CAREER: "Adapt, Learn, Collaborate" — Closing the Pervasive Edge AI Loop with Liquid Intelligence

George Mason University, Fairfax VA

Investigators

Abstract

With the maturity of massive edge computing technologies, people are increasingly looking forward to the edge deployment of artificial intelligence (AI) and hope to realize a “pervasive edge AI” ecosystem that could provide timely AI services from various edge devices and perform edge learning for sustainable service customization. However, given the vast heterogeneity in edge devices, a series of challenges also emerge: (1) How to efficiently generate adaptive AI models to accommodate vast edge system heterogeneity? (2) How to overcome the AI model heterogeneity across edge learning collaboration at the same time? (3) How to unify heterogeneous edge computing systems and provide scalable edge AI support? This project addresses these fundamental challenges with three major research thrusts: Thrust 1 thoroughly investigates the hardware resource consumption of different deep neural network (DNN) operator structures and designs a novel neural architecture search (NAS) method to address the edge heterogeneity down to the architecture level. It significantly improves the effectiveness and efficiency of edge AI model generation and adaptation. Thrust 2 advances the federated learning technique’s capability with heterogeneous AI models, by revealing the underlying structure-information correlation problem. And therefore, it enables extreme heterogeneous edge collaboration with optimal convergence and communication performance. Thrust 3 renovates the full-stack edge AI system support with hardware abstraction and software virtualization to enhance the system scalability and ease of development for pervasive edge AI development. With these thrusts completed, this project could achieve a joint innovation with deep learning, computing system, edge collaboration techniques, and related edge AI applications. Regarding its significant advantages of flexibility and feasibility, we name this framework as “Liquid Intelligence”. With the success of this project, a pervasive and continuous edge AI ecosystem could be targeted, boosting the next wave of AI applications, edge computing, as well as other technologies. It will also contribute to many societal challenges, such as smart cities, healthcare informatics, industrial infrastructures, etc. The education plan enhances existing curricula and pedagogy by integrating interdisciplinary modules on embedded systems, mobile computing, and machine learning with newly developed teaching practices. 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.