Edge Device

Edge devices are resource-constrained computing units performing computation closer to data sources, aiming to reduce latency, bandwidth usage, and privacy concerns associated with cloud computing. Current research focuses on optimizing deep learning models (e.g., CNNs, LLMs, GNNs) for edge deployment through techniques like model compression (quantization, pruning, knowledge distillation), efficient parallel processing (pipeline parallelism, tensor parallelism), and federated learning. This work is significant for enabling the deployment of sophisticated AI applications, such as autonomous driving and medical imaging analysis, on low-power devices, thereby expanding the accessibility and applicability of advanced technologies.