SHF: Medium: Software Engineering for Hardware Errors

SHF：中：针对硬件错误的软件工程

• 批准号: 1956374
• 负责人: Sarita Adve
• 金额: $ 120万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1956374&HistoricalAwards=false
• 关键词: SHF; Medium; Software; Engineering; Hardware

Silicon technology underlying the growth in computer performance and functionality over the last several decades is now reaching fundamental physical limits. As this happens, computer hardware is becoming increasingly susceptible to errors. Traditional reliability solutions to avoid such errors rely on indiscriminate redundancy, which is too expensive for emerging systems. A promising approach is to rely on software to provide acceptable resiliency to hardware errors at a much lower cost by using selective redundancy only where needed. A key obstacle to practical adoption of software-driven solutions is that some hardware errors may escape the software stack, leading to unacceptable data corruptions. It is therefore critical to develop analysis techniques that can identify software regions that are potentially vulnerable to hardware errors, and low-cost mitigation or hardening techniques that can make such software regions resilient to data corruption.This project is to develop a principled and scalable approach to resiliency analysis and hardening for software. The project is based on two observations. First, resiliency analysis is analogous to the problem of software testing, which seeks to find software bugs. Second, resiliency hardening is analogous to software debugging and repair. The work will leverage methods previously used for software testing and debugging to improve resiliency analysis and hardening for diverse computer architectures. It will (1) explore new testing-based techniques to improve the quality and diversity of test inputs used for resiliency analysis; (2) leverage program-analysis and machine-learning methods to make resiliency analysis faster and more accurate for diverse computer architectures; (3) develop formal specifications, optimization strategies, and machine-learning-based methods to harden software using low-cost checkers; and (4) develop techniques to apply resiliency solutions in an incremental and compositional way. The goal is to make the promise of low-cost software-driven approaches to hardware reliability practical by incorporating resiliency analysis and hardening within a modern software-development workflow. The project offers the opportunity for multidisciplinary training of students in the fields of computer architecture, software testing, program analysis, and machine learning, as well as broadening participation in computing through increased recruitment and retention efforts for women and under-represented minorities.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.

在过去几十年里，支撑计算机性能和功能增长的硅技术现在正达到基本的物理极限。随着这种情况的发生，计算机硬件变得越来越容易出错。避免此类错误的传统可靠性解决方案依赖于不分青红皂白的冗余，这对新兴系统来说过于昂贵。一种有前景的方法是依靠软件以低得多的成本提供对硬件错误的可接受恢复能力，只在需要的地方使用选择性冗余。实际采用软件驱动的解决方案的一个关键障碍是，一些硬件错误可能会逃脱软件堆栈，导致不可接受的数据损坏。因此，关键是开发能够识别潜在易受硬件错误影响的软件区域的分析技术，以及能够使这些软件区域对数据损坏具有弹性的低成本缓解或加固技术。该项目旨在开发一种原则性和可扩展性的方法来对软件进行弹性分析和加固。该项目基于两个观察结果。首先，弹性分析类似于软件测试的问题，软件测试试图找到软件错误。第二，弹性加固类似于软件调试和修复。这项工作将利用以前用于软件测试和调试的方法来改进对不同计算机体系结构的弹性分析和加固。它将(1)探索新的基于测试的技术，以提高用于弹性分析的测试输入的质量和多样性；(2)利用程序分析和机器学习方法，使不同计算机体系结构的弹性分析更快、更准确；(3)开发正式规范、优化策略和基于机器学习的方法，以使用低成本检查器强化软件；以及(4)开发技术，以增量和组合的方式应用弹性解决方案。其目标是通过将弹性分析和强化整合到现代软件开发工作流程中，使低成本软件驱动的硬件可靠性方法的承诺变得切实可行。该项目提供了在计算机体系结构、软件测试、程序分析和机器学习领域对学生进行多学科培训的机会，并通过增加对妇女和代表性不足的少数民族的招聘和保留努力，扩大对计算的参与。该奖项反映了NSF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

Proof transfer for fast certification of multiple approximate neural networks

• DOI: 10.1145/3527319
• 发表时间: 2022-04
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Shubham Ugare;Gagandeep Singh

Fault Localization for Declarative Models in Alloy

• DOI: 10.1109/issre5003.2020.00044
• 发表时间: 2018-07
• 期刊: 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)
• 作者: Kaiyuan Wang;Allison Sullivan;D. Marinov;S. Khurshid

Preempting Flaky Tests via Non-Idempotent-Outcome Tests

• DOI: 10.1145/3510003.3510170
• 发表时间: 2022-05
• 期刊: 2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)
• 作者: Anjiang Wei;Pu Yi;Zhengxi Li;Tao Xie;D. Marinov;Wing Lam

AQUA: Automated Quantized Inference for Probabilistic Programs

AQUA：概率程序的自动量化推理

• DOI: 10.1007/978-3-030-88885-5_16
• 发表时间: 2021
• 期刊: 2021 in Automated Technology for Verification and Analysis
• 作者: Huang, Zixin;Dutta, Saikat;Misailovic, Sasa
• 通讯作者: Misailovic, Sasa

SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning

SixthSense：通过程序表示学习调试概率程序中的收敛问题

• DOI: 10.1007/978-3-030-99429-7_7
• 发表时间: 2022
• 期刊: 25th International Conference on Fundamental Approaches to Software Engineering
• 作者: Dutta, Saikat;Huang, Zixin;Misailovic, Sasa
• 通讯作者: Misailovic, Sasa