课题基金 / 基金详情

CAREER: New Foundations for Next-Generation Reliable Throughput Architecture Design

CAREER: New Foundations for Next-Generation Reliable Throughput Architecture Design
职业生涯:下一代可靠吞吐量架构设计的新基础
批准号:
1351054
负责人:
Xin Fu
金额:
$43.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-02-01 至 2015-06-30

项目摘要

项目成果

Xin Fu的其他基金

相似基金

相关文献

中文摘要
翻译
随着对提高性能和能效的需求,包括非易失性存储器(例如,自旋转移扭矩RAM(STT-RAM))、3D集成技术(3D)和近阈值电压计算(NTC)在内的新技术被越来越多地部署在最先进的吞吐量处理器中。由于新技术不是为可靠计算而设计的,可靠性挑战成为将其集成到下一代吞吐量处理器中的主要障碍,而可靠性挑战一直是传统吞吐量体系结构设计中的关键问题。迫切需要研究能够利用吞吐量处理器的独特特性来表征和提高基于下一代新技术的吞吐量体系结构设计的可靠性的创新技术。吞吐量处理器中最大的可靠性挑战包括粒子撞击导致的软错误、老化效应导致的硬错误以及制造工艺变化。这位首席研究员正在为漏洞表征和预测、错误检测和容错建立新的基础,以应对与新技术集成的吞吐量处理器中的那些主要可靠性挑战。项目目标包括:(1)对新技术(如STT-RAM、NTC和3D)启用的吞吐量处理器在存在软错误、老化效应和工艺变化的情况下的脆弱性进行建模和分析;(2)快速准确地预测新技术下吞吐量处理器的脆弱性阶段行为;(3)开发轻量级错误检测机制;以及(4)探索新技术带来的机遇和挑战,以经济高效地容忍下一代吞吐量体系结构设计中的各种错误。这项研究将极大地促进在未来技术中设计可靠的吞吐量处理器的能力,使其能够在不遭受各种故障机制造成的负面影响的情况下满足摩尔定律。此外,该项目还将实现将吞吐量处理器应用于从移动计算到云计算的广泛计算规模的愿望,并增加吞吐量处理器的部署,以支持科学和工程(如金融、医疗、生物、石油、航空航天、地质等)的超级计算。该项目还将通过吸引少数族裔服务机构的高中生和本科生参与研究,利用吞吐量处理器的可靠性建模和优化技术来扩展计算机工程课程,吸引女性和代表性不足的群体进入研究生教育,并传播用于教育和培训美国IT员工的研究基础设施,从而为社会做出贡献。
英文摘要
With the demand on improving performance and energy-efficiency, novel technologies including non-volatile memory (e.g., spin-transfer torque RAM (STT-RAM)), 3D integration technology (3D), and near-threshold voltage computing (NTC) have been increasingly deployed in the state-of-the-art throughput processors. Since the novel technologies are not designed for dependable computing, the reliability challenges, which have been a crucial issue in conventional throughput architecture design, become the major obstacle for integrating them into next-generation throughput processors. There is a pressing need for the investigation of innovative techniques that are able to take advantage of throughput processors' unique features for characterizing and improving the reliability of the next-generation new-technology based throughput architecture design. The paramount reliability challenges in throughput processors include particle strikes induced soft errors, hard errors driven by aging effects, and manufacturing process variations. The principle investigator is building new foundations for vulnerability characterization and prediction, error detection, and fault tolerance against those dominant reliability challenges in throughput processors integrated with novel technologies. The project objectives include: (1) modeling and analyzing the vulnerability of novel-technology (e.g., STT-RAM, NTC, and 3D) enabled throughput processors in the presence of soft error, aging effects, and process variations; (2) fast and accurate predictive model to forecast the vulnerability phase behavior of throughput processors under new technologies; (3) developing the light-weight error detection mechanisms; and (4) exploring the opportunities and challenges introduced by the novel technologies to cost-effectively tolerate various types of errors in next-generation throughput architecture design. The proposed research will significantly promote the capability of architecting reliable throughput processors in future technologies beyond CMOS, making it possible to fulfill the Moore's Law without suffering the negative effects caused by various fault mechanisms. Moreover, this project will realize the desire of applying throughput processors into a wide range of computing scale from mobile computing to cloud computing, and increasing the deployment of throughput processors in support of supercomputing in science and engineering (e.g., finance, medical, biology, petroleum, aerospace, and geology). This project will also contribute to society through engaging high-school and undergraduate students from minority-serving institutions into research, expanding the computer engineering curriculum with reliability modeling and optimization techniques on throughput processors, attracting women and under-represented groups into graduate education, and disseminating research infrastructure for education and training of US IT workforce.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
SHF: Small: Enabling On-Device Bayesian Neural Network Training via An Integrated Architecture-System Approach
  • 批准号:
    2130688
  • 项目类别:
    Standard Grant
  • 资助金额:
    $50.0万
  • 财政年份:
    2021
  • 负责人:
    Xin Fu
  • 依托单位:
SHF: Medium: Collaborative Research: Enhancing Mobile VR/AR User Experience: An Integrated Architecture-System Approach
  • 批准号:
    1900904
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $45.0万
  • 财政年份:
    2019
  • 负责人:
    Xin Fu
  • 依托单位:
SHF: Small: Leveraging User Preferences for Mobile User Experience Improvement
  • 批准号:
    1619243
  • 项目类别:
    Standard Grant
  • 资助金额:
    $41.0万
  • 财政年份:
    2016
  • 负责人:
    Xin Fu
  • 依托单位:
SHF:Small:Collaborative Research:Exploring Energy-Efficient GPGPUs Through Emerging Technology Integration
  • 批准号:
    1537062
  • 项目类别:
    Standard Grant
  • 资助金额:
    $22.69万
  • 财政年份:
    2014
  • 负责人:
    Xin Fu
  • 依托单位:
海外基金