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.

通过社交媒体，生物医学监测和The-Things产生的数据呈指数增长。从这些大数据中提取有用信息的能力对于各种域的进步至关重要。数据的巨大性超出了电子系统处理，通信和存储正在生成的数据的能力。尽管历史上晶体管的缩放有助于满足计算需求的增长，但随着缩放的限制，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，该平台可以提供前所未有的机会，以在第三个维度扩展芯片设计，从而通过无缝集成来模糊内存和逻辑功能之间的差距，从而加速大数据分析。我们已经将研究人员聚集在一起，从材料到系统，以跨层的专业知识来创建铁电子平台，以提供变革性的动力来加速大数据分析。组合练习将有助于吸引其他专业知识和参与研讨会和试点项目。我们的跨层策略将导致同时进步，例如新型的多门设备，这些设备利用极化来实现复杂功能的紧凑型，新的体系结构来利用铁电的内在特性，例如极化领域和光学敏感性，以支持新的安全性和传感器。和存储计算以支持大量数据的就地分析。设备铁电系统将对从大数据中心到the Internet的计算基础架构的能源效率产生变革性的影响。一个高度有能力的劳动力将成为这个新兴地区全球领导力的基础。因此，与社区学院，四年制机构和行业合作的劳动力发展战略将是重点。我们的计划工作还将投资于飞行员跨机构项目，以加快代表性不足社区的个人的参与。计划的研讨会将评估教育结构和培训计划，以及流程整合和技术的加强，以迅速建立美国作为铁电创新的中心。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来通过评估来支持的。