CSR: Small: Quality Programmable Processing Platforms for Approximate Computing

由于技术扩展和计算需求的预计增长带来的效益递减而产生的差距导致了对计算效率的新来源的需求。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。在数据中心和云计算中，计算需求是由组织、分析、解释和搜索爆炸式增长的数字数据的需求驱动的。在移动的和深度嵌入式设备中，创建和使用更丰富的媒体以及与用户和环境进行更自然、更智能的交互的需求推动了大部分计算需求。这些应用程序在很大程度上不是计算精确的数值最终结果;对它们来说，“正确性”被定义为产生足够好或足够高质量的结果。这将如何帮助设计更高效的计算平台？这些新出现的工作负载以及许多其他工作负载，对于以近似或不精确的方式执行的底层计算表现出高度的内在弹性。本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利用固有的应用程序弹性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，本研究探索高质量的可编程处理器-用于近似计算的可编程平台，该平台为软件提供在自然的硬件/软件接口（即，指令集高质量可编程处理器的底层硬件能够理解和保证并行级质量规范，同时利用它们提供的灵活性来获得性能或能源改进。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器系统和片上互连网络。将开发技术，以优化程序的质量可编程平台上执行，通过识别弹性计算，并调整他们可以近似的程度，同时保持可接受的应用程序级输出质量。该项目将利用普渡大学的外展计划，包括夏季本科生研究奖学金（SURF），NCN（计算纳米技术网络）以及工程项目中的妇女和少数民族，让本科生和少数民族学生参与这项研究。

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.