SHF: Large: General-Purpose Approximate Computing Across the System Stack

SHF：大型：跨系统堆栈的通用近似计算

• 批准号: 1518703
• 负责人: Luis Ceze
• 金额: $ 239.98万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1518703&HistoricalAwards=false
• 关键词: SHF; Large; General; Purpose; Approximate

Energy efficiency is a fundamental challenge facing the IT industry. Benefits go beyond reduced power demands in data centers and longer battery life in mobile devices. It is a fundamental enabler of future systems as we approach the limits of silicon device scaling. Therefore, providing a novel and holistic approach to energy efficiency in computer systems can have a transformative effect on IT and society. Many important applications---e.g., computer vision, novel user interfaces, signal processing, web search, augmented reality, and big-data analytics---can inherently tolerate some forms of inaccurate computation at various levels. With approximate computing, this fact can be exploited for fundamentally more efficient computing systems. This is a direct analog to Daniel Kahneman's model of how our brains work: they do cheap and quick reasoning (using System 1) in an approximate way, and when required, they do more expensive (and tiring) detailed thinking (using System 2). This research project will develop a analogous model for computer systems, from hardware to programming tools. Taking advantage of approximate computing requires significant innovation: programming models, tools for testing and debugging, and system support with quality guarantees. This project will develop a comprehensive solution across the system stack, from programming language to hardware. To demonstrate the potentials, prototypes of compelling applications amenable to approximate computing (e.g., computer vision) will be created. The project involves work on systems, programming languages, formal methods, and architecture, matching the inter-disciplinary expertise of the PI team. In addition to research papers, the project scope also includes releasing tools, benchmarks, and general infrastructure to the academic and industrial communities. The PIs have a history of inclusion of minorities and undergraduate students in their research efforts.

能源效率是IT行业面临的根本挑战。优势不仅仅是降低数据中心的电力需求和延长移动设备的电池续航时间。随着我们接近硅器件规模的极限，它是未来系统的根本使能器。因此，在计算机系统中提供一种新颖而全面的能源效率方法可以对IT和社会产生变革性的影响。许多重要的应用程序-例如，计算机视觉、新颖的用户界面、信号处理、网络搜索、增强现实和大数据分析-可以在不同的水平上容忍某些形式的不准确计算。有了近似计算，这一事实可以被用于从根本上更有效的计算系统。这是对丹尼尔·卡尼曼的大脑工作方式模型的直接类比：他们以一种近似的方式进行廉价而快速的推理(使用系统1)，当需要时，他们进行更昂贵(和疲惫)的详细思考(使用系统2)。这个研究项目将为计算机系统开发一个从硬件到编程工具的类似模型。利用近似计算需要重大创新：编程模型、测试和调试工具，以及有质量保证的系统支持。该项目将开发从编程语言到硬件的跨系统堆栈的全面解决方案。为了展示其潜力，将创建适合于近似计算(例如，计算机视觉)的引人注目的应用的原型。该项目涉及系统、编程语言、形式方法和体系结构方面的工作，与PI团队的跨学科专业知识相匹配。除了研究论文，项目范围还包括向学术界和工业界发布工具、基准和通用基础设施。私人投资机构有将少数族裔和本科生纳入其研究努力的历史。