CAREER: Efficient Mobile Edge Oriented Deep Learning Framework

职业：高效的面向移动边缘的深度学习框架

• 批准号: 2145389
• 负责人: Yan Wang
• 金额: $ 54.33万
• 依托单位: Temple University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-03-01 至 2027-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145389&HistoricalAwards=false
• 关键词: CAREER; Efficient; Mobile; Edge; Oriented

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.

近年来，移动边缘计算已广泛应用于许多应用领域。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边缘设备中的广泛部署。它还显着影响各种实时移动边缘应用程序的性能（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颖的面向移动边缘的深度学习框架，可以显着提高移动边缘设备上深度神经网络（DNN）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，可以在移动边缘设备上实现高效的训练和推理。它还研究移动边缘数据和硬件的独特特征，并通过计算重用以及动态分区和调度加速资源受限的移动边缘设备上的 DNN 执行。该项目将移动边缘传感和计算系统的先进研究结合起来。开发的框架为移动边缘设备的 DNN 和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边缘应用程序设计具有成本效益的计算机系统和架构的基础技术和分析。该项目成果显着提高了深度学习在与移动边缘计算相关的大量研究中的效率，包括物联网、移动和普适传感以及人机交互。该提案的结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/研究生和少数族裔学生提供新的研究主题和任务，从而使跨学科课程受益。该教育计划还包括与当地合作伙伴一起对从 K-5 到 K-12 的广泛学生计划进行外展工作。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。

项目成果

mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave

• DOI: 10.1109/icccn54977.2022.9868878
• 发表时间: 2022-07
• 期刊: 2022 International Conference on Computer Communications and Networks (ICCCN)
• 作者: Yucheng Xie;Ruizhe Jiang;Xiaonan Guo;Yan Wang;Jerry Q. Cheng;Yingying Chen

Defending against Thru-barrier Stealthy Voice Attacks via Cross-Domain Sensing on Phoneme Sounds

通过音素声音的跨域感知防御穿墙隐形语音攻击

• DOI: 10.1109/icdcs54860.2022.00071
• 发表时间: 2022
• 期刊: 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS
• 作者: Shi, Cong;Zhao, Tianming;Zhang, Wenjin;Mahdad, Ahmed Tanvir;Ye, Zhengkun;Wang, Yan;Saxena, Nitesh;Chen, Yingying
• 通讯作者: Chen, Yingying

Stealthy Backdoor Attack on RF Signal Classification

• DOI: 10.1109/icccn58024.2023.10230152
• 发表时间: 2023-07
• 期刊: 2023 32nd International Conference on Computer Communications and Networks (ICCCN)
• 作者: Tianming Zhao;Zijie Tang;Tian-Di Zhang;Huy Phan;Yan Wang;Cong Shi;Bo Yuan;Ying Chen

A Survey of Deep Learning on Mobile Devices: Applications, Optimizations, Challenges, and Research Opportunities

移动设备深度学习调查：应用、优化、挑战和研究机会

• DOI: 10.1109/jproc.2022.3153408
• 发表时间: 2022
• 期刊: Proceedings of the IEEE
• 影响因子: 20.6
• 作者: Zhao, Tianming;Xie, Yucheng;Wang, Yan;Cheng, Jerry;Guo, Xiaonan;Hu, Bin;Chen, Yingying
• 通讯作者: Chen, Yingying

Universal Targeted Adversarial Attacks Against mmWave-based Human Activity Recognition

• DOI: 10.1109/infocom53939.2023.10228887
• 发表时间: 2023-05
• 期刊: IEEE INFOCOM 2023 - IEEE Conference on Computer Communications
• 作者: Yucheng Xie;Ruizhe Jiang;Xiaonan Guo;Yan Wang;Jerry Q. Cheng;Yingying Chen