The collaboration will see SensiML’s Analytics Toolkit development software combined with the RSL10 Sensor Development Kit from onsemi creating a platform for edge sensing applications such as industrial process control and monitoring.
SensiML is able to support AI functions in a small memory footprint and, along with the advanced sensing and Bluetooth Low Energy connectivity provided by the RSL10 platform, without the need for cloud analytics and highly dynamic raw sensor data.
Featuring 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 small form-factor board that interfaces with the SensiML Toolkit.
Developers using the RSL10-based platform and the SensiML software together can add low latency local AI predictive algorithms to their industrial wearables, robotics, process control, or predictive maintenance applications regardless of expertise in data science and AI. The auto-generated code enables smart sensing embedded endpoints that are are able to 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 is too slow, too remote and too unreliable for the most 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 failures and personnel staying safe and productive.”
“Other AutoML solutions for the edge rely on neural network classification models with only rudimentary AutoML provisions, yielding suboptimal code for a given application,” said 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.”
