Collaborative Research: Feedback-Driven Resiliency for Near-Threshold Systems

协作研究:反馈驱动的近阈值系统弹性

基本信息

  • 批准号:
    1255937
  • 负责人:
  • 金额:
    $ 9.6万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2013
  • 资助国家:
    美国
  • 起止时间:
    2013-04-01 至 2017-03-31
  • 项目状态:
    已结题

项目摘要

Near-threshold voltage (NTV) operation has emerged as a promising strategy to improve energy-efficiency by reducing the supply voltage while exploiting parallelism to compensate for frequency loss. NTV processors combined with "Turbo mode" can also offer peak performance, thereby making them suitable for a range of dynamic workload behaviors. Despite the promise, applications of these systems have remained largely elusive due to many issues. This research, involving VLSI circuits, computer architecture, and software systems, for the first time, addresses the reliability challenges of NTV/Turbo mode systems in the context of device aging and variability. Researchers will integrate distributed system monitors and controls to enable power-efficient fault resiliency for on-chip NTV/Turbo-mode cores. The work will lay the foundations for a feedback-directed optimization system that tunes the hardware's execution to meet application requirements by balancing performance, reliability and energy consumption.Our society's productivity and advancements are intimately tied to the predictable and sustainable operation of the world's computing infrastructure. Unfortunately, this trend is beginning to falter due to increasing computer energy consumption. Aggressive energy reductions are tightly coupled with destabilizing computer system performance and longevity. The research will uncover observations that are necessary to overcome the problem in near-threshold voltage systems that hold great promise for further advanced computing. Using an approach spanning both the hardware and software layers, researchers will investigate and develop novel techniques to overcome the energy problem. Such a holistic and joint effort across the layers is the key for ensuring our society's long-term success based on advanced computing.
近阈值电压(NTV)操作已经成为一种很有前途的策略,以提高能源效率,通过降低电源电压,同时利用并行补偿频率损失。NTV处理器结合“Turbo模式”还可以提供峰值性能,从而使其适用于一系列动态工作负载行为。尽管有希望,但由于许多问题,这些系统的应用在很大程度上仍然难以捉摸。这项研究,涉及超大规模集成电路,计算机体系结构和软件系统,第一次,解决了NTV/Turbo模式系统的可靠性挑战的背景下,设备老化和变化。研究人员将集成分布式系统监视器和控制,为片上NTV/Turbo模式内核提供高能效的故障恢复能力。这项工作将为反馈导向的优化系统奠定基础,该系统通过平衡性能、可靠性和能耗来调整硬件的执行,以满足应用需求。我们社会的生产力和进步与世界计算基础设施的可预测和可持续运行密切相关。不幸的是,由于计算机能耗的增加,这一趋势开始动摇。积极的能量减少与不稳定的计算机系统性能和寿命紧密相关。这项研究将揭示克服近阈值电压系统中的问题所必需的观测结果,这些系统为进一步的高级计算带来了巨大的希望。使用跨越硬件和软件层的方法,研究人员将研究和开发新的技术来克服能源问题。这种跨层次的整体和联合努力是确保我们社会基于先进计算的长期成功的关键。

项目成果

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Chris Kim其他文献

Optimizing Marker Design to Improve Precision of Optical Camera-Based Rigid-Body Motion Tracking and Correction in Medical Imaging
优化标记设计以提高医学成像中基于光学相机的刚体运动跟踪和校正的精度
Lexichrome: Text Construction and Lexical Discovery with Word-Color Associations Using Interactive Visualization
Lexichrome:使用交互式可视化的文本构建和词汇颜色关联的词汇发现
Peripheral Display in Virtual Reality Environments involves Higher Cognitive Demands Compared to Centered Display during Dual-Tasking
与双任务期间的中心显示相比,虚拟现实环境中的外围显示涉及更高的认知需求
Re-L Catan: Evaluation of Deep Reinforcement Learning for Resource Management Under Competitive and Uncertain Environments
Re-L Catan:竞争和不确定环境下资源管理的深度强化学习评估
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Chris Kim;Aaron Y. Li
  • 通讯作者:
    Aaron Y. Li
サブスレッショルド領域におけるラッチ回路の動作安定性モデル
锁存电路亚阈值区工作稳定性模型
  • DOI:
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Ketul B. Sutaria;Jyothi Bhaskarr Velamala;Chris Kim;Takashi Sato;and Yu Cao;室田一雄;鎌苅竜也,塩見準,石原亨,小野寺秀俊
  • 通讯作者:
    鎌苅竜也,塩見準,石原亨,小野寺秀俊

Chris Kim的其他文献

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{{ truncateString('Chris Kim', 18)}}的其他基金

ASCENT: TUNA: TUnable randomness for NAtural computing
ASCENT:TUNA:TU 无法实现自然计算的随机性
  • 批准号:
    2230963
  • 财政年份:
    2022
  • 资助金额:
    $ 9.6万
  • 项目类别:
    Standard Grant
Collaborative Research: Innovating Quantum-Inspired Learning for Undergraduates in Research and Engineering
协作研究:为研究和工程本科生创新量子启发学习
  • 批准号:
    2142248
  • 财政年份:
    2022
  • 资助金额:
    $ 9.6万
  • 项目类别:
    Standard Grant
SHF: Medium: Time Based Deep Neural Networks: An Integrated Hardware-Software Approach
SHF:媒介:基于时间的深度神经网络:一种集成的硬件软件方法
  • 批准号:
    1763761
  • 财政年份:
    2018
  • 资助金额:
    $ 9.6万
  • 项目类别:
    Continuing Grant
A Sub-2V Printed Flexible Organic RFID System Design for Long Range Communication
用于远距离通信的 Sub-2V 印刷柔性有机 RFID 系统设计
  • 批准号:
    0925312
  • 财政年份:
    2009
  • 资助金额:
    $ 9.6万
  • 项目类别:
    Standard Grant
CAREER: Design Framework for Organic Transistor Based Integrated Circuits
职业:基于有机晶体管的集成电路的设计框架
  • 批准号:
    0845605
  • 财政年份:
    2009
  • 资助金额:
    $ 9.6万
  • 项目类别:
    Continuing Grant

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Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
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  • 项目类别:
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