SHF: Medium: Software Engineering for Hardware Errors

在过去的几十年里，作为计算机性能和功能增长基础的硅技术现在已经达到了基本的物理极限。在这种情况下，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任意冗余，这对于新兴系统来说过于昂贵。一种很有前途的方法是，仅在需要的地方使用选择性冗余，依靠软件以低得多的成本为硬件错误提供可接受的弹性。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过软件堆栈，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原则性的、可伸缩的方法，用于软件的弹性分析和加固。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，它试图找到软件缺陷。第二，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各种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，对不同的计算机体系结构进行更快、更准确的弹性分析；(3)制定正式规范、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；(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中结合弹性分析和强化，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，并通过增加对女性和代表性不足的少数民族的招聘和保留努力，扩大了对计算机的参与。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.