Qeexo, the developer of the Qeexo AutoML automated machine learning (ML) platform that accelerates the development of tinyML models for the edge and STMicroelectronics announced the availability of ST’s machine learning core (MLC) sensors on Qeexo AutoML.
By themselves, ST’s MLC sensors substantially reduce overall system power consumption by running sensing-related algorithms, built from large sets of sensed data, that would otherwise run on the host processor. Using this sensor data, Qeexo AutoML can automatically generate highly optimised machine learning solutions for edge devices, with ultra-low latency, ultra-low power consumption and a notably small memory footprint. These algorithmic solutions overcome die-size-imposed limits to computation power and memory size, with efficient machine learning models for the sensors that extend system battery life.
“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.”
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