Collaborative Research: SHF: Medium: Hardware and Software Support for Memory-Centric Computing Systems

协作研究:SHF:中:以内存为中心的计算系统的硬件和软件支持

基本信息

  • 批准号:
    2312509
  • 负责人:
  • 金额:
    $ 33.3万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-12-15 至 2026-11-30
  • 项目状态:
    未结题

项目摘要

Modern data-center applications are becoming increasingly memory-intensive and severely constrained by the limitations of the traditional von Neumann architecture. The emerging hardware ecosystem of Compute Express Link (CXL) opens an unprecedented opportunity to enable a radically new in-memory computing paradigm that can effectively mitigate the von Neumann bottleneck for future memory-centric applications. However, to fully unlock the potential of this emerging technology, several fundamental research challenges must be adequately addressed. This research project takes a holistic and cohesive approach to develop solutions that tackle the challenges head-on, paving the way for the future of in-memory computing infrastructure and fundamentally impacting memory-centric applications. Furthermore, the academic activities in this project extend their impact by providing training opportunities to students, enriching curriculum and classroom teaching, and contributing to educational and outreach initiatives. This project spearheads the fundamental research aimed at overcoming three pivotal challenges that hinder the realization of in-memory computing: fragmented memory resources, insufficient architectural support for memory sharing, and inefficient separation between hardware and software models. Leveraging the emerging CXL technology, this project adopts a systematic design methodology to address these intricate issues. It involves comprehensive efforts spanning multiple layers within the system stack, featuring the development of advanced hardware functionalities, the optimization of memory resource utilization in the system, and the integration of hardware support into general programming platforms to expedite applications. The research undertaken in this project lays the groundwork for fundamental studies that cater to the pressing demands of data-intensive applications in the realm of future memory-centric computing.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
现代数据中心应用程序的内存占用越来越多,并且受到传统冯·诺依曼架构的限制。Compute Express Link(CXL)的新兴硬件生态系统为实现全新的内存计算范式提供了前所未有的机会,该范式可以有效缓解未来以内存为中心的应用程序的冯诺依曼瓶颈。然而,为了充分释放这一新兴技术的潜力,必须充分解决几个基础研究挑战。该研究项目采用整体和有凝聚力的方法来开发解决方案,以正面应对挑战,为内存计算基础设施的未来铺平道路,并从根本上影响以内存为中心的应用程序。此外,该项目中的学术活动通过为学生提供培训机会、丰富课程和课堂教学以及促进教育和外联举措来扩大其影响。 该项目率先开展了基础研究,旨在克服阻碍内存计算实现的三个关键挑战:碎片化的内存资源,对内存共享的架构支持不足,以及硬件和软件模型之间的低效分离。利用新兴的CXL技术,该项目采用系统的设计方法来解决这些复杂的问题。它涉及系统堆栈内多个层次的综合努力,包括开发高级硬件功能,优化系统中的内存资源利用,以及将硬件支持集成到通用编程平台中以加快应用程序。该项目的研究为满足未来以内存为中心的计算领域中数据密集型应用的迫切需求奠定了基础。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估而被认为值得支持。

项目成果

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Feng Chen其他文献

In situ self-transformation synthesis of g-C3N4-modified CdS heterostructure with enhanced photocatalytic activity
原位自转化合成具有增强光催化活性的g-C3N4修饰的CdS异质结构
  • DOI:
    10.1016/j.apsusc.2015.06.074
  • 发表时间:
    2015-12
  • 期刊:
  • 影响因子:
    6.7
  • 作者:
    Huogen Yu;Fengyun Chen;Feng Chen;Xuefei Wang
  • 通讯作者:
    Xuefei Wang
Determination of iodine in seawater: methods and applications
海水中碘的测定:方法和应用
  • DOI:
    10.1016/b978-0-12-374135-6.00001-7
  • 发表时间:
    2009
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Huabin Li;Xiangrong Xu;Feng Chen
  • 通讯作者:
    Feng Chen
A preliminary investigation of metal element profiles in the serum of patients with bloodstream infections using inductively-coupled plasma mass spectrometry (ICP-MS)
使用电感耦合等离子体质谱 (ICP-MS) 对血流感染患者血清中金属元素谱进行初步研究
  • DOI:
    10.1016/j.cca.2018.07.013
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    5
  • 作者:
    Suying Zhao;Shuyuan Cao;Lan Luo;Zhan Zhang;Gehui Yuan;Yanan Zhang;Yanting Yang;Weihui Guo;Li Wang;Feng Chen;Qian Wu;Lei Li
  • 通讯作者:
    Lei Li
Development and Validation of a Novel Predictive Model for the Early Differentiation of Cardiac and Non-Cardiac Syncope
心源性晕厥和非心源性晕厥早期区分的新型预测模型的开发和验证
  • DOI:
    10.2147/ijgm.s454521
  • 发表时间:
    2024
  • 期刊:
  • 影响因子:
    2.3
  • 作者:
    Sijin Wu;Zhongli Chen;Yuan Gao;S. Shu;Feng Chen;Ying Wu;Yan Dai;Shu Zhang;Keping Chen
  • 通讯作者:
    Keping Chen
Training of Multi-class Linear Classifier with BFGS Method
用BFGS方法训练多类线性分类器

Feng Chen的其他文献

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{{ truncateString('Feng Chen', 18)}}的其他基金

ATD: Sparse and Localized Graph Convolutional Networks for Anomaly Detection and Active Learning
ATD:用于异常检测和主动学习的稀疏和局部图卷积网络
  • 批准号:
    2220574
  • 财政年份:
    2023
  • 资助金额:
    $ 33.3万
  • 项目类别:
    Standard Grant
FAI: A novel paradigm for fairness-aware deep learning models on data streams
FAI:数据流上具有公平意识的深度学习模型的新颖范式
  • 批准号:
    2147375
  • 财政年份:
    2022
  • 资助金额:
    $ 33.3万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Medium: A New Direction of Research and Development to Fulfill the Promise of Computational Storage
合作研究:SHF:Medium:实现计算存储承诺的研发新方向
  • 批准号:
    2210755
  • 财政年份:
    2022
  • 资助金额:
    $ 33.3万
  • 项目类别:
    Continuing Grant
III: Medium: Collaborative Research: MUDL: Multidimensional Uncertainty-Aware Deep Learning Framework
III:媒介:协作研究:MUDL:多维不确定性感知深度学习框架
  • 批准号:
    2107449
  • 财政年份:
    2021
  • 资助金额:
    $ 33.3万
  • 项目类别:
    Continuing Grant
III: Small: Collaborative Research: A novel paradigm for detecting complex anomalous patterns in multi-modal, heterogeneous, and high-dimensional multi-source data sets
III:小型:协作研究:一种检测多模态、异构和高维多源数据集中复杂异常模式的新范式
  • 批准号:
    1954409
  • 财政年份:
    2019
  • 资助金额:
    $ 33.3万
  • 项目类别:
    Standard Grant
CAREER: SPARK: A Theoretical Framework for Discovering Complex Patterns in Big Attributed Networks
职业:SPARK:发现大属性网络中复杂模式的理论框架
  • 批准号:
    1954376
  • 财政年份:
    2019
  • 资助金额:
    $ 33.3万
  • 项目类别:
    Continuing Grant
SHF: Small: Redesigning the System Architecture for Ultra-High Density Data Storage
SHF:小型:重新设计超高密度数据存储的系统架构
  • 批准号:
    1910958
  • 财政年份:
    2019
  • 资助金额:
    $ 33.3万
  • 项目类别:
    Standard Grant
CAREER: SPARK: A Theoretical Framework for Discovering Complex Patterns in Big Attributed Networks
职业:SPARK:发现大属性网络中复杂模式的理论框架
  • 批准号:
    1750911
  • 财政年份:
    2018
  • 资助金额:
    $ 33.3万
  • 项目类别:
    Continuing Grant
III: Small: Collaborative Research: A novel paradigm for detecting complex anomalous patterns in multi-modal, heterogeneous, and high-dimensional multi-source data sets
III:小型:协作研究:一种检测多模态、异构和高维多源数据集中复杂异常模式的新范式
  • 批准号:
    1815696
  • 财政年份:
    2018
  • 资助金额:
    $ 33.3万
  • 项目类别:
    Standard Grant
XPS: FULL: Collaborative Research: Maximizing the Performance Potential and Reliability of Flash-based Solid State Devices for Future Storage Systems
XPS:完整:协作研究:最大限度地提高未来存储系统基于闪存的固态设备的性能潜力和可靠性
  • 批准号:
    1629291
  • 财政年份:
    2016
  • 资助金额:
    $ 33.3万
  • 项目类别:
    Standard Grant

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合作研究:SHF:媒介:可微分硬件合成
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