Edge Inference

Edge inference focuses on performing machine learning inference directly on resource-constrained devices at the network edge, aiming to reduce latency, bandwidth consumption, and privacy concerns associated with cloud-based processing. Current research emphasizes efficient model architectures (like Vision Transformers and MobileNets), optimization techniques (including quantization, pruning, and model merging), and intelligent task offloading strategies to balance accuracy and resource usage. This field is crucial for enabling real-time AI applications in diverse areas such as video analytics, natural language processing, and robotics, driving advancements in both hardware and software for efficient AI deployment.