In recent years, the rise of Transformer models and attention mechanisms has significantly revolutionized the field of machine learning, particularly in computer vision and natural language processing. While attention-based architectures enable models to effectively prioritize and understand complex relationships within the data, deploying Transformer models on microcontrollers remains a challenge. Simultaneously, Neural Processing Units (NPUs) have become key hardware accelerators, designed specifically to optimize deep learning tasks. Their efficiency and low power consumption make them ideal for real-time applications in constrained environments. This study focuses on developing a complex deep learning pipeline that leverages attention-based models for real-time face recognition, targeting a cutting-edge microcontroller equipped with an NPU.