Qeexo Collaborates with STMicroelectronics to Automate Machine Learning on Machine Learning Core (MLC) Sensors

By Tiera Oliver

Associate Editor

Embedded Computing Design

May 19, 2021

News

Qeexo Collaborates with STMicroelectronics to Automate Machine Learning on Machine Learning Core (MLC) Sensors

Qeexo announced that it is working with STMicroelectronics to create machine learning models for ST’s Machine Learning Core (MLC) sensors.

Qeexo and STMicroelectronics are collaborating to enable developers to create machine learning models for ST’s MLC sensors, so that inferences can run right on the sensor, without the need for a microcontroller. This feature will be available to users beginning in Q2. 

Traditionally, due to limited computation power, memory size, and battery life, building machine learning solutions for edge devices was not ideal. Per the companies, Qeexo AutoML solves this. Its one-click, fully automated platform allows customers to build machine learning solutions for edge devices using sensor data. By moving machine learning to embedded processors and now sensors on edge devices, developers can improve privacy, latency, and availability.  

“Many IoT solutions developers are looking to easily add embedded machine learning to their very low power applications and need help to bridge the gap from concept to prototype to production,” said Simone Ferri, MEMS Sensors Division Director, STMicroelectronics. “We put MLC in our sensors to reduce system data transfer volumes and offload network processing. Qeexo AutoML can help unlock the benefits of inherently low-power sensor design, advanced AI event detection, wake-up logic, and real-time Edge computing.”  

Qeexo also announced that it is launching a model converter that can take machine learning models in the ONNX format to optimize them for embedded devices. For customers who already have a machine learning team, and who have worked on and have existing machine learning models, they can use the Qeexo Model Converter to make them smaller and more optimized for embedded devices. The technology will also be ideal for developers who want to compare the performance of their hand-built models against the models automatically created with Qeexo AutoML. 

In addition, Qeexo is now offering a machine learning consulting service to help clients jump-start their projects. Qeexo will first work with clients to develop and deploy commercial-ready machine learning solutions, then tailor Qeexo AutoML to fit customer needs. Qeexo will provide the knowledge transfer necessary for client teams to continue to use Qeexo AutoML for current and future projects. 

For more information, visit: https://qeexo.com

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Tiera Oliver, Associate Editor for Embedded Computing Design, is responsible for web content edits, product news, and constructing stories. She also assists with newsletter updates as well as contributing and editing content for ECD podcasts and the ECD YouTube channel. Before working at ECD, Tiera graduated from Northern Arizona University where she received her B.S. in journalism and political science and worked as a news reporter for the university’s student led newspaper, The Lumberjack.

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