CSR: Medium: Optimal Control of Approximate Computing Systems

CSR：中：近似计算系统的最优控制

• 批准号: 1705092
• 负责人: Keshav Pingali
• 金额: $ 59.92万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2021-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1705092&HistoricalAwards=false
• 关键词: CSR; Medium; Optimal; Control; Approximate

Computing is increasingly constrained by energy both at the low end in portable devices like cell phones and at the high end in large-scale data centers. Therefore, reducing the energy expended in computation is one of the most important problems facing Computer Science today. By taking computational shortcuts such as not executing certain portions of the program or executing them with lower accuracy, it is possible in many applications, for instance video processing, to reduce energy requirements without substantially affecting the quality of the output. The goal of the Capri project is to build a system that can optimize the energy consumption of a program in these ways while guaranteeing that the output quality stays within some bound specified by the programmer.There are two major problems that must be solved: (i) building models to characterize the output quality and energy behavior of a program, and (ii) using these models to solve a constrained optimization problem to determine how approximation can be used. Capri will use state-of-the-art machine learning techniques to build program models for output quality and energy behavior, and will employ modern non-linear optimization algorithms to solve the constrained optimization problem. The project will produce scalable open-loop and closed-loop control systems for optimizing applications for energy efficiency. The project includes presenting tutorials on information-efficient computing at conferences like Principles and Practice of Parallel Programming (PPoPP) and High-Performance Computer Architecture (HPCA), and incorporate this material into classes and make it publicly available. All data and outputs produced by the Capri project will be made available at this website: http://iss.ices.utexas.edu/.

计算越来越受到能源的限制，无论是在低端的便携式设备（如手机）还是在高端的大型数据中心。因此，减少计算中消耗的能量是当今计算机科学面临的最重要的问题之一。通过采取计算捷径，例如不执行程序的某些部分或以较低的精度执行它们，在许多应用中，例如视频处理，可以减少能量需求，而不会实质上影响输出的质量。卡普里项目的目标是建立一个系统，可以优化程序的能源消耗，同时保证输出质量保持在程序员指定的范围内。有两个主要问题必须解决：（i）建立模型以表征程序的输出质量和能量行为，以及（ii）使用这些模型来求解约束优化问题以确定如何使用近似。卡普里将使用最先进的机器学习技术来构建输出质量和能源行为的程序模型，并将采用现代非线性优化算法来解决约束优化问题。该项目将生产可扩展的开环和闭环控制系统，以优化能源效率的应用。该项目包括在并行编程原理与实践（PPoPP）和高性能计算机体系结构（HPCA）等会议上提供有关信息高效计算的教程，并将这些材料纳入课堂并公开提供。卡普里项目产生的所有数据和产出将在以下网站上提供：http://iss.ices.utexas.edu/。