Edge Computing Device

Edge computing devices are resource-constrained hardware platforms designed to process data locally, minimizing reliance on cloud infrastructure. Current research emphasizes optimizing deep learning models, such as YOLO and MobileNet variants, for deployment on these devices, focusing on balancing accuracy, speed, and energy efficiency through techniques like quantization and neural architecture search. This work is crucial for enabling real-time applications in areas like autonomous vehicles, IoT, and robotics, where low latency and privacy are paramount. Furthermore, research explores neuromorphic computing and federated learning approaches to further enhance efficiency and security in edge deployments.