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: $ 50万
• 依托单位: University of California-Santa Barbara
• 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
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1730309&HistoricalAwards=false
• 关键词: II; New; RICARDO; Research; Infrastructure

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.

扩展到深亚微米（DSM）范围的积极技术已经伴随着晶体管密度的急剧增加，其中缩小的晶体管特征尺寸接近物理极限，从而导致预计将阻碍传统互补金属氧化物半导体（CMOS）技术的持续扩展的主要经济和技术挑战。这导致了寻找能够继续计算系统的性能/功率改进的替代新兴技术的趋势。随着底层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。这种新的电路和架构设计将面临多种挑战，从这些新兴技术的建模/抽象和仿真，到由于必须集成用于复杂功能的纳米器件的数量庞大而导致的复杂性。（包括三维集成，新兴的非易失性存储器和纳米光子学）与全面覆盖的创新工作的建模，设计分析、仿真、验证、测试和评估基于新兴技术构建的电路和架构，以克服所有CMOS缩放限制。 使用新兴技术的未来系统架构的有效设计和使用对于计算的未来至关重要。获得的设备和芯片原型将用于培训研究生和本科生，包括PI建议的代表性不足的群体（妇女和少数民族学生），以获得计算机体系结构，超大规模集成（VLSI）和设备制造领域的专业知识。将为几门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。与行业合作伙伴和国家实验室在当前NSF资助的研究中正在进行的合作将用于技术转让。

项目成果

Timely: Pushing Data Movements And Interfaces In Pim Accelerators Towards Local And In Time Domain

• 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)
• 作者: Weitao Li;Pengfei Xu;Yang Zhao;Haitong Li;Yuan Xie;Yingyan Lin

SuperMem: Enabling Application-transparent Secure Persistent Memory with Low Overheads

• DOI: 10.1145/3352460.3358290
• 发表时间: 2019-10
• 期刊: Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture
• 作者: Pengfei Zuo;Yu Hua;Yuan Xie

Securing Emerging Nonvolatile Main Memory With Fast and Energy-Efficient AES In-Memory Implementation

• 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

Taming Unstructured Sparsity on GPUs via Latency-Aware Optimization

• DOI: 10.1109/dac18072.2020.9218644
• 发表时间: 2020-07
• 期刊: 2020 57th ACM/IEEE Design Automation Conference (DAC)
• 作者: Maohua Zhu;Yuan Xie

Practical Attacks on Deep Neural Networks by Memory Trojaning

• DOI: 10.1109/tcad.2020.2995347
• 发表时间: 2021-06-01
• 期刊: IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
• 影响因子: 2.9
• 作者: Hu, Xing;Zhao, Yang;Xie, Yuan
• 通讯作者: Xie, Yuan