Infineon Technologies

Infineon customers can now use SensiML's tools to easily create intelligent IoT endpoints using the ultra-low power and versatile dual-core PSoC™ devices

Introducing the PSoC™ 6 MCU Platform

SensiML has teamed with Infineon to provide a complete AI/ML solution for the Infineon PSoC™ 6 family of MCUs and XENSIV™ sensors. As a result, designers with little to no data science expertise can now add local intelligence to their IoT designs for smart home, industrial, fitness, and other applications.

Further information is available at

The PSoC™ 6 family is the perfect solution for secure, low-power, feature-rich IoT products. The family is built on an ultra-low-power architecture, including advanced low-power design techniques to extend battery life up to a full week for battery powered applications. The dual-core Arm® Cortex®-M4 and Cortex-M0+ architecture lets developers optimize for power and performance simultaneously. Using its dual cores combined with configurable memory and peripheral protection units, PSoC™ 6 enables Platform Security Architecture (PSA) level 2 certified MCUs.
Designers can use the MCU’s rich analog and digital peripherals to create custom analog front-ends (AFEs) or digital interfaces for innovative system components such as MEMS sensors, electronic-ink displays. Through comprehensive software support available in ModusToolbox™, PSoC™ 6 MCUs, pair seamlessly with Infineon’s AIROC™ Wi-Fi, AIROC Bluetooth®, or AIROC combos radio modules. The PSoC™ 6 MCUs feature the latest generation of industry-leading CapSense capacitive-sensing technology, enabling modern touch and gesture-based interfaces that are robust and reliable.
Predictive maintenance techniques detect anomalies in equipment before those turn into system-critical failures, allowing maintenance to be scheduled before the equipment actually breaks down. Monitoring the condition of equipment make use of various sensors for vibration analysis, sound anomaly detection, current sensing etc. to gain meaningful insights into equipment’s health. Deploying predictive maintenance algorithms directly on Edge devices can significantly reduce the data transfer and connectivity requirement of the final system by being able to classify commonly known anomalies and send lower probability cases to the cloud for further analysis.
Machine Learning for the Edge has particularly broad applications when processing and combining data from various sensors can detect specific events. A few key example of these are Glassbreak detection using audio sensors and Radar-based presence detection.