Collaborative Research: III: Small: Efficient and Robust Multi-model Data Analytics for Edge Computing

协作研究：III：小型：边缘计算的高效、稳健的多模型数据分析

• 批准号: 2311596
• 负责人: Yingying Chen
• 金额: $ 24万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311596&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Small; Efficient

Many advanced edge-computing applications rely on large-scale data analysis for high-level decision-making. Edge computing makes computing faster and more efficient because it takes place near the physical location of either the user or the data rather than sending all the information to the cloud. For example, augmented reality/virtual reality (AR/VR) applications process data from high-definition sensors (such as cameras, motion sensors, and microphones) to enable accurate and robust human-computer interactions. Drones and electric vehicles perform tracking, adjustments and obstacle recognition, and avoidance via analyzing data at the level of the vehicle. However, the current ability to understand and manage various high-dimensional sensing data is obscured by significant knowledge and data gaps due to the heterogeneous edge device and environments, hindering the building of precise models for emerging edge computing applications using data analytics. One important trend in edge computing is utilizing artificial intelligence (AI) to extract complex knowledge from various sensor measurements for precise modeling. However, most edge devices have limited computing and memory resources, making it challenging to perform sophisticated data analytics using AI while satisfying the time requirements of most applications. Therefore, a heuristic data analytic framework is needed to enable efficient and robust edge event prediction using multi-model learning on resource-constrained edge devices. The goal of this project is developing transformative machine-learning and data analytics technologies for enabling AI-based applications on resource-constrained edge computing devices (such as IoT devices, AR/VR headsets, and drones). The outcome of this project will advance data analytics and machine-learning research by deriving and integrating various high-dimensional sensing data from diverse data sources and building robust predictive models for generic edge-computing applications. This project addresses two major problems: 1) the gap between the data complexity and limited computing resources on edge devices and 2) the gap between the robust performance requirement and the multi-dimensional data and complex data modeling from heterogeneous edge devices and environments. The project develops an efficient and robust edge-computing framework to provide correctness guarantees on heterogeneous edge-computing hardware across different environments. In particular, deep neural network acceleration techniques are designed to enable fine-grained data analytics on resource-constrained commercial-off-the-shelf edge devices. Novel multivariate data-analytic models are developed to characterize the unique features of the target event based on high-dimensional sensing data. Such models advance the usage of data science in generic edge sensing tasks that usually suffer from long training times, low prediction accuracy, and ineffective parameter selection. Additionally, the project addresses the challenges arising from the heterogeneity in devices and environments by developing environment-transferable features and models, which enable easy deployment of AI-enabled applications across devices and environments. The project seeks to integrate computer science research with graduate and undergraduate curricula and promote female engineering student involvement. The outcomes will be shared through conferences, journals, and website access.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.

许多先进的边缘计算应用程序依赖于大规模数据分析来进行高层决策。边缘计算使计算更快，更高效，因为它发生在用户或数据的物理位置附近，而不是将所有信息发送到云端。例如，增强现实/虚拟现实（AR/VR）应用程序处理来自高清传感器（如摄像头，运动传感器和麦克风）的数据，以实现准确和强大的人机交互。无人机和电动汽车通过分析车辆水平的数据来执行跟踪、调整和障碍物识别以及规避。然而，由于边缘设备和环境的异构性，当前理解和管理各种高维传感数据的能力被显著的知识和数据差距所掩盖，阻碍了使用数据分析为新兴边缘计算应用构建精确模型。边缘计算的一个重要趋势是利用人工智能（AI）从各种传感器测量中提取复杂的知识，以进行精确建模。然而，大多数边缘设备的计算和内存资源有限，这使得使用AI执行复杂的数据分析同时满足大多数应用程序的时间要求变得非常具有挑战性。因此，需要一种启发式数据分析框架，以便在资源受限的边缘设备上使用多模型学习来实现高效且鲁棒的边缘事件预测。该项目的目标是开发变革性的机器学习和数据分析技术，以便在资源受限的边缘计算设备（如物联网设备，AR/VR耳机和无人机）上实现基于AI的应用程序。该项目的成果将通过从不同的数据源中获取和整合各种高维传感数据，并为通用边缘计算应用构建强大的预测模型，来推进数据分析和机器学习研究。该项目解决了两个主要问题：1）数据复杂性与边缘设备上有限的计算资源之间的差距; 2）健壮的性能需求与来自异构边缘设备和环境的多维数据和复杂数据建模之间的差距。该项目开发了一个高效而强大的边缘计算框架，为不同环境中的异构边缘计算硬件提供正确性保证。特别是，深度神经网络加速技术旨在实现资源受限的商业现成边缘设备上的细粒度数据分析。新的多变量数据分析模型被开发来描述基于高维传感数据的目标事件的独特特征。这些模型推进了数据科学在一般边缘检测任务中的使用，这些任务通常受到训练时间长，预测精度低和参数选择无效的影响。此外，该项目还通过开发环境可转移的功能和模型来解决设备和环境异构性带来的挑战，这些功能和模型可以在设备和环境中轻松部署支持AI的应用程序。该项目力求将计算机科学研究与研究生和本科生课程结合起来，并促进工科女生的参与。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价值和更广泛的影响审查标准进行评估来支持。