In this white paper, explore tiny ML applications using Qorvo’s highly integrated battery management and motor control ICs.
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.
Want to know more? Contact us today!
Qorvo’s PAC52410 expands Qorvo’s broad portfolio of full-featured Power Application Controller® (PAC) products – highly optimized System On Chip (SOC) for controlling and powering next
Mike Engelhardt has been designing SPICE models for 40 years. Read about his journey from rural Michigan, to living physics concepts, to building tools that
Qorvo contributed articles to this Springer SiC book from editor Maurizio Di Paolo Emilio. Find out about the transformative role of SiC in power conversion
By leveraging Qorvo’s advanced SiC FET technology, Qorvo’s SiC modules significantly enhance thermal performance through reduced R DS(on) and switching losses. Discover how these easy-to-use
