Edge Offloading

Edge offloading optimizes the execution of computationally intensive tasks, particularly deep neural networks (DNNs), by transferring processing from resource-constrained devices to more powerful edge servers. Current research focuses on developing efficient algorithms, such as deep reinforcement learning (DRL) and techniques like split computing and coordinated DNNs, to manage this offloading process, balancing accuracy, latency, and energy consumption. This approach is crucial for enabling real-time AI applications on mobile and embedded devices, impacting fields like object detection, image classification, and AI security by improving performance and reducing power demands.