Abstract: With the development of artificial intelligence (AI) technologies, the proportion of related courses and technological applications in university curricula has been increasing, leading to a surge in students’ demand for AI experimentation. However, the current AI experimental systems are not well-established, with most experiments being software-based and lacking hardware platforms and projects, which results in a disconnect from real-world engineering applications and industry needs. This experimental instruction integrates the CDIO (conceive, design, implement, operate) model, and to address issues such as slow inference speeds and long processing times in deep learning networks during experiments, an AI experiment based on the Huawei Ascend Atlas 200DK hardware platform is proposed, which utilizes deep learning networks to perform colorization of grayscale images. Through this experiment, students can learn about the principles of AI, the structure and inference processes of neural networks, set up software development environments, configure hardware systems, and deploy neural networks that integrate both software and hardware. The experiment also involves the completion of functional and performance testing. This hands-on experience helps students understand the operational mechanisms of neural networks from a practical application perspective, master their application methods on actual hardware platforms, and thereby enhance their overall engineering and practical skills in both software and hardware.