Edge Deployment

Edge deployment focuses on efficiently executing machine learning models, particularly deep learning models like transformers and graph neural networks, on resource-constrained devices near data sources to minimize latency and bandwidth usage. Current research emphasizes optimizing model architectures (e.g., binarization, quantization) and developing algorithms for efficient resource allocation, task offloading, and model protection against attacks. This field is crucial for advancing applications like autonomous driving, speech recognition, and personalized recommendations while addressing concerns about energy efficiency, privacy, and security in AI deployments.