SaTC: CORE: Small: Towards Securing the Hardware and Software for Approximate Computing Systems

SaTC：核心：小型：致力于保护近似计算系统的硬件和软件

• 批准号: 2022279
• 负责人: Dongpeng Xu
• 金额: $ 50万
• 依托单位: University of New Hampshire
• 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 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2022279&HistoricalAwards=false
• 关键词: SaTC; CORE; Small; Towards; Securing

Approximate Computing (AC) trades accuracy for performance and energy efficiency. AC is increasingly attractive in various computation-intensive applications such as imaging processing, audio recognition, information search, and artificial intelligence. However, recent work shows that the unique approximate behavior and computational uncertainty in AC systems attract new attack opportunities. Compromised AC modules will sabotage the integrity and security of the entire computing system because AC components closely interact with non-AC modules in the same system. This project will identify the risk of applying approximate mechanisms in computing systems and explore effective methods to protect AC systems.The objective of this project is to develop holistic, hardware-software integrated methods to secure AC systems. More specifically, the research tasks include: (1) performing a thorough study on the security vulnerabilities of the hardware and software for AC systems, (2) developing methods to locate the vulnerable AC modules, (3) exploring hardware and software obfuscation methods to secure AC systems, and (4) evaluating the attack resilience of a cross-layer proactive defense framework in practical applications.The success of this project will facilitate the security of large-scale energy-efficient computing systems used in military, government, transportation, and commercial applications. This project is expected to support female Ph.D. students and engage more women students via the Workshop for Women in Hardware and Systems Security (WISE). The project will also promote undergraduate research and train undergraduate students to compete in international security competitions.The output of the project (source code, documentation, experiment log, and scholarly publications) will be managed by the version control system Git. New developed curriculum materials will be managed by the course system Canvas at the University of New Hampshire (UNH). A local copy of all the data and documents will be stored in the backup servers at UNH. Data will be retained for at least three years beyond the award period. The scholarly publications, presentations, and open-source code will be available on the project homepage at https://mypages.unh.edu/qyu/secure-approximate-computing-systems.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.

近似计算(AC)以精度为代价换取性能和能效。AC在图像处理、音频识别、信息搜索和人工智能等计算密集型应用中越来越有吸引力。然而，最近的工作表明，交流系统独特的近似行为和计算不确定性吸引了新的攻击机会。受损的交流模块将破坏整个计算系统的完整性和安全性，因为交流组件与同一系统中的非交流模块密切交互。本项目将确定在计算系统中应用近似机制的风险，并探索保护AC系统的有效方法。本项目的目标是开发整体的、硬件-软件集成的方法来保护AC系统。具体研究内容包括：(1)深入研究AC系统软硬件的安全漏洞，(2)开发易受攻击的AC模块的定位方法，(3)探索AC系统安全的软硬件混淆方法，(4)评估跨层主动防御框架在实际应用中的攻击弹性，该项目的成功将促进用于军事、政府、交通和商业应用的大规模节能计算系统的安全。该项目预计将支持女博士生，并通过硬件和系统安全妇女讲习班(WISE)吸引更多的女学生。该项目还将促进本科生的研究，培养本科生参加国际安全竞争。项目的输出(源代码、文档、实验日志和学术出版物)将由版本控制系统Git管理。新开发的课程材料将由新汉普郡大学(UNH)的课程系统Canvas管理。所有数据和文件的本地副本将存储在UNH的备份服务器中。数据将在授权期后至少保留三年。学术出版物、演示文稿和开放源代码将在项目主页上提供，https://mypages.unh.edu/qyu/secure-approximate-computing-systems.This奖反映了国家科学基金会的法定使命，并通过使用基金会的智力优势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

Securing Approximate Computing Systems via Obfuscating Approximate-Precise Boundary

• DOI: 10.1109/tcad.2022.3168261
• 发表时间: 2023-01
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Pruthvy Yellu;Qiaoyan Yu

No Free Lunch: On the Increased Code Reuse Attack Surface of Obfuscated Programs

• DOI: 10.1109/dsn58367.2023.00039
• 发表时间: 2023-06
• 期刊: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
• 作者: Naiqian Zhang;Daroc Alden;Dongpeng Xu;Shuai Wang;T. Jaeger;Wheeler Ruml

Can We Securely Use Approximate Computing?

我们可以安全地使用近似计算吗？

• DOI: 10.1109/iscas45731.2020.9180840
• 发表时间: 2020
• 期刊: 2020 IEEE International Symposium on Circuits and Systems (ISCAS
• 作者: Yellu, Pruthvy;Yu, Qiaoyan
• 通讯作者: Yu, Qiaoyan

Blurring Boundaries: A New Way to Secure Approximate Computing Systems

• DOI: 10.1145/3386263.3407593
• 发表时间: 2020-09
• 期刊: Proceedings of the 2020 on Great Lakes Symposium on VLSI
• 作者: Pruthvy Yellu;Landon Buell;Dongpeng Xu;Qiaoyan Yu

GraphMR: Graph Neural Network for Mathematical Reasoning

• DOI: 10.18653/v1/2021.emnlp-main.273
• 发表时间: 2021
• 作者: Weijie Feng;Binbin Liu;Dongpeng Xu;Qilong Zheng;Yun Xu