Netherlands Nxp Microsoft Tender

NXP Semiconductors N.V.announced collaboration with Microsoft to bring Artificial Intelligence AI and Machine Learning ML capabilities for anomaly detection to Azure IoT users. By combining complementary strengths NXPs offline machine learning capability and embedded processing with Microsofts cloud expertise the two companies jointly demonstrate a new Anomaly Detection Solution for Azure IoT at Microsoft Build in Seattle WA from May 6-8.The solution consists of a small form factor low power System-on-Module SOM powered by NXPs i.MX RT106C Crossover Processors a full suite of sensors and an associated Anomaly Detection Toolbox. The toolbox utilizes various ML algorithms such as Random Forest and Simple Vector Machine SVM to model normal behavior of devices detect anomalous behavior through a combined local and cloud mechanisms. This allows much lower cloud bandwidth requirements while maintaining full online logging and processing capabilities at a fraction of the cost. Applications include predictive maintenance for rotating components presence detection and intrusion detection. Preventing failures and reducing downtime are key to enhance productivity and system safety said Denis Cabrol executive director and general manager of IoT and Security Solutions at NXP. We partnered with Microsoft to combine the power of Azure IoT with local intelligence running on NXPs embedded technology to unlock innovation for the IoT as part of our continued efforts to bring cognitive services down to the silicon.We are proud to expand our collaboration with NXP to include the new Azure IoT and i.MX RT106C Anomaly Detection Solution said Rodney Clark Vice President IoT Sales Microsoft. Solutions like this from NXP empower developers with products tools and services to accelerate development of complete edge to cloud solutions.NXPs cost-effective anomaly detection solution is designed with a robust set of sensors and high-performance i.MX RT106C crossover microcontroller MCU running up to 600MHz capable of collecting and analyzing sensor data in real time locally at the edge. The solution seamlessly connects to the Azure IoT Cloud providing customers an easy way to transfer data to the cloud where they can visualize the data and utilize powerful data analytical tools to train behavior prediction models for deploying on edge devices.