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（机器学习的一个子领域）的进步正在深刻地改变移动边缘计算领域，这要归功于最近的研究表明深度学习方法可提供显着的性能提升。然而，大量计算和资源的需求阻碍了深度学习方法在移动边缘设备（例如智能手机和物联网（IoT）设备）中的广泛部署。在移动边缘设备中启用深度学习方法的一个显着优势是，它可以大大减少移动应用程序的响应延迟和能耗，因为计算是在本地执行的。通过消除深度学习技术远离普及的低功耗移动边缘计算设备的障碍，这项研究可以在未来的移动边缘计算中实现高精度、低延迟的应用。特别是，本研究系统地研究了基本和具有挑战性的问题，旨在显着降低移动边缘设备中深度学习推理过程的成本，同时保证性能。该项目的成功可以极大地惠及各个研究领域的整个深度学习领域，包括计算机体系结构、移动传感、网络安全和人机交互研究领域。该项目还旨在开发新课程并鼓励女工科学生的参与。这项研究的主要目标是构建一个软件加速器，使成本高昂的深度学习模型能够广泛部署到资源受限的异构移动边缘设备（例如低成本传感平台和物联网设备）中。其基本思想是开发深度学习资源管理算法，能够根据异构边缘设备的硬件限制调整不同深度学习模型的结构。更具体地说，本研究分析了移动边缘设备上不同的深度学习行为，并设计了不同的策略来提高多种基于深度学习的推理模型的效率。此外，这项研究开发的算法可以调整不同深度学习模型的复杂性，以减少移动边缘设备上的能源和内存消耗。此外，该项目设计了以电力为中心的资源重新分配算法，以验证和部署适合移动设备的深度学习模型。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标准进行评估，被认为值得支持。

项目成果

Stealthy Backdoor Attack on RF Signal Classification

针对射频信号分类的隐形后门攻击

• 发表时间: 2023-01
• 期刊: International Conference on Computing Communication and Networking Technologies
• 作者: Zhao, T;Tang, Z;Zhang, T;Phan, H;Wang, Y;Shi, C;Yuan, B;Chen, Y
• 通讯作者: Chen, Y

BioTag: Robust RFID-based Continuous User Verification Using Physiological Features from Respiration

BioTag：使用呼吸生理特征进行基于 RFID 的稳健连续用户验证

• DOI: 10.1145/3492866.3549718
• 发表时间: 2022-10
• 期刊: and Protocol Design for Mobile Networks and Mobile Computing
• 作者: Hu, B.;Zhao, T.;Wang, Y.;Cheng, J.;Howard, R.;Chen, Y.;Wan, H.
• 通讯作者: Wan, H.

mmFit: Low-Effort Personalized Fitness Monitoring Using Millimeter Wave

mmFit：使用毫米波轻松进行个性化健身监测

• DOI: 10.1109/icccn54977.2022.9868878
• 发表时间: 2022-07
• 期刊: International Conference on Computer Communications and Networks (ICCCN
• 作者: Xie, Y.;Jiang, R.;Guo, X.;Wang, Y.;Cheng, J.;Chen, Y.
• 通讯作者: Chen, Y.

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

MIXP：高效深度神经网络修剪，通过神经元键进一步压缩 FLOP

• 发表时间: 2021-07
• 期刊: Proceedings of International Joint Conference on Neural Networks
• 作者: Hu, B.;Zhao, T.;Xie, Y.;Wang, Y.;Guo, X.;Cheng, J.;Chen, Y.
• 通讯作者: Chen, Y.

Speech privacy attack via vibrations from room objects leveraging a phased-MIMO radar

利用相控 MIMO 雷达通过房间物体振动进行语音隐私攻击

• DOI: 10.1145/3498361.3538790
• 发表时间: 2022-06-27
• 期刊: Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services
• 作者: Cong Shi;Tianfang Zhang;Zhaoyi Xu;Shuping Li;Yichao Yuan;A. Petropulu;C. Wu;Yingying Chen
• 通讯作者: Yingying Chen