At STMicroelectronics’ media briefing, a new STM32 MCU series was unveiled, set to influence edge AI. Want to know more about the event? Read here!
On January 22, 2025, STMicroelectronics held a media briefing where it shared its vision for using its expertise in processing to address the edge AI market across industrial, business technology, and consumer electronics sectors. During the event, STMicroelectronics introduced a new STM32 microcontroller (MCU) series, designed to create opportunities in these industries. The company highlighted how the new microcontroller will support edge AI applications, meeting demand in various technology sectors.
Sridhar Ethiraj, Technical Marketing – General Purpose MCU at STMicroelectronics India, represented the company at the event. He discussed the new edge AI series within the STM32 MCU family, the most powerful series created by ST to date. Ethiraj noted that the new series would bring machine learning (ML) capabilities for embedded devices, delivering AI to cost-sensitive and power-conscious customers.
The presentation began with an overview of edge AI’s importance and its potential applications, before highlighting the key features of the STM32N6 MCU. Ethiraj revealed that the product is offered in two versions: one with STMicro’s Neural ART Accelerator and one without, providing users flexibility based on their needs and applications.
When comparing the new MCU to previous STM32 MCUs, Ethiraj explained that the new version improves processing speed and offers additional features, including an H.264 encoder, 1 Gigabit Ethernet with TSN, and CSI-2, expanding possibilities for customers.
Addressing questions about the limitations of the new MCU plus NPU series, he mentioned that the company is still working on improvements and will continue innovating with the support of its R&D team.
The event concluded with a demonstration of an STM32N6-based sample showcasing object detection. Using the NPU, the sample was able to detect multiple objects simultaneously, with noticeable speed in the detection process.
