Computer/Embedded Technology


Full AI/machine learning sensing solution

24 November 2021 Computer/Embedded Technology

SensiML, a developer of AI tools for building intelligent Internet of Things (IoT) endpoints, has teamed up with onsemi to deliver a complete machine learning solution for autonomous sensor data processing and predictive modelling.

The collaboration combines SensiML’s Analytics Toolkit development software with the RSL10 Sensor Development Kit from onsemi to create a platform 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, enables sophisticated smart sensing without the need for cloud analytics of highly dynamic raw sensor data.


RSL10 Sensor Development Kit.

Featuring very low-power Bluetooth Low Energy connectivity, the RSL10 Sensor Development Kit combines the RSL10 radio with a full range of environmental and inertial motion sensors onto a tiny 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 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,” said Dave Priscak, vice president of applications engineering at onsemi. “The difference between analysing a key event with local machine learning versus remote cloud learning can equate to production staying online, equipment not incurring expensive downtime and personnel remaining safe and productive.”

Other AutoML solutions for the edge rely on only neural network classification models with rudimentary AutoML provisions, yielding suboptimal code for a given application, according to 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 SensiML Analytics Toolkit and the RSL10 Sensor Development Kit from onsemi are both available immediately from their respective companies. For more information, visit the SensiML information page at https://sensiml.com/partners/onsemi




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