take appropriate action in real time. Furthermore, the smart endpoints also drastically reduce network traffic by communicating data only when it offers valuable insight.

"Cloud-based analytics add unwanted, non-deterministic latency, and are too slow, too remote and too unreliable for critical industrial processes," says Dave Priscak, vice president of Applications Engineering at onsemi. "The difference between analyzing a key event with local machine learning versus remote cloud learning can equate to production staying online, equipment

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