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

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
批准号:
2235366
负责人:
VIJAYKRISHNAN NARAYANAN
金额:
$60.0万
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
项目状态:
未结题
起止时间:
2023-05-01 至 2025-04-30

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中文摘要
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英文摘要
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.
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DOI: 10.3390/ma17030589
发表时间: 2024-01
期刊: Materials
影响因子: 3.4
作者: [Morteza Fathipour;Yanan Xu;Mukti Rana]
通讯作者: Morteza Fathipour;Yanan Xu;Mukti Rana
Collaborative Research: FET: Medium:Compact and Energy-Efficient Compute-in-Memory Accelerator for Deep Learning Leveraging Ferroelectric Vertical NAND Memory
EFRI BRAID: Neuroscience Inspired Visual Analytics
SHF: Small: Leveraging Monolithic 3D for Architectural Innovations
Collaborative Research: Visual Cortex on Silicon
  • 批准号:
    1317560
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $482.35万
  • 财政年份:
    2013
  • 负责人:
    VIJAYKRISHNAN NARAYANAN
  • 依托单位:
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