SHF: Small: A General Framework for Accelerating AI on Resource-Constrained Edge Devices

SHF：小型：在资源受限的边缘设备上加速 AI 的通用框架

• 批准号: 2211163
• 负责人: Yingying Chen
• 金额: $ 60万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211163&HistoricalAwards=false
• 关键词: SHF; Small; General; Framework; Accelerating

The upward trend of the pervasive usage of edge devices provides excellent opportunities for on-device intelligence in future mobile and IoT applications, including mobile augmented reality (AR)/Virtual reality (VR), smart manufacturing, mobile healthcare, and autonomous vehicles. While these edge devices have complete software/hardware stacks to execute machine-learning models, they usually have constrained computing resources. They cannot afford to execute the machine-learning models directly. To keep up with the fast-growing deployment of mobile and IoT applications, it is urgently needed to design new neural-network architectures for accelerating artificial intelligence (AI) on resource-constrained edge devices. This proposal aims to develop a novel framework that can efficiently design neural-network architectures suitable for execution on edge devices. The proposed framework develops network architectures that simultaneously balance memory cost, computing efficiency, and prediction accuracy, which can advance on-device AI applications with low-latency and high-efficiency requirements. The new deployment optimization methods can generally benefit neural-network implementation and deployment on heterogeneous commodity computing platforms without customized hardware. The project will lead to a solid foundation for a broad range of research topics related to computing architecture design and edge-computing systems. The research results can benefit interdisciplinary curriculums with new research topics and tasks for undergraduate/graduate and minority students.This project develops a holistic framework for designing efficient and effective neural-network architectures considering edge devices’ hardware constraints. It first develops an automated hardware-aware neural-network architecture-design method to efficiently generate optimal neural-network architectures that can balance the trade-offs between the required accuracy and computational performance. A further investigation is conducted to develop novel neural-network optimization methods to reduce memory footprints and computational costs in a fine-grained way while satisfying the accuracy requirement. New pruning methods are designed to accurately track the importance of network parameters and effectively reduce pruning iterations and floating-point operations. Moreover, novel implementation mechanisms, such as weight-sharing-aware fine-tuning, dynamic partitioning, and on-demand loading schemes, are developed to minimize the loading time overhead and enable efficient deployment and evaluation of the designed architectures on edge devices. Commodity edge devices and FPGAs are employed to implement and evaluate the designed neural-network architecture.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.

边缘设备普遍使用的上升趋势为未来的移动和物联网应用程序提供了极好的机会，包括移动增强现实（AR）/虚拟现实（VR），智能制造，移动医疗保健和自动驾驶汽车。尽管这些边缘设备具有完整的软件/硬件堆栈以执行机器学习模型，但它们通常具有限制的计算资源。他们无力直接执行机器学习模型。为了跟上移动和物联网应用程序快速增长的部署，迫切需要设计新的神经网络架构来加速人工智能（AI）在资源受限的边缘设备上。该建议旨在开发一个可以有效设计适合在边缘设备执行的神经网络体系结构的新颖框架。所提出的框架开发网络架构仅平衡内存成本，计算效率和预测准确性，可以推进具有低延迟和高效率要求的设备AI应用程序。新的部署优化方法通常可以使无定制硬件的异质商品计算平台上的中性网络实现和部署受益。该项目将为与计算体系结构设计和边缘计算系统有关的广泛研究主题奠定坚实的基础。该研究结果可以通过新的研究主题和本科生和少数民族学生的新主题和任务受益。该项目为设计高效有效的神经网络架构建筑设计了整体框架，以考虑Edge设备硬件的硬件约束。它首先开发了一种自动化的硬件感知神经网络体系结构设计方法，以有效地生成最佳的神经网络结构体系结构，以平衡所需准确性和计算性能之间的权衡。进行了进一步的研究，以开发新颖的中性网络优化方法，以细分的方式减少记忆足迹和计算成本，同时满足准确性要求。新的修剪方法旨在准确跟踪网络参数的重要性，并有效地减少迭代和浮点操作。此外，开发了新颖的实施机制，例如体重分担的微调，动态分配和按需加载方案，以最大程度地减少负载时间开销，并启用边缘设备上设计的架构的有效部署和评估。商品边缘设备和FPGA被用来实施和评估设计的神经网络架构。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为是通过评估而被视为珍贵的支持。

项目成果

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

Person re-identification using wifi signals

利用wifi信号进行人员重新识别

• DOI: 10.1145/3495243.3558252
• 发表时间: 2022
• 期刊: 28th Annual International Conference on Mobile Computing and Networking
• 作者: Ren, Yili;Wang, Yichao;Tan, Sheng;Chen, Yingying;Yang, Jie
• 通讯作者: Yang, Jie