tinyML Talks webcast - recorded November 24, 2020
"Amber: A Complete, ML-Based, Anomaly Detection Pipeline for Microcontrollers"
Brian Turnquist and Rodney Dockter - Boon Logic
Sensor anomaly detection pipelines deployable on microcontrollers typically begin with data collection which is followed by off-line training and model-building on multi-core, high performance compute resources. The resulting model is static and may require additional pruning prior to deployment. Furthermore, the model may not translate to other sensors, even identical sensors monitoring identical assets running the same motion profiles. This talk will demonstrate a complete, unsupervised machine learning-based, anomaly detection pipeline that is deployable on low-power microcontrollers such as the ARM Cortex M7. Using live sensor values in real-time, the Amber algorithm seamlessly tunes its hyperparameters, then trains its ML model, and finally transitions to anomaly detection mode where it can generate thousands of inferences per second with extremely high accuracy.
