Remote Inference

Remote inference focuses on performing machine learning inferences on data collected at a distance, aiming to improve efficiency, responsiveness, and privacy in edge computing. Current research explores diverse model architectures, including spiking neural networks and hierarchical inference systems that combine on-device and remote processing to optimize accuracy, latency, and energy consumption. This field is significant for its potential to enhance various applications, from optimizing power grid management and improving the efficiency of IoT devices to enabling remote healthcare diagnostics and monitoring. Key challenges include managing data freshness and developing efficient algorithms for feature selection and scheduling in resource-constrained environments.