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.

过去几十年来计算机性能和功能不断增长的硅技术现在已经达到了基本的物理极限。当这种情况发生时，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加区别的冗余，这对于新兴系统来说过于昂贵。一种有前途的方法是依靠软件仅在需要时使用选择性冗余，以低得多的成本提供可接受的硬件错误弹性。实际采用软件驱动解决方案的一个主要障碍是，某些硬件错误可能会逃脱软件堆栈，从而导致不可接受的数据损坏。因此，开发能够识别可能容易受到硬件错误影响的软件区域的分析技术，以及能够使此类软件区域能够抵御数据损坏的低成本缓解或强化技术至关重要。该项目旨在开发一种有原则的、可扩展的方法软件的弹性分析和强化。该项目基于两个观察结果。首先，弹性分析类似于软件测试问题，旨在发现软件错误。其次，弹性强化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不同计算机架构的弹性分析和强化。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，使针对不同计算机架构的弹性分析更快、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； (4) 开发以增量和组合方式应用弹性解决方案的技术。目标是通过将弹性分析和强化纳入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为现实。该项目为计算机架构、软件测试、程序分析和机器学习领域的学生提供多学科培训的机会，并通过增加女性和代表性不足的少数族裔的招募和保留工作来扩大对计算机的参与。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

ViX: Analysis-driven Compiler for Efficient Low-Precision Variational Inference

ViX：用于高效低精度变分推理的分析驱动编译器

• DOI: 10.23919/date56975.2023.10137324
• 发表时间: 2023-04
• 期刊: Automation & Test in Europe Conference & Exhibition (DATE
• 作者: Misra, Ashitabh;Laurel, Jacob;Misailovic, Sasa
• 通讯作者: Misailovic, Sasa

Optimizing Selective Protection for CNN Resilience

优化 CNN 弹性的选择性保护

• DOI: 10.1109/issre52982.2021.00025
• 发表时间: 2021-10-01
• 期刊: 2021 IEEE 32nd International Symposium on Software Reliability Engineering (ISSRE)
• 作者: Abdulrahman Mahmoud;S. Hari;Christopher W. Fletcher;S. Adve;Charbel Sakr;Naresh R Shanbhag;Pavlo Molchanov;Michael B. Sullivan;Timothy Tsai;S. Keckler
• 通讯作者: S. Keckler

Power, Performance, and Image Quality Tradeoffs in Foveated Rendering

焦点渲染中的功耗、性能和图像质量权衡

• DOI: 10.1109/vr55154.2023.00036
• 发表时间: 2023-03-01
• 期刊: 2023 IEEE Conference Virtual Reality and 3D User Interfaces (VR)
• 作者: Rahul Singh;Muhammad Huzaifa;Jeffrey Liu;Anjul Patney;Hashim Sharif;Yifan Zhao;S. Adve
• 通讯作者: S. Adve

A Theoretical Analysis of Random Regression Test Prioritization

随机回归测试优先级的理论分析

• DOI: 10.1007/978-3-030-99527-0_12
• 发表时间: 2022-04
• 期刊: 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
• 作者: Yi, P.;Wang, H;Xie, T.;Marinov, D.;Lam, W.
• 通讯作者: Lam, W.

A dual number abstraction for static analysis of Clarke Jacobians

克拉克雅克比行列式静态分析的双数抽象

• DOI: 10.1145/3498718
• 发表时间: 2022-01
• 期刊: Proceedings of the ACM on Programming Languages
• 作者: Laurel, Jacob;Yang, Rem;Singh, Gagandeep;Misailovic, Sasa
• 通讯作者: Misailovic, Sasa