Executive Summary

Machine learning for MCU implementation (tiny ML) is a growing field that offers new and enhanced functionality for battery management and motor control. ML algorithms discover information and patterns in complex sensor data that can be used to optimize performance and improve understanding of overall system health. In addition to advances in tiny ML techniques, the availability of AutoML tools that automate the collection of data, training of ML algorithms, and generation and deployment of MCU firmware is on the rise. Such tools, combined with access to system on chip (SoC) sensor data, enable the development of ML-based solutions in today’s power management systems. This paper discusses the development of machine learning (ML) applications using Qorvo’s intelligent power management systems ICs. Qorvo's highly integrated power management SoCs combine Arm^® Cortex^® M0 and M4F MCUs with an analog front end with an array of sensors to enable smart control and monitoring functions.

Introduction

Learning from Data
Machine learning models derive their performance directly from data, which is an advantage when the data is complex, high-dimensional, or difficult for a human to determine an optimal algorithm. However, reliance on data means that a good outcome depends on access to good training data – data that is representative of actual device behavior across different environmental conditions and manufacturing tolerances. Data collection is often the most expensive and time-consuming phase of an ML project. In cases where the costs are prohibitive, synthetic data from physical models can satisfy requirements.

Qorvo’s evaluation kit includes a graphical user interface (GUI) that incorporates data logging features to facilitate the data collection process. The same sensor data available to the internal MCU can be saved to files on a computer for offline model training and testing. Some AutoML (automated ML) tools also support live data collection and model testing by integrating hardware abstraction layers and data streaming functions to the evaluation firmware.