SHF: Small: Collaborative Research: Software Hardware Architecture Co-design for Low-power Heterogeneous Edge Devices

SHF：小型：协作研究：低功耗异构边缘设备的软件硬件架构协同设计

• 批准号: 1909963
• 负责人: Yingying Chen
• 金额: $ 32万
• 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: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909963&HistoricalAwards=false
• 关键词: SHF; Small; Collaborative; Research; Software

The advancement of deep learning techniques, a sub-field of machine learning, is profoundly changing the field of mobile edge computing, thanks to recent research demonstrating that deep learning methods provide significant performance gains. However, the requirement of heavy computations and resources prevent deep learning methods from being widely deployed in mobile edge devices, such as smartphones and Internet of Things (IoT) devices. A significant advantage of enabling deep learning methods in mobile edge devices is that it can drastically reduce the response delay and energy consumption of mobile applications because the computations are executed locally. By removing the barrier that keeps deep learning techniques away from pervasive low-power mobile edge computing devices, this research enables high-accuracy, low-latency applications in future mobile edge computing. In particular, this research systematically investigates the fundamental and challenging issues targeting to significantly reduce the cost of deep learning inference process in mobile edge devices with guaranteed performance. The success of this project could significantly benefit the entire spectrum of deep learning across various research domains, including computer architecture, mobile sensing, cyber security, and human-computer interaction research areas. This project also aims to develop new curricula and encourage the participation of female engineering students. The primary goal of this research is to build a software accelerator that enables the broad deployment of heavy-cost deep learning models into resource-constrained, heterogeneous mobile edge devices (e.g., low-cost sensing platforms and IoT devices). The basic idea is to develop deep-learning resource management algorithms that can adjust structures of different deep learning models according to hardware constraints of heterogeneous edge devices. More specifically, this research analyzes distinct deep learning behaviors on mobile edge devices and designs different strategies to improve the efficiency of multiple deep-learning-based inference models. Furthermore, this research develops algorithms that can adjust the complexity of different deep learning models to reduce their energy and memory consumption on mobile edge devices. In addition, this project designs power-centric resource reallocation algorithms to verify and deploy the mobile-friendly deep learning models.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.

深度学习技术的进步是机器学习的一个子场，它正在深刻地改变移动边缘计算的领域，这要归功于最近的研究表明，深度学习方法可带来显着的性能提高。但是，重度计算和资源的要求阻止了深度学习方法被广泛部署在移动边缘设备中，例如智能手机和物联网（IoT）设备。在移动边缘设备中启用深度学习方法的一个重要优点是，它可以大大减少移动应用程序的响应延迟和能源消耗，因为计算是在本地执行的。通过删除使深度学习技术远离普遍的低功耗移动边缘计算设备的障碍，该研究可以在未来的移动边缘计算中实现高质量，低延迟应用。特别是，这项研究系统地研究了针对性的基本和具有挑战性的问题，以显着降低具有保证性能的移动边缘设备中深度学习推断过程的成本。该项目的成功可以显着使整个研究领域的深度学习范围都受益，包括计算机架构，移动传感，网络安全和人类计算机互动研究领域。该项目还旨在开发新的课程，并鼓励女性工程专业的学生参与。这项研究的主要目的是建立一个软件加速器，该软件加速器可以将重成本深度学习模型广泛部署到资源受限的，异质的移动边缘设备（例如，低成本传感平台和物联网设备）中。基本思想是开发深度学习资源管理算法，这些算法可以根据异质边缘设备的硬件约束来调整不同深度学习模型的结构。更具体地说，这项研究分析了移动边缘设备上的不同深度学习行为，并设计了不同的策略，以提高多个基于深度学习的推理模型的效率。此外，这项研究开发了算法，可以调整不同深度学习模型的复杂性，以减少其在移动边缘设备上的能量和记忆消耗。此外，该项目还设计了以功率为中心的资源重新分配算法来验证和部署移动友好的深度学习模型。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影响的评估来进行评估的支持标准。

项目成果

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

TrueHeart: Continuous Authentication on Wrist-worn Wearables Using PPG-based Biometrics

• DOI: 10.1109/infocom41043.2020.9155526
• 发表时间: 2020-01-01
• 期刊: IEEE INFOCOM 2020 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS
• 作者: Zhao, Tianming;Wang, Yan;Yu, Jiadi
• 通讯作者: Yu, Jiadi

MIXP: Efficient Deep Neural Networks Pruning for Further FLOPs Compression via Neuron Bond

• DOI: 10.1109/ijcnn52387.2021.9533522
• 发表时间: 2021-07
• 期刊: 2021 International Joint Conference on Neural Networks (IJCNN)
• 作者: Bin Hu;Tianming Zhao;Yucheng Xie;Yan Wang;Xiaonan Guo;Jerry Q. Cheng;Yingying Chen

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

Environment-independent In-baggage Object Identification Using WiFi Signals

• DOI: 10.1109/mass52906.2021.00018
• 发表时间: 2021-10
• 期刊: 2021 IEEE 18th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
• 作者: Cong Shi;Tianming Zhao;Yucheng Xie;Tianfang Zhang;Yan Wang;Xiaonan Guo;Yingying Chen