Embedded System

Embedded systems research centers on designing and deploying computationally efficient AI algorithms, particularly deep learning models, onto resource-constrained hardware platforms like microcontrollers and specialized processors. Current efforts focus on model compression techniques (e.g., quantization, pruning), efficient architectures (e.g., lightweight CNNs, Transformers), and optimized hardware accelerators to enable real-time performance for applications such as computer vision, natural language processing, and sensor data analysis. This field is crucial for advancing AI's reach into various domains, including robotics, IoT devices, and wearable technology, by enabling intelligent functionalities in power- and computation-limited environments.