Collaborative Research: PPoSS: Planning: Hardware-accelerated Trustworthy Deep Neural Network

合作研究：PPoSS：规划：硬件加速的可信深度神经网络

• 批准号: 2028873
• 负责人: Jerry Cheng
• 金额: $ 6万
• 依托单位: New York Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2028873&HistoricalAwards=false
• 关键词: Collaborative; Research; PPoSS; Planning; Hardware

Deep-learning approaches have recently achieved much higher accuracy than traditional machine-learning approaches in various applications (e.g., computer vision, virtual/augmented reality, and natural language processing). Existing research has shown that large-scale data from various sources with high-resolution sensing or large-volume data-collection capabilities can significantly improve the performance of deep-learning approaches. However, state-of-the-art hardware and software cannot provide sufficient computing capabilities and resources to ensure accurate deep-learning performance in a timely manner when using extremely large-scale data. This project develops a scalable and robust heterogeneous system that includes a new low-cost, secure, deep-learning hardware-accelerator architecture and a suite of large-data-compatible deep-learning algorithms. It allows deep learning to fully benefit from extremely large-scale data and facilitates efficient, low-latency applications in connected vehicles, real-time mobile applications, and timely precision health. The new technologies resulting from this project can enable more research opportunities to design new hardware accelerators for deep learning and obtain further optimization in computational complexity and reduction in power consumption. Moreover, by integrating the research results with the undergraduate and graduate curricula and outreach activities, this project has great impacts on education and training of researchers and engineers for computer architecture, security, theory and algorithms, and systems.This project designs trustworthy hardware accelerators optimized for large-scale deep-learning computations and models the complicated structure of large-scale datasets. More specifically, this project develops a novel hardware accelerator for deep learning that can achieve low power consumption. In addition, this project designs innovative in-memory encryption schemes to secure the neural models in deep-learning accelerators. Furthermore, data-modeling and statistical-learning algorithms are developed in this project to further reduce the computing cost of deep learning when processing extremely large-scale datasets. Finally, this project builds and evaluates a prototype of the proposed heterogeneous deep-learning system in terms of efficiency, scalability, and security in multiple application domains including mobile applications, connected vehicles and precision health.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.

最近，深入学习方法的准确性比在各种应用程序中的传统机器学习方法（例如，计算机视觉，虚拟/增强现实和自然语言处理）高得多。现有的研究表明，来自具有高分辨率传感或大量数据收集功能的各种来源的大规模数据可以显着提高深度学习方法的性能。但是，最先进的硬件和软件无法提供足够的计算功能和资源，以确保使用极其大规模的数据时及时准确的深度学习绩效。该项目开发了一个可扩展且强大的异质系统，其中包括一种新的低成本，安全，学习的硬件加速器架构和一套与DATA兼容的大型深度学习算法。它允许深入学习能够从极大的大规模数据中充分受益，并促进互联车辆，实时移动应用程序和及时精确健康的高效，低延迟应用。该项目产生的新技术可以使更多的研究机会设计新的硬件加速器，以深入学习并获得进一步的计算复杂性和降低功耗的优化。此外，通过将研究结果与本科和研究生课程和外展活动相结合，该项目对计算机架构，安全，理论和算法以及系统的研究人员和工程师以及系统都有很大的影响。用于大规模的深度学习计算和建模大规模数据集的复杂结构。更具体地说，该项目开发了一个新颖的硬件加速器，用于深度学习，可以实现低功耗。此外，该项目设计了创新的内存加密方案，以确保深度学习加速器中的神经模型。此外，该项目中开发了数据建模和统计学习算法，以进一步降低处理极大的数据集时深度学习的计算成本。最后，该项目在效率，可伸缩性和安全性的多个应用程序域，包括移动应用程序，互联车辆和精密健康状况的方面建立并评估了提议的异质深度学习系统的原型值得通过基金会的智力优点和更广泛的影响审查标准来通过评估来支持。

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

WatchID: Wearable Device Authentication via Reprogrammable Vibration

WatchID：通过可重新编程的振动进行可穿戴设备身份验证

• 发表时间: 2021
• 期刊: International Conference on Mobile and Ubiquitous Systems Computing Networking and Services
• 作者: Cheng, J.Q.