Aspinity and Infineon Partner to Accelerate Development of Intelligent Sensing Products with Longer-Lasting Batteries

Collaboration facilitates ultra-low-power, high-performance always-on sensing for portable IoT and consumer applications


PITTSBURGH, May 14, 2020 (GLOBE NEWSWIRE) -- Aspinity, a pioneer in power-efficient analog edge processing, today announced a partnership with Infineon Technologies AG that will speed development of battery-operated always-on sensing products for consumer and Internet of Things (IoT) applications. The combination of Aspinity’s Reconfigurable Analog Modular Processing (RAMP) technology with Infineon’s XENSIV™ family of sensors enables devices that continuously gather information from their environment without compromising on battery life.

“As one of the world’s leading semiconductor suppliers, Infineon Technologies consistently sets the bar for the high-performance MEMS sensors used to interface smart electronic products with the world,” said Tom Doyle, founder and CEO, Aspinity. “We are thrilled to partner with such an innovative company as they deliver their extensive portfolio of sensors for intelligent always-on products, whether they’re worn on the body, embedded in a smart home system, or in a smart factory. We look forward to collaborating with Infineon to enable customers to overcome the power challenges associated with integrating high-performance always-on sensing into a growing array of battery-operated always-on products.”

“Infineon’s high-performance XENSIV™ sensors allow electronic devices to see, hear, feel, and understand their environment — attributes that have become increasingly important for our customers,” said Rosina Kreutzer, director of business development at Infineon Technologies AG. “Combining Aspinity’s RAMP IC and Infineon’s XENSIV™ sensors promotes high accuracy in combination with power efficiency in a broad range of always-on smart products. Our aim is to delight users with the features and functionality they can rely on.”

About RAMP

Aspinity’s RAMP chip is the world’s first compact, ultra-low-power, analog machine learning chip that can analyze raw, unstructured analog sensor data to determine which data are important at the start of the signal chain — introducing an architectural approach to system design that saves significant battery power in end devices. Functioning like an intelligent gate keeper, the RAMP chip analyzes analog data from Infineon’s best-in-class XENSIV™ MEMS sensors to determine what is relevant. The RAMP chip then triggers the analog-to-digital converter and downstream digital signal processor or microcontroller to perform more complex analysis only on the relevant data, eliminating the power inefficiencies typical of other systems that waste power digitizing all of the data, relevant or not. Since designers can easily program a RAMP chip for application-specific inferencing, the combination of Aspinity’s RAMP chip with Infineon’s XENSIV™ sensors can facilitate a power-efficient analyze-first architecture in a whole new generation of small, power- and data-efficient always-on devices.

About Aspinity
Founded in 2015, Aspinity is transforming the design of always-on sensing devices that run on battery through the world’s first ultra-low power, analog machine learning RAMP chip. With its patented technology, Aspinity continues to develop solutions that support the company’s mission to offer power- and data-efficient solutions for voice/sound wake-up, industrial machine health monitoring, wearable health monitoring, and other applications in IoT, consumer, industrial and biomedical markets.

To learn more about Aspinity, visit www.Aspinity.com.

XENSIV is a trademark of Infineon Technologies AG. All other product and company names are trademarks or registered trademarks of their respective holders.

Press Contacts:

Marcie Weinstein
Aspinity
Email: marcie@aspinity.com

Maria Vetrano
Vetrano Communications
Email: maria@vetrano.com

A video accompanying this announcement is available at: https://www.globenewswire.com/NewsRoom/AttachmentNg/06c59a97-5857-46bc-8ee5-939e2d477a03