Qeexo is the developer of Qeexo AutoML Automated Machine-Learning (ML) platform that speeds up the creation of models using tinyML for the Edge as well STMicroelectronics (NYSE: STM) the world’s leading semiconductor company providing customers with a wide range of electronic applications, recently announced that ST’s machine learning base (MLC) sensors for Qeexo AutoML.
As a set by themselves, ST’s MLC sensors significantly reduce the power consumption of the system through the use of sensing-related algorithms constructed from huge sets of data that are sensed, which could otherwise be operate on the processor of host. By utilizing the sensor information, Qeexo AutoML can automatically develop highly optimized machine learning strategies for Edge devices that have ultra-low power consumption, extremely low latency and a tiny footprint of memory. These algorithms overcome die size-imposed limitations on computation energy and the size of memory using efficient machine-learning models to the sensors , which extends system battery lifespan.
“Delivering on the promise we made recently when we announced our collaboration with ST, Qeexo has added support for ST’s family of machine-learning core sensors on Qeexo AutoML,” said Sang Won Lee, CEO of Qeexo. “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.”
“Adapting Qeexo AutoML for ST’s machine-learning core sensors makes it easier for developers to quickly add embedded machine learning to their very-low-power applications,” said Simone Ferri, MEMS Sensors Division Director, STMicroelectronics. “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.”
