Collaborative Research: Approximate Computing on Real World Data Using Representation and Coding

协作研究：使用表示和编码对现实世界数据进行近似计算

• 批准号: 1848810
• 负责人: Vahid Tarokh
• 金额: $ 14.82万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1848810&HistoricalAwards=false
• 关键词: Collaborative; Research; Approximate; Computing; Real

The diminishing benefits from traditional transistor scaling has coincided with an overwhelming increase in the rate of data generation. Expert analyses show that in 2011, the amount of generated data surpassed 1.8 zeta bytes and will increase by a factor of 50 until 2020. To overcome these challenges, both the semiconductor industry and the research community are exploring new avenues in computing. Two of the promising approaches are acceleration and approximation. Among accelerators, Graphic Processing Units provide significant compute capabilities. Graphic Processing Units, originally designed to accelerate graphics functions, now are processing large amounts of real-world data that are collected from sensors, radar, environment, financial markets, and medical devices. As Graphic Processing Units play a major role in accelerating many classes of applications, improving their performance and energy efficiency has become imperative. This project leverages the fact that many applications that benefit from Graphic Processing Units are amenable to imprecise computation. This characteristic provides an opportunity to devise approximation techniques that trade small losses in the output quality for significant gains in performance and energy efficiency. This project aims to exploit this opportunity and develop a comprehensive framework for approximation in Graphic Processing Units along with effective quality control mechanisms based on coding theory. Energy efficiency is arguably the biggest challenge of the computing industry. To maintain the nation's economic leadership in this industry, it is vital to develop solutions, such as this project, that address the fundamental challenges of energy-efficient computing. The computing industry has reached an era in which many of the innovative techniques, such as this work, crosses the boundary of multiple disciplines, including computer architecture, information theory, and signal processing. Thus, it is imperative to educate a workforce that not only deeply understands multiple disciples, but also can innovate across their boundaries. This project provides a foundation for such education and research. This project will produce benchmarks, tools and general infrastructure. These artifacts will be made publicly available and will be integrated in the Georgia Tech and Harvard curricula. To transfer these technologies, the principle investigators have established close contacts with several companies. Besides the customary routes academics use to disseminate results, the principle investigator will continue organizing workshops on approximate computing. The principle investigator is also coauthoring a book on approximate computing, which will include results from this project. The investigators are committed to diversity and inclusion of undergraduate, underrepresented, and high school students and are currently mentoring students from all groups that will continue throughout this project. This project will first develop an accelerated architecture for Graphic Processing Units, which leverage an approximate algorithmic transformation for faster and more energy efficient execution. The core idea is to use neural models to learn how a region of code behaves and replace the region with a hardware accelerator that is tightly integrated within the many cores of the Graphic Processing Units. Second, inspired by Shannon's work and the success of random codes in providing reliable communication over noisy channels, this work will devise quality control solutions that utilize coding techniques to reduce the imprecision. The code is implicit in a sense that whenever an approximate output must be improved, its correlation with available exact outputs is exploited for constructing and decoding the code. Third, the project will study mechanisms that leverage the inherent similarity and predictability in the real-world data to address the memory bottlenecks in Graphic Processing Units. The main idea is to predict the values of a data load operation when it misses in the local on-chip cache and continue the computation without waiting for the long-latency response from the off-chip memory. To perform effective prediction, this project will develop multi-regime adaptive nonlinear time-varying dynamical models for the input data using our new theories of model matching.

传统晶体管缩放的益处减少与数据生成率的压倒性增加相吻合。专家分析表明，在2011年，生成的数据量超过1.8 Zeta字节，并将增加50倍，直到2020年。为了克服这些挑战，半导体行业和研究界都在探索计算中的新途径。有两个有希望的方法是加速和近似。在加速器中，图形处理单元提供了重要的计算功能。图形处理单元最初旨在加速图形功能，现在正在处理大量实际数据，这些数据是从传感器，雷达，环境，金融市场和医疗设备中收集的。由于图形处理单元在加速许多类别的应用中起着重要作用，因此必须提高其性能和能源效率。该项目利用了一个事实，即从图形处理单元中受益的许多应用程序可以不精确地计算。这种特征为设计近似技术提供了一个机会，该技术在产出质量方面造成了微小的损失，以获得绩效和能源效率的显着提高。该项目旨在利用这一机会，并为图形处理单元中的近似以及基于编码理论的有效质量控制机制开发综合框架。能源效率可以说是计算行业的最大挑战。为了维持该国在这个行业的经济领导地位，开发解决方案（例如该项目）至关重要，以应对节能计算的基本挑战。计算行业已经达到了一个时代，其中许多创新技术（例如这项工作）跨越了多个学科的边界，包括计算机架构，信息理论和信号处理。因此，必须教育一个不仅了解多个门徒的劳动力，而且可以在他们的界限上进行创新。该项目为此类教育和研究奠定了基础。该项目将产生基准，工具和一般基础架构。这些文物将公开可用，并将集成到佐治亚理工学院和哈佛课程中。为了转移这些技术，主要调查人员与几家公司建立了密切联系。除了学者们用于传播结果的习惯路线外，研究人员还将继续组织有关近似计算的研讨会。主要研究者还在合着了一本关于近似计算的书，其中将包括该项目的结果。调查人员致力于多样性，并包括本科生，代表性不足和高中生，目前正在指导所有将在整个项目中持续的团体的学生。该项目将首先开发用于图形处理单元的加速体系结构，该架构利用近似算法转换来实现更快，更有效的执行。核心思想是使用神经模型来了解代码区域的行为以及用硬件加速器替换区域，该硬件加速器紧密整合在图形处理单元的许多核心中。其次，灵感来自香农的工作以及随机代码在提供可靠的噪声渠道方面的成功，这项工作将设计质量控制解决方案，这些解决方案利用编码技术来减少不精确。该代码是隐含的，从某种意义上说，每当必须改进近似输出时，就可以利用其与可用的确切输出相关联，用于构建和解码代码。第三，该项目将研究利用现实世界数据中固有的相似性和可预测性来解决图形处理单元中的内存瓶颈。主要思想是当数据加载操作在本地片上缓存中错过并继续计算而无需等待芯片外存储器的长期响应时，它可以预测数据负载操作的值。为了执行有效的预测，该项目将使用我们的新型模型匹配理论为输入数据开发多主体自适应非线性时间变化的动力学模型。