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

随着大规模边缘计算技术的成熟，人们越来越期待人工智能（AI）的边缘部署，并希望实现一个“普适边缘AI”生态系统，可以从各种边缘设备提供及时的AI服务，并进行边缘学习以实现可持续的服务定制。然而，鉴于边缘设备的巨大异构性，一系列挑战也随之出现：（1）如何有效地生成自适应AI模型以适应巨大的边缘系统异构性？(2)如何同时克服边缘学习协作中的AI模型异构性？(3)如何统一异构边缘计算系统，提供可扩展的边缘AI支持？该项目通过三个主要研究方向解决了这些基本挑战：Thrust 1彻底调查了不同深度神经网络（DNN）运算符结构的硬件资源消耗，并设计了一种新的神经架构搜索（NAS）方法，以解决架构级别的边缘异构性。它显著提高了边缘AI模型生成和自适应的有效性和效率。推力2通过揭示潜在的结构信息相关性问题，提高了联邦学习技术在异构AI模型中的能力。因此，它可以实现极端的异构边缘协作，并具有最佳的收敛和通信性能。Thrust 3通过硬件抽象化和软件虚拟化技术革新了全栈边缘AI系统支持，增强了系统的可扩展性和易开发性，实现了对边缘AI普适开发的支持。通过这些突破，该项目可以实现深度学习、计算系统、边缘协作技术以及相关边缘AI应用的联合创新。鉴于其灵活性和可行性的显著优势，我们将此框架命名为“液体智能”。随着该项目的成功，一个普遍而持续的边缘AI生态系统可能成为目标，推动下一波AI应用、边缘计算以及其他技术的发展。该教育计划通过整合嵌入式系统，移动的计算，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

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.