Qeexo AutoML Enables Machine Learning on Arm Cortex-M0 and Cortex-M0+

September 10, 2020 Tiera Oliver

Qeexo, developer of an automated machine learning (ML) platform that accelerates the deployment of tinyML at the edge, announced that its Qeexo AutoML platform now supports machine learning on Arm Cortex-M0 and Cortex-M0+ processors.

According to the company, the Arm Cortex-M0 processor is the smallest Arm processor available, and the Cortex-M0+ processor builds on Cortex-M0 while further reducing energy consumption and increasing performance. Per the company, Qeexo is the first company to automate adding machine learning to a processor of this size. The Cortex-M0 and Cortex-M0+ processors are designed for smart and connected embedded applications, and are ideal for use in simple, cost-sensitive devices due to the lower power-consumption and ability to extend the battery life of critical use cases such as activity trackers.  

Machine learning models built with Qeexo AutoML are optimized and have a small memory footprint. Models are designed to run locally on embedded devices, ideal for low-power, low-latency applications on MCUs and other constrained platforms. 

The list of machine learning algorithms supported on Qeexo AutoML currently include: GBM, XGBoost, Random Forest, Logistic Regression, Decision Tree, SVM, CNN, RNN, CRNN, ANN, Local Outlier Factor, and Isolation Forest. Several hardware platforms from Arduino, Renesas, and STMicroelectronics work with Qeexo AutoML out-of-the-box.

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

About the Author

Tiera Oliver, edtorial intern for Embedded Computing Design, is responsible for web content edits as well as newsletter updates. She also assists in news content as far as constructing and editing stories. Before interning for ECD, Tiera had recently graduated from Northern Arizona University where she received her B.A. in journalism and political science and worked as a news reporter for the university's student led newspaper, The Lumberjack.

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