Collaborative Research: Software Canaries

随着晶体管变得越来越小，它们越来越容易受到其时序特性变化的影响。处理可能的变化的一种方法是在硬件设计期间假定最坏情况的特征。不幸的是，最坏情况下设计的功率和性能成本正变得令人望而却步。因此，处理器设计者寻求更积极的设计风格，允许比最坏情况下更好的操作。其中一种方法是使用“金丝雀电路”来指示处理器何时由于变化而处于不安全操作的边缘。这项研究调查的是“软件金丝雀”--运行处理器最敏感电路以确定积极的安全操作点的软件例程。软件金丝雀实现了硬件金丝雀电路的所有好处，而没有相应的硬件开销。更重要的是，它们可能会提供额外的好处，因为它们可以跟踪老化和温度变化-这可能是硬件金丝雀电路无法区分的。此外，与硬件金丝雀电路不同，它们允许在每个应用的基础上识别最激进的工作点。这项研究的重点是探索软件金丝雀的好处，开发用例，开发为不同微体系结构构建软件金丝雀的技术，以及开发支持基于软件金丝雀的执行所需的微体系结构和系统支持。该研究的更广泛影响在于通过选择积极的操作点使计算系统具有显著较低的能耗。其他具体影响包括开发可供其他研究小组进一步工作的开放源码工具，以及更多地讨论研究生和本科生课程中的差异和能源效率。

As transistors get smaller, they become increasingly susceptible to variations in their timing characteristics. One way to deal with possible variations is to assume worst-case characteristics during hardware design. Unfortunately, the power and performance costs of worst-case design are becoming prohibitive. Consequently, processor designers have sought more aggressive design styles that permit better-than-worst-case operation. One such approach uses "canary circuits" that indicate when a processor is on the verge of unsafe operation due to variations. This research investigates "software canaries" - software routines that exercise the most susceptible circuits of a processor to determine an aggressive safe operating point. Software canaries enable all the benefits of hardware canary circuits, without the corresponding hardware overhead. More importantly, they may provide additional benefits as they can track aging and temperature - variations that may be indistinguishable to hardware canary circuits. Furthermore, unlike hardware canary circuits, they allow identifying the most aggressive operating point on a per application basis. This research focuses on exploring the benefits of software canaries, developing use cases, developing techniques for building software canaries for different microarchitectures, and developing microarchitecture and system support needed to support software-canary-based execution.The broader impact of the research lies in enabling computing systems with significantly lower energy consumption by selecting aggressive operating points. Other specific impacts include development of open source tools that may be used for further work by other research groups, and a greater discussion of variations and energy efficiency in graduate and undergraduate coursework.