ST's machine learning capable (MLC) sensors can reduce system-level power consumption by running sensing-related algorithms. Using sensor data, Qeexo's automated machine-learning (ML) platform can accelerates the development of tinyML models for the Edge with reduced latency, power consumption, and memory footprint.
"Our work with ST has now enabled application developers to quickly build and deploy machine-learning algorithms on ST's MLC sensors without consuming MCU cycles and system resources, for an unlimited range of applications, including industrial and IoT use cases," said Sang Won Lee, CEO of Qeexo in the US.
"Putting MLC in our sensors, including the LSM6DSOX or ISM330DHCX, significantly reduces system data transfer volumes, offloads network processing, and potentially cuts system power consumption by orders of magnitude while delivering enhanced event detection, wake-up logic, and real-time Edge computing," said Simone Ferri, director of the MEMS sensors division of STMicroelectronics.
Qeexo claims that more than 300 million devices have been shipped with AI built on Qeexo AutoML.
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