CSR: Small: Quality Programmable Processing Platforms for Approximate Computing

CSR：小型：用于近似计算的高质量可编程处理平台

• 批准号: 1423290
• 负责人: Anand Raghunathan
• 金额: $ 48.06万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423290&HistoricalAwards=false
• 关键词: CSR; Small; Quality; Programmable; Processing

The gap created by diminishing benefits from technology scaling and projected growth in computing demand has led to a need for new sources of computing efficiency. Fortunately, the workloads that are driving demand across the computing spectrum also present new opportunities. In data centers and the cloud, computing demand is driven by the need to organize, analyze, interpret, and search through exploding amounts of digital data. In mobile and deeply embedded devices, the creation and consumption of richer media and the need to interact more naturally and intelligently with users and the environment drive much of the computing demand. These applications are largely not about calculating a precise numerical end result; for them, "correctness'' is defined as producing results that are good enough, or of sufficient quality. How does this help design more efficient computing platforms? These emerging workloads, and many others, demonstrate a high degree of intrinsic resilience to their underlying computations being executed in an approximate or inexact manner. This project will explore approximate computing, an emerging design approach that improves the efficiency of computing platforms by designing them to leverage intrinsic application resilience.To establish approximate computing in a broader context, this research explores quality programmable processors - programmable platforms for approximate computing that offer the ability for software to express application resilience at the natural HW/SW interface, i.e., the instruction set. The hardware underlying a quality programmable processor is equipped to understand and guarantee the instruction-level quality specifications, while exploiting the flexibility that they provide to obtain performance or energy improvements. This project will explore quality programmable designs of various programmable architectures, including general-purpose cores, vector processors, and GPGPUs. It will also extend the notion of quality programmability to the memory system and on-chip interconnect network. Techniques will be developed to optimize programs for execution on quality programmable platforms by identifying resilient computations, and tuning the degree to which they can be approximated while maintaining acceptable application-level output quality. The project will leverage outreach programs at Purdue, including Summer Undergraduate Research Fellowships (SURF), the NCN (Network for Computational Nanotechnology), and the Women and Minority in Engineering programs, to involve undergraduates and minority students in this research.

技术扩展带来的收益递减和计算需求的预计增长所造成的差距导致需要新的计算效率来源。幸运的是，推动整个计算领域需求的工作负载也带来了新的机遇。在数据中心和云中，计算需求是由组织、分析、解释和搜索爆炸量的数字数据的需求驱动的。在移动和深度嵌入式设备中，丰富媒体的创建和消费以及与用户和环境更自然、更智能地交互的需求推动了大部分计算需求。这些应用程序很大程度上不是计算精确的数字最终结果；而是计算结果。对于他们来说，“正确性”被定义为产生足够好或足够质量的结果。这如何帮助设计更高效的计算平台？这些新兴工作负载以及许多其他工作负载，展示了对其以近似或不精确方式执行的底层计算的高度内在弹性。该项目将探索近似计算，这是一种新兴的设计方法，通过设计计算平台来提高计算平台的效率 为了在更广泛的背景下建立近似计算，本研究探索了高质量的可编程处理器——用于近似计算的可编程平台，使软件能够在自然的硬件/软件接口（即指令集）上表达应用程序的弹性。质量可编程处理器底层的硬件能够理解和保证指令级质量规范，同时利用它们提供的灵活性来获得性能或能量 改进。该项目将探索各种可编程架构的高质量可编程设计，包括通用内核、矢量处理器和 GPGPU。它还将质量可编程性的概念扩展到存储系统和片上互连网络。将开发技术，通过识别弹性计算并调整它们可以近似的程度，同时保持可接受的程度，来优化在高质量可编程平台上执行的程序 应用程序级输出质量。该项目将利用普渡大学的推广项目，包括夏季本科生研究奖学金（SURF）、NCN（计算纳米技术网络）以及工程领域的女性和少数族裔项目，让本科生和少数族裔学生参与这项研究。