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Design and Implementation of the STM32N6-Based Modular Embedded Edge AI Teaching Platform for Engineering Education
Zixuan Wang1, Liguo Liu1, Ping Wang2
1School of Engineering, Naval University of Engineering, Wuhan 430030, China.
Sensors (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a new embedded edge-AI teaching platform for engineering education, offering low-power, real-time intelligence. The reliable platform efficiently handles on-device inference for AI projects and competitions.
Area of Science:
- * Computer Engineering
- * Artificial Intelligence (AI)
- * Embedded Systems
Background:
- * Growing demand for low-power, real-time edge intelligence in engineering education.
- * Need for deployable AI solutions suitable for classroom projects and competitions.
- * Limitations of existing platforms in handling complex AI tasks efficiently on edge devices.
Purpose of the Study:
- * To present a novel modular embedded edge-AI teaching platform based on the STM32N6 microcontroller.
- * To demonstrate the platform's capability for efficient on-device AI inference.
- * To validate its reliability and suitability for educational purposes in embedded AI computing.
Main Methods:
- * Development of a heterogeneous architecture integrating an ARM Cortex-M55 core and a Neural-ART Neural Processing Unit (NPU).
- * Implementation of a multi-power-domain supply architecture for enhanced stability and protection.
- * Integration of high-speed modular peripherals (MIPI-CSI, RGB display, NOR Flash, expansion interfaces) on a four-layer PCB.
Main Results:
- * Achieved an inference-only latency of 18.4 ms using a custom lightweight vision algorithm (PEPoseNet).
- * Demonstrated an energy efficiency of 28.31 Frames Per Second per Watt (FPS/Watt).
- * Operated within a sub-3 Watt power budget, confirming low-power capabilities.
Conclusions:
- * The developed platform is a reliable and efficient educational tool for embedded edge-AI computing.
- * It effectively supports on-device inference for both educational projects and competitive tasks.
- * The platform addresses the need for accessible and performant edge AI solutions in engineering education.
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