Collaborative Research: CNS Core: Small: Edge AI with Streaming Data: Algorithmic Foundations for Online Learning and Control

University Of Oregon Eugene, Eugene OR

Investigators

Abstract

Many emerging applications, such as smart healthcare, autonomous driving, and augmented reality, rely on applying real-time Artificial Intelligence (AI) to streaming data that are constantly generated online. Edge AI, which moves AI services to the network edge close to the end users and devices where data streams are generated, is crucial for reducing latency and communication bottlenecks and enabling fast and accurate inference decisions. However, edge AI for online streaming data poses significant challenges due to the unpredictable dynamics of the streaming data and the limited computation/communication capability at the network edge. This project addresses these challenges by developing both new theoretic models that integrate sophisticated learning methods with advanced edge-network control, and practical algorithms that significantly improve the accuracy and timeliness of edge AI services for streaming data. Specifically, the project will focus on three closely-related thrusts: (i) online learning policies for model selection will be developed to quickly identify which machine-learning models should be dynamically deployed at the edge servers for best inference accuracy, while accounting for the heterogeneous switching and feedback costs; (ii) distributed online transfer learning methods will be developed to quickly retrain new machine learning models at the edge upon new streaming data; and (iii) partial-index based edge-network control policies will be developed to optimize the timeliness of interactive edge-AI services under tight resource constraints. Both edge networks and AI are considered crucial elements of next-generation wireless networks. This project will directly benefit network operators and service providers that deploy and operate edge-AI systems. Specifically, the results will help them automate the complex decision-making process required for the end-to-end orchestration of such systems, and improve the accuracy and timeliness of the edge-AI services despite the constantly-changing environments. This project will also benefit the end users of emerging applications powered by edge AI, improving their user experience and well-being. More broadly, the theories and algorithms developed in this project for learning/control co-design will not only transform edge AI, but also benefit other disciplines with similar requirements for optimization under significant dynamism and uncertainty. Finally, this project will contribute teaching and training materials to multiple undergraduate and graduate courses, and will engage women and underrepresented minority students by reaching out to local schools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.