FET: SHF: Small: Collaborative: Advanced Circuits, Architectures and Design Automation Technologies for Energy-efficient Single Flux Quantum Logic
FET: SHF: Small: Collaborative: Advanced Circuits, Architectures and Design Automation Technologies for Energy-efficient Single Flux Quantum Logic
批准号:
2008514
负责人:
Yanzhi Wang
金额:
$20.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-06-15 至 2024-05-31
中文摘要
世界经济对计算领域的节能运营的严重依赖现已几乎得到普遍认可。为此,“超越 CMOS”器件技术和相应逻辑系列的进步现在被视为实现高性能计算下一次重大飞跃的关键一步。本研究中描述的挑战和机遇为开发非常有前途的“超越 CMOS”技术的许多方面提供了方向,该技术可以产生极高性能且节能的计算系统,从而确保信息技术生态系统的可持续性。 基于约瑟夫森结 (JJ) 单通量量子 (SFQ) 逻辑单元的超导电子 (SCE) 已发展成为一种触手可及的“超越 CMOS”技术,其开关速度可达数百 GHz,每次转换的能量耗散为 10^-19 焦耳或更少。该项目将通过生产适用于各种计算结构的超高性能和节能电子产品来增强商业和社会机会,在此过程中还将通过为本科生、研究生和代表性不足的学生提供教育和研究机会,将他们纳入计划的研究,从而有助于增强美国的技术能力。这项研究旨在在先进电路、架构和设计自动化技术的开发方面取得重大进展,以支持大规模超导SFQ数字电子产品,以满足未来的需求节能、高性能埃级计算系统。设计自动化研究将使大规模SCE系统集成成为可能。本研究针对直流供电的节能快速 SFQ 和交流供电的绝热量子通量参量管电路系列,旨在解决与 SFQ 逻辑电路的设计自动化和优化相关的四个关键问题,即: 最大限度地减少 SFQ 电路重定时到缓冲器的数量,以提高工作频率和/或时钟相位一致性;带环路的顺序 SFQ 电路的路径平衡技术映射;使用 SFQ 逻辑系列的独特功能和强大的数学优化工具进行时序驱动的全局布局;该奖项反映了 NSF 的法定使命,并通过使用基金会的智力优点和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The critical dependence of the world economy on energy-efficient operations in computing are now almost universally recognized. To this end, advances in “beyond-CMOS” device technologies and corresponding logic families are now seen as a key step towards achieving the next major leap in high-performance computing. The challenges and opportunities described in this research provide directions for developing many aspects of a very promising “beyond-CMOS” technology, which can result in extremely high-performance, yet energy-efficient, computing systems, and thereby ensure sustainability of the information-technology ecosystem. SuperConductive Electronics (SCE) based on the Josephson junction (JJ) Single Flux Quantum (SFQ) logic cells have evolved into a within-reach “beyond-CMOS” technology, with switching speeds in the hundreds of GHz and energy dissipation of 10^-19 or less Joules per transition. The project will enhance business and societal opportunities by producing ultra-high performance and energy-efficient electronics for a wide range of computing fabrics, and in the process will also contribute to enhancing the technological capabilities of the US by providing education and research opportunities to undergraduate, graduate, and underrepresented students by including them in the planned research.This research aims to achieve major strides in the development of advanced circuits, architectures and design-automation technologies in support of large-scale superconductive SFQ digital electronics to meet the needs of future energy-efficient, high-performance exa-scale computing systems. Research on design automation will enable large-scale SCE systems integration. Targeting both DC-powered energy-efficient Rapid SFQ and AC-powered Adiabatic Quantum-Flux-Parametron circuit families, this research aims to solve four key problems associated with the design automation and optimization of SFQ logic circuits, namely: minimization of SFQ circuit retiming to the number of buffers for operating frequency improvement and/or clock-phase consistency; path-balancing technology mapping for sequential SFQ circuits with loops; timing-driven global placement using unique features of SFQ logic families and a powerful mathematical optimization tool; and circuit partitioning to enable effective current recycling.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.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
Towards AQFP-Capable Physical Design Automation
迈向支持 AQFP 的物理设计自动化
DOI:
--
发表时间:
2021
期刊:
and Test in Europe (DATE
影响因子:
--
作者:
[Hongjia Li, Mengshu Sun]
通讯作者:
Hongjia Li, Mengshu Sun
TAAS: a timing-aware analytical strategy for AQFP-capable placement automation
TAAS:用于支持 AQFP 的贴装自动化的时序感知分析策略
DOI:
10.1145/3489517.3530487
发表时间:
2022
期刊:
Design Automation Conference (DAC
影响因子:
--
作者:
[Dong, Peiyan, Xie, Yanyue, Li, Hongjia, Sun, Mengshu, Chen, Olivia, Yoshikawa, Nobuyuki, Wang, Yanzhi]
通讯作者:
Wang, Yanzhi
Collaborative Research: CSR: Small: Expediting Continual Online Learning on Edge Platforms through Software-Hardware Co-designs
-
批准号:2312158
-
项目类别:Standard Grant
-
资助金额:$25.0万
-
财政年份:2023
-
负责人:Yanzhi Wang
-
依托单位:
SPX: Collaborative Research: FASTLEAP: FPGA based compact Deep Learning Platform
-
批准号:1919117
-
项目类别:Standard Grant
-
资助金额:$35.0万
-
财政年份:2019
-
负责人:Yanzhi Wang
-
依托单位:
CNS Core: Small: Collaborative: Content-Based Viewport Prediction Framework for Live Virtual Reality Streaming
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批准号:1909172
-
项目类别:Standard Grant
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资助金额:$17.12万
-
财政年份:2019
-
负责人:Yanzhi Wang
-
依托单位:
IRES Track I: U.S.-Japan International Research Experience for Students on Superconducting Electronics
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批准号:1854213
-
项目类别:Standard Grant
-
资助金额:$29.93万
-
财政年份:2019
-
负责人:Yanzhi Wang
-
依托单位:
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