SensiML & onsemi Collaborate on Industrial Edge AI Sensing Solutions
SensiML™ Corporation, a developer of AI tools for building intelligent Internet of Things (IoT) endpoints and onsemi have partnered to deliver a 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 for edge sensing applications such as industrial process control and monitoring.
SensiML, a subsidiary of QuickLogic, offers software that enables ultra-low-power IoT endpoints that implement AI to transform raw sensor data into meaningful insight at the device itself. The company’s flagship solution, the SensiML Analytics Toolkit, provides an end-to-end development platform spanning data collection, labelling, algorithm and firmware auto-generation, and testing.
“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 analyzing 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”.
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.
Featuring the industry’s lowest 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 form-factor board that interfaces readily 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 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 reduce network traffic by communicating data only when it offers valuable insight.
“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”, 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”.
The SensiML Analytics Toolkit and the RSL10 Sensor Development Kit from onsemi are both available immediately from their respective companies.
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