Machine Learning in Intelligent Power Management Systems

This white paper discusses the application of machine learning (ML) in intelligent power management systems, particularly focusing on MCU implementation, often referred to as tiny ML. It outlines how ML algorithms can analyze complex sensor data to optimize performance and enhance system health understanding. The paper highlights the rise of AutoML tools that automate data collection, ML algorithm training, and MCU firmware generation. It details the development process of ML applications using Qorvo's power management system integrated circuits, which combine Arm Cortex MCUs with various sensors. The document explains the importance of data quality for effective ML model training and describes the iterative ML development flow, known as MLOps, which includes data collection, model training, deployment, and performance monitoring. Additionally, it presents an implementation example focusing on weak-cell detection in battery management systems, illustrating how ML can improve safety and efficiency in power management applications.