Transformer-Level Face Recognition on a Microcontroller - Real-Time Implementation with STM32N6

Learn how to implement transformer-level face recognition on a microcontroller in this technical walkthrough demonstrating real-time performance on the STM32N6 platform. Discover how Software Engineer Davide Aiello from Sensor Reply achieved 25 FPS face recognition running entirely on-device with no cloud dependency, fitting within a 4MB RAM footprint. Explore the complete pipeline architecture combining a RetinaFace-style detector, MobileNetV2 anti-spoofing system, and EdgeFace recognizer that delivers both accuracy and speed. Understand how the STM32N6's NPU provides 600 GOPS at low power consumption, and see how unsupported dot-product attention was creatively reimagined using convolutional self-attention to work within embedded constraints. Examine the 512-dimension embedding approach that enables fast, privacy-first face recognition at the edge, with detailed benchmarks proving the system's performance in a constrained embedded environment. Gain insights into model design principles and silicon alignment strategies that unlock powerful, private, and performant AI applications for embedded engineers and edge AI developers.