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

许多先进的边缘计算应用依赖于大规模数据分析来进行高层决策。边缘计算使计算速度更快、效率更高，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中。例如，增强现实/虚拟现实(AR/VR)应用程序利用来自高清晰度传感器(例如，摄像头、运动传感器和麦克风)的数据来实现准确和健壮的人机交互。无人机和电动汽车通过分析车辆层面的数据来执行跟踪、调整以及障碍物识别和避让。然而，由于边缘设备和环境的异质性，当前理解和管理各种高维传感数据的能力被大量的知识和数据缺口所掩盖，阻碍了使用数据分析为新兴的边缘计算应用建立精确的模型。边缘计算的一个重要趋势是利用人工智能(AI)从各种传感器测量数据中提取复杂知识，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，因此在满足大多数应用程序的时间要求的同时，使用人工智能执行复杂的数据分析具有挑战性。因此，需要一个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和稳健的边缘事件预测。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(例如物联网设备、AR/VR耳机和无人机)上实现基于AI的应用。该项目的成果将推动数据分析和机器学习研究，从不同的数据源获得和集成各种高维传感数据，并为通用边缘计算应用建立稳健的预测模型。该项目解决了两个主要问题：1)边缘设备上的数据复杂性与有限的计算资源之间的差距；2)健壮的性能需求与来自不同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和健壮的边缘计算框架，以提供跨不同环境的异质边缘计算硬件的正确性保证。特别是，深度神经网络加速技术旨在实现对资源受限的商业现成边缘设备的细粒度数据分析。发展了新的多变量数据分析模型，以基于高维传感数据来表征目标事件的独特特征。这种模型促进了数据科学在一般边缘检测任务中的使用，这些任务通常存在训练时间长、预测精度低和参数选择无效的问题。此外，该项目通过开发环境可迁移的功能和模型来应对设备和环境中的异构性带来的挑战，从而实现在设备和环境中轻松部署支持人工智能的应用程序。该项目旨在将计算机科学研究与研究生和本科生的课程相结合，并促进女性工程专业学生的参与。结果将通过会议、期刊和网站的可访问性进行分享。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.