Machine Learning In Intelligent Power Management Systems

Machine learning for microcontroller units, or tiny ML, is a rapidly advancing field with significant implications for battery management and motor control. By utilizing ML algorithms, we can derive valuable insights from complex sensor data, optimizing system performance and enhancing our understanding of overall system health.

The rise of AutoML tools has further expedited the development of ML-driven solutions. These tools automate various stages of the machine learning process, such as data collection, algorithm training, and firmware generation, making it easier for developers to integrate ML into power management systems.

Qorvo's intelligent power management system ICs provide a strong foundation for creating ML applications. By combining these ICs with tiny ML and AutoML capabilities, innovative solutions can be developed to improve battery efficiency, optimize motor control, and enhance the overall performance of power management systems.

This paper delves into the development of ML applications using Qorvo's intelligent power management ICs, emphasizing the potential benefits and advancements that arise from integrating machine learning into power management systems.