Infineon and SensiML enable sensor data capture and ML models for smart home, fitness and industry applications; announce design challenge

Market News

Feb 08, 2022

Munich, Germany – 8 February, 2022 – Infineon Technologies AG (FSE: IFX / OTCQX: IFNNY) is collaborating with SensiML, a leading developer of AI tools for building intelligent Internet of Things (IoT) endpoints. Together they offer developers an easy and seamless process to capture data from Infineon XENSIV™ sensors, train Machine Learning (ML) models, and deploy real-time inferencing models directly on ultra-low power PSoC™ 6 microcontrollers (MCUs). This can be completed by using the SensiML Analytics Toolkit and ModusToolbox™. This collaboration offers designers the right tools to develop smart applications for IoT devices spanning the smart home, industrial and fitness sectors.

Infineon is also hosting the “Build AI for the IoT Design Challenge,” launched on hackster.io February 3, 2022, where innovative developers are challenged to use a combination of tools to develop new ML/AI solutions. Design challenge participants will use Infineon's ModusToolbox ML, ultra-low power PSoC 6 technology and CAPSENSE™ capacitive sensing combined with the robust connectivity of the AIROC™ wireless connectivity solutions and industry-leading sensors in the XENSIV™ family. SensiML software provides participants the necessary firmware and data science tools to capture and label data from sensors. Additionally they can use an AutoML cloud platform to train models without extensive AI expertise, and then deploy the resulting models on the PSoC 6 MCU for use on edge devices.

“Capturing the right data for smart home, fitness and industrial IoT devices, and implementing this information to train ML modules is a complex but key process for OEMs to make their devices smarter,” said Steve Tateosian, Vice President of IoT Compute and Wireless Business Line of Infineon. “Our collaboration with SensiML enables just that by combining their ML/AI software tools with our proven PSoC 6 MCUs, connectivity and sensor solutions to simplify the complexity of creating intelligent IoT solutions. Through this collaboration, we look forward to offering a complete suite of the right tools to enable developers and design challenge participants to create tomorrow’s smart IoT devices for a variety of sectors.”

“Infineon offers a broad range of low-power processors and cutting-edge sensors beneficial to developers across a vast array of embedded IoT applications,” said Chris Rogers, CEO of SensiML. “Our SensiML Data Analytics toolkit can help customers using those Infineon products to quickly and easily implement AI/ML capabilities to transform sensor-enabled devices into smart IoT solutions.”

Ideal for battery-powered applications, Infineon’s PSoC 6 MCUs are built on an ultra-low-power architecture featuring low-power design techniques. The dual-core Arm® Cortex®-M4 and Cortex-M0+ architecture allow designers to optimize power and performance simultaneously. Using its dual cores combined with configurable memory and peripheral protection units, the PSoC 6 MCU delivers the highest level of protection defined by the Platform Security Architecture (PSA) from Arm. With SensiML’s AI software tools, customers can now capture the right sensor data and create ML models to run on the PSoC 6 MCU.

Availability

The Infineon PSoC 6 evaluation boards CY8CKIT-062S2-43012 and CY8CKIT-028-SENSE are available now. More information is available at:

A free trial account with SensiML is available at: https://sensiml.com/plans/community-edition/.

Press Photos

Infineon and SensiML

Infineon and SensiML

Infineon and SensiML provide developers with an easy and seamless process to capture data from Infineon XENSIV™ sensors, train Machine Learning models, and deploy real-time inferencing models directly on ultra-low power PSoC™ 6 microcontrollers. This collaboration offers designers the right tools to develop smart applications for IoT devices spanning the smart home, industrial and fitness sectors.

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Fabian Schiffer

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