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: 2311598
• 负责人: Jerry Cheng
• 金额: $ 16万
• 依托单位: New York Institute of Technology
• 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=2311598&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 utilize data from high-definition sensors (e.g., 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 (e.g., IoT devices, AR/VR headsets, and drones). The outcome of this project will advance data analytics and machine learning research of 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 accessibility.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进行高级决策。边缘计算使计算更快，更有效，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。 例如，增强现实/虚拟现实（AR/VR）应用程序利用来自高清传感器（例如，摄像机，运动传感器和麦克风）的数据来启用准确且可靠的人类计算机相互作用。无人机和电动汽车通过分析车辆级别的数据进行跟踪，调整和障碍物识别和避免。但是，由于异质的边缘设备和环境，当前理解和管理各种高维度数据的能力被大量知识和数据差距所掩盖，从而阻碍了使用数据分析的精确模型构建用于新兴边缘计算应用程序的精确模型。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。但是，大多数边缘设备的计算和内存资源有限，因此在满足大多数应用程序的时间要求的同时，使用AI进行复杂的数据分析而具有挑战性。因此，需要一个启发式数据分析框架，以使用对资源受限的边缘设备上的多模型学习来实现高效且健壮的边缘事件预测。该项目的目的是开发可变革性的机器学习和数据分析技术，以启用基于AI的应用程序应用程序，以实现资源受限的边缘计算设备（例如，IoT设备，AR/VR耳机和无人机）。该项目的结果将推进数据分析和机器学习研究，以从各种数据源中得出和整合各种高维感应数据，并为通用边缘计算应用程序构建强大的预测模型。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以及2）可靠的性能要求与多维数据以及来自异质边缘设备和环境的复杂数据建模之间的差距。该项目开发了一个高效且强大的边缘计算框架，以在不同环境的异质边缘计算硬件上提供正确的保证。特别是，深度神经网络加速技术旨在启用有关资源约束的商业货架边缘设备的细粒度数据分析。开发了新型的多元数据分析模型，以根据高维感应数据来表征目标事件的独特特征。这样的模型可以推进数据科学在通用边缘传感任务中的使用，这些任务通常遭受较长的训练时间，低预测准确性和无效参数选择。此外，该项目通过开发可转移的功能和模型来解决设备和环境中异质性引起的挑战，这可以轻松在设备和环境之间轻松部署AI-a-Spapend应用程序。该项目旨在将计算机科学研究与研究生和本科课程相结合，并促进女性工程学生的参与。结果将通过会议，期刊和网站可访问性分享。该奖项反映了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评估标准通过评估来获得支持的。