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FuSe-TG: FAB: A Heterogeneous Ferroelectronics Platform for Accelerating Big Data Analytics

FuSe-TG：FAB：加速大数据分析的异构铁电子平台

基本信息

批准号：
2235366
负责人：
VIJAYKRISHNAN NARAYANAN
金额：
$ 60万
依托单位：
Pennsylvania State Univ University Park
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2025-04-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2235366&HistoricalAwards=false
关键词：
FuSe TG FAB Heterogeneous Ferroelectronics

项目摘要

Data generated over social media, biomedical monitoring, and the Internet-of-Things are increasing exponentially. The ability to extract useful information from this big data is critical towards advances in varied domains. The enormity of the data is outstripping the ability for electronic systems to process, communicate and store the data that is being generated. While historically transistor scaling helped address the growth in computational needs, with scaling now reaching its limits, new approaches are needed. To this end, this project brings together experts for realizing a new ferroelectric-based platform that can provide unprecedented opportunities to scale chip designs in the third dimension, to blur the gap between memory and logic functionality through seamless integration to accelerate big data analytics. We have gathered together researchers with cross-layer expertise from materials to systems to create a ferroelectronics platform to provide transformative impetus towards accelerating big data analytics. The teaming exercise will help draw in additional expertise and engagement in workshops and pilot projects. Our cross-layer strategies will lead to concurrent advances such as novel multi-gate devices that leverage the polarization for compact implementation of complex functions, new architectures that harness the intrinsic properties of ferroelectrics, such as polarization domains and optical sensitivity, to support novel security and sensor features. and in-storage computing to support in-place analysis of large amounts of data.Novel ferroelectric systems will have transformative impact on energy efficiency of computing infrastructure from large data centers to the Internet-of-Things. A highly capable workforce will be the foundation of global leadership in this emerging area. Consequently, strategies for workforce development in partnership with community colleges, four-year institutions and industry will be a key focus. Our planning effort will also invest in pilot cross-institutional projects to accelerate engagement of individuals from underrepresented communities. Planned workshops will assess the educational structure and training programs along with process integration and technology ramp-up to rapidly establish the United States as the center of ferroelectric innovation.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.

社交媒体、生物医学监测和物联网生成的数据正在呈指数级增长。从这些大数据中提取有用信息的能力对于各个领域的进步至关重要。数据的复杂性超过了电子系统处理、通信和存储正在生成的数据的能力。虽然历史上晶体管缩放有助于解决计算需求的增长，但随着缩放现在达到极限，需要新的方法。为此，该项目汇集了专家，以实现一个新的基于铁电的平台，可以提供前所未有的机会，在第三维中扩展芯片设计，通过无缝集成来模糊内存和逻辑功能之间的差距，以加速大数据分析。我们聚集了从材料到系统的跨层专业知识的研究人员，以创建一个铁电平台，为加速大数据分析提供变革动力。协作活动将有助于吸引更多的专门知识和参与讲习班和试点项目。我们的跨层策略将带来同步进步，例如利用极化实现复杂功能的新型多门器件，利用铁电体固有特性（如极化域和光学灵敏度）的新架构，以支持新的安全和传感器功能。新型铁电系统将对从大型数据中心到物联网的计算基础设施的能源效率产生变革性影响。一支高素质的员工队伍将是这一新兴领域全球领导力的基础。因此，与社区学院、四年制院校和工业界合作的劳动力发展战略将是一个重点。我们的规划工作还将投资于试点跨机构项目，以加速代表性不足社区的个人参与。计划中的研讨会将评估教育结构和培训计划，沿着工艺整合和技术提升，以迅速将美国确立为铁电创新的中心。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，被认为值得支持。