SHF:Small:Disciplined Approximate Programming for Energy-Efficient Computing

SHF:Small：节能计算的规范近似编程

• 批准号: 1216611
• 负责人: Luis Ceze
• 金额: $ 30万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1216611&HistoricalAwards=false
• 关键词: SHF; Small; Disciplined; Approximate; Programming

This research aims to make computing systems more energy-efficient, leading to longer battery life for mobile devices and lower energy bills for end users and data centers. The key approach is to identify and exploit portions of computations where approximate results or occasional errors are allowable and use this flexibility to save energy. While sacrificing precision may initially seem unthinkable for computers, it is appropriate for the energy-intensive portions of many applications, such as programs related to multimedia, sensing, or random simulation. While allowing some approximation, one must still insist that only data and code specifically annotated as "approximate" by the programmer is allowed to be imprecise. Moreover, this research will develop techniques for monitoring and enforcing "quality of service", which in this context means the impact that approximation has on the observable output.The research necessarily crosses several layers of the conventional computing execution stack. The best way to save energy is via novel hardware that, thanks to approximation, can save energy via various means such as voltage scaling or lower cache refresh rates. But only higher levels of abstraction know where approximation is appropriate, so we need language design and language implementation to communicate this information to the hardware. In addition, programmers need software-development tools for debugging and profiling applications that leverage approximation. The results of this research will include advances in computer architecture, programming language design and implementation, and software tools.

这项研究旨在使计算系统更加节能，从而延长移动的设备的电池寿命，并降低最终用户和数据中心的能源费用。 关键的方法是识别和利用计算的近似结果或偶尔的错误是允许的部分，并使用这种灵活性来节省能源。 虽然牺牲精度对于计算机来说最初似乎是不可想象的，但它适用于许多应用程序的能源密集型部分，例如与多媒体，传感或随机模拟相关的程序。 虽然允许一些近似，但仍然必须坚持只有程序员特别标注为“近似”的数据和代码才允许是不精确的。 此外，这项研究将开发技术，用于监测和实施“服务质量”，这在这种情况下意味着近似的可观察的output.The研究必然跨越几个层的传统计算执行堆栈的影响。 节省能源的最佳方法是通过新型硬件，由于近似，可以通过各种方式节省能源，如电压缩放或降低缓存刷新率。 但是只有更高层次的抽象才知道近似在哪里是合适的，所以我们需要语言设计和语言实现来将这些信息传达给硬件。 此外，程序员需要软件开发工具来调试和分析利用近似的应用程序。 这项研究的结果将包括计算机体系结构，编程语言的设计和实现，以及软件工具的进步。