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

能源效率是IT行业面临的一个根本性挑战。好处不仅仅是降低数据中心的电力需求和延长移动的设备的电池寿命。它是未来系统的基本推动力，因为我们接近硅器件扩展的极限。因此，为计算机系统的能源效率提供一种新颖而全面的方法可以对IT和社会产生变革性的影响。许多重要的应用-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--本质上可以容忍各种级别的某些形式的不准确计算。通过近似计算，可以利用这一事实来实现更高效的计算系统。这与丹尼尔·卡尼曼（Daniel Kahneman）的大脑工作模式有着直接的相似之处：大脑以一种近似的方式进行廉价而快速的推理（使用系统1），而在需要时，它们会进行更昂贵（也更累人）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类似的模型，从硬件到编程工具。利用近似计算需要重大创新：编程模型，测试和调试工具，以及具有质量保证的系统支持。该项目将开发一个全面的解决方案，跨越系统堆栈，从编程语言到硬件。为了展示潜力，服从近似计算的引人注目的应用程序的原型（例如，计算机视觉）将被创建。该项目涉及系统，编程语言，形式化方法和架构方面的工作，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、基准和一般基础设施。该PI有一个历史，包括少数民族和本科生在他们的研究工作。

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.