SHF: Medium: Software Engineering for Hardware Errors

过去几十年来，硅技术支撑着计算机性能和功能的增长，现在正达到基本的物理极限。当这种情况发生时，计算机硬件变得越来越容易出错。传统的可靠性解决方案，以避免这种错误依赖于不加选择的冗余，这是太昂贵的新兴系统。一种有前途的方法是依靠软件，通过仅在需要时使用选择性冗余，以低得多的成本提供对硬件错误的可接受的弹性。实际采用软件驱动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，开发分析技术，可以识别软件区域，可能容易受到硬件错误，和低成本的缓解或硬化技术，可以使这些软件区域恢复到数据corruption.This项目是开发一个原则性和可扩展的方法，以弹性分析和硬化的软件是至关重要的。该项目基于两项观察。首先，弹性分析类似于软件测试问题，它试图找到软件错误。其次，弹性硬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，以改善弹性分析和各种计算机架构的加固。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，使弹性分析更快，更准确地用于不同的计算机架构;（3）开发正式的规范，优化策略和基于机器学习的方法，使用低成本的检查器来强化软件;以及（4）开发以增量和组合方式应用弹性解决方案的技术。目标是通过在现代软件开发工作流程中纳入弹性分析和硬化，实现低成本软件驱动的硬件可靠性方法的承诺。该项目为计算机体系结构，软件测试，程序分析和机器学习领域的学生提供了多学科培训的机会，以及通过增加对妇女和年龄不足者的招聘和保留工作来扩大对计算的参与，该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的智力价值和更广泛的评估支持影响审查标准。

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.