Method and implementation of intelligent embedded systems based on deep learning technology

With the rapid development of the Internet of Things and artificial intelligence technologies, intelligent embedded systems face increasingly complex challenges and demands. This paper proposes an intelligent embedded system approach based on deep learning technology and analyzes its implementation. First, the concept and characteristics of intelligent embedded systems, as well as the development and application of deep learning technology, are introduced. Next, the basic principles of deep neural networks and design optimization strategies for deep learning processors are described. Then, based on the analysis of application scenarios and cases of deep learning in embedded systems, an embedded system model based on Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Reinforcement Learning (RL), and Generative Adversarial Networks (GAN) have been developed. This model is applied to image processing, speech recognition, robot control, and data augmentation, and verified in real-world conditions. Finally, the paper summarizes the advantages and development trends of intelligent embedded systems based on deep learning technology and discusses their potential and challenges in the future. Furthermore, the paper highlights the importance of optimizing hardware-software integration to achieve enhanced performance, energy efficiency, and real-time processing in intelligent embedded systems.