CAREER: Efficient Mobile Edge Oriented Deep Learning Framework

近年来，移动的边缘计算得到了广泛的应用。然而，流行的深度学习技术的极端计算成本与资源受限的移动的边缘计算系统之间存在差距。这样的差距阻碍了许多深度学习方法在移动的边缘设备中的广泛部署。它还显著地影响广泛的实时移动的边缘应用（例如，驾驶行为分析、对象识别和自然语言处理）。该项目旨在开发一种新型的面向移动的边缘的深度学习框架，可以显著提高深度神经网络（DNN）在移动的边缘设备上的性能和效率。为此，该项目系统地研究了DNN生命周期中具有挑战性的研究问题，包括架构设计，模型优化，计算减少和计算加速。它开发了高效的DNN模型，可以在移动的边缘设备上实现高效的训练和推理。它还研究了移动的边缘数据和硬件的独特特性，并通过计算重用和动态分区和调度来加速DNN在资源受限的移动的边缘设备上的执行。该项目连接了移动的边缘传感和计算系统的高级研究。开发的框架为DNN和移动的边缘设备的计算机架构设计和优化奠定了坚实的基础。它还加强了为实时移动的边缘应用设计经济高效的计算机系统和架构的基础技术和分析。该项目的成果显著提高了深度学习在与移动的边缘计算相关的大量研究中的效率，包括物联网、移动的和普适传感以及人机交互。该提案的成果可以提高实时数据分析中的硬件资源利用率，从而提高许多跨学科社区的科学产出效率。除了与社区分享成果外，该项目还可以为本科生/研究生和少数民族学生提供新的研究课题和任务，从而使跨学科的研究生院受益。该教育项目还包括与当地合作伙伴一起为从K-5到K-12的广泛学生项目开展的外展工作。该奖项反映了NSF的法定使命，并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，被认为值得支持。

Mobile edge computing has been widely used in many applications in these years. However, there is a gap between the extreme computational costs of prevalent deep learning techniques and resource-constrained mobile edge computing systems. Such a gap prevents many deep learning methods from being widely deployed in mobile edge devices. It also significantly impacts the performance of a broad range of real-time mobile edge applications (e.g., driving behavior analysis, object identification, and natural language processing). This project aims to develop a novel mobile edge-oriented deep learning framework that can significantly improve the performance and efficiency of deep neural networks (DNNs) on mobile edge devices. Toward this end, this project systematically investigates challenging research problems in the life cycle of DNNs, including architecture design, model optimization, computation reduction, and computing acceleration. It develops efficient DNN models that can achieve efficient training and inference on mobile edge devices. It also investigates unique characteristics of mobile edge data and hardware and accelerates DNN executions on resource-constrained mobile edge devices through computation reuse and dynamic partitioning and scheduling. This project connects advanced research in mobile edge sensing and computing systems. The developed framework leads to a solid foundation for DNN and computer architecture design and optimization for mobile edge devices. It also strengthens the foundational technology and analysis in designing cost-effective computer systems and architectures for real-time mobile edge applications. The project results significantly improve the efficiency of deep learning in a large spectrum of research related to mobile edge computing, including the Internet of Things, mobile and pervasive sensing, and human-computer interaction. The outcomes of the proposal can improve hardware resource utilization in real-time data analytics, leading to increased efficiency of scientific outputs in many interdisciplinary communities. In addition to sharing results with the community, this project can benefit interdisciplinary curriculums with new research topics and tasks for undergraduate/graduate and minority students. The education program also includes outreach efforts to a broad range of student programs from K-5 to K-12 with local partners.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.