课题基金 / 基金详情

II-New: RICARDO: Research Infrastructure for Circuit and Architecture Design with Emerging Technologies

II-New: RICARDO: Research Infrastructure for Circuit and Architecture Design with Emerging Technologies
II-新:RICARDO:利用新兴技术进行电路和架构设计的研究基础设施
批准号:
1730309
负责人:
Yuan Xie
金额:
$50.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2020-08-31

项目摘要

项目成果

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中文摘要
翻译
积极的技术扩展到深亚微米(DSM)领域,伴随着晶体管密度的急剧增加,晶体管特征尺寸的缩小接近物理极限,导致主要的经济和技术挑战,预计将阻碍传统互补金属氧化物半导体(CMOS)技术的持续扩展。这导致了寻找替代新兴技术的趋势,这些技术可以继续提高计算系统的性能/功率。随着基础新兴技术的不断发展,计算机工程师必须将这些新兴设备的潜力转化为电路和架构设计。这些新的电路和架构设计将面临多重挑战,从这些新兴技术的建模/抽象和仿真,到由于纳米器件的庞大数量而必须集成复杂功能的复杂性。提案团队正在探索多种新兴技术(包括三维集成,新兴非易失性存储器和纳米光子学),全面覆盖新兴技术构建的电路和架构的建模,设计分析,仿真,验证,测试和评估方面的创新工作,以克服所有CMOS缩放限制。利用新兴技术高效地设计和使用未来的系统架构对未来的计算至关重要。获得的设备和芯片原型将用于培训研究生和本科生,包括那些由pi建议的代表性不足的群体(女性和少数民族学生),以获得计算机体系结构,超大规模集成电路(VLSI)和设备制造领域的专业知识。将为若干课程编制教材和实验单元,并在网上提供,以便更广泛地传播。与工业伙伴和国家实验室正在进行的合作将用于技术转让。
英文摘要
Aggressive technology scaling into the deep sub-micron (DSM) regime has been accompanied by a dramatic increase in transistor densities with shrinking transistor feature size that approaches the physical limits, resulting in major economic and technical challenges that are expected to hinder the continued scaling of traditional Complimentary Metal-Oxide Semiconductor (CMOS) technology. This has resulted in the trends of searching for alternative emerging technologies that can continue the performance/power improvement for computing systems. As the underlying emerging technologies continue to evolve, it has become imperative for computer engineers to translate the potential of such emerging devices into circuits and architecture designs. Such new circuits and architecture designs will face multiple challenges ranging from the modeling/abstraction and simulation of such emerging technologies, to the complexity due to the sheer volume of nanodevices that will have to be integrated for complex functionality.The proposing team is exploring multiple emerging technologies (including three-dimensional integration, emerging non-volatile memory, and nanophotonics) with a comprehensive coverage of innovative work in the modeling, design analysis, simulation, verification, testing, and evaluation of circuits and architectures constructed out of emerging technologies, to overcome all the CMOS scaling limits. Efficient design and use of future system architectures using emerging technologies will be vital to the future of computing. The acquired equipment and the chip prototyping will be used to train graduate students and undergraduate students including those from under-represented groups advised by the PIs (women and minority students) to gain expertise in the area of computer architecture, Very Large-Scale Integration (VLSI), and device fabrication. Teaching material and lab modules will be developed for several courses and made available on the web for wider dissemination. Ongoing collaborations with industry partners and national labs in current NSF-funded research will be used for transfer of technology.
期刊论文(25)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1109/isca45697.2020.00073
发表时间: 2020-05
期刊: 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA)
影响因子: --
作者: [Weitao Li;Pengfei Xu;Yang Zhao;Haitong Li;Yuan Xie;Yingyan Lin]
通讯作者: Weitao Li;Pengfei Xu;Yang Zhao;Haitong Li;Yuan Xie;Yingyan Lin
DOI: 10.1145/3352460.3358290
发表时间: 2019-10
期刊: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture
影响因子: --
作者: [Pengfei Zuo;Yu Hua;Yuan Xie]
通讯作者: Pengfei Zuo;Yu Hua;Yuan Xie
DOI: 10.1109/tvlsi.2018.2865133
发表时间: 2018-11-01
期刊: IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS
影响因子: 2.8
作者: [Xie, Mimi, Li, Shuangchen, Xie, Yuan]
通讯作者: Xie, Yuan
DOI: 10.1109/dac18072.2020.9218644
发表时间: 2020-07
期刊: 2020 57th ACM/IEEE Design Automation Conference (DAC)
影响因子: --
作者: [Maohua Zhu;Yuan Xie]
通讯作者: Maohua Zhu;Yuan Xie
共 23 条
    SHF:SMALL:Collaborative Research: Exploring Nonvolatility of Emerging Memory Technologies for Architecture Design
    SPX: Collaborative Research: Ula! - An Integrated Deep Neural Network (DNN) Acceleration Framework with Enhanced Unsupervised Learning Capability
    XPS: FULL: DSD: Collaborative Research: Parallelizing and Accelerating Metagenomic Applications
    SHF: Medium: ASKS - Architecture Support for darK Silicon
    海外基金