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

合作研究：中枢神经系统核心：小型：具有流数据的边缘人工智能：在线学习和控制的算法基础

• 批准号: 2225950
• 负责人: Charlie Hu
• 金额: $ 30万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2225950&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

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.

许多新兴应用程序，例如智能医疗保健，自动驾驶和增强现实，都依靠将实时人工智能（AI）应用于不断在线生成的流媒体数据。 Edge AI将AI服务移至网络边缘，靠近生成数据流的最终用户和设备，对于减少延迟和通信瓶颈至关重要，并实现快速，准确的推理决策。但是，由于流媒体数据的不可预测动态以及网络边缘的有限的计算/通信能力，用于在线流数据的Edge AI构成了重大挑战。该项目通过开发将复杂的学习方法与先进的边缘网络控制和实用算法相结合的新理论模型来解决这些挑战，从而显着提高了Edge AI服务用于流数据的准确性和及时性。具体而言，该项目将集中在三个密切相关的推力上：（i）将制定用于模型选择的在线学习政策，以快速确定应在边缘服务器上动态部署哪些机器学习模型，以最佳推理准确性，同时考虑异类的切换和反馈成本； （ii）将开发分布式的在线转移学习方法，以快速在新的流数据上重新培训新的机器学习模型； （iii）将制定基于部分索引的边缘网络控制策略，以优化紧密的资源约束下交互式边缘服务的及时性。两者的边缘网络和AI都被认为是下一代无线网络的重要元素。该项目将直接使部署和操作边缘系统系统的网络运营商和服务提供商受益。具体而言，结果将帮助他们自动化此类系统端到端编排所需的复杂决策过程，并提高Edge-AI服务的准确性和及时性，尽管环境不断变化。该项目还将使Edge AI提供支持的新兴应用程序的最终用户受益，从而改善其用户体验和福祉。更广泛地说，在该项目中开发的用于学习/控制共同设计的理论和算法不仅会改变边缘AI，而且使其他学科受益于在重大动态和不确定性下具有相似优化要求的其他学科。最后，该项目将向多个本科和研究生课程贡献教学和培训材料，并通过与当地学校接触来吸引妇女和代表性不足的少数族裔学生。这项奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来支持的。