Reversible Computing and Reservoir Computing with Magnetic Skyrmions for Energy-Efficient Boolean Logic and Artificial Intelligence Hardware
用于节能布尔逻辑和人工智能硬件的磁斯格明子可逆计算和储层计算
基本信息
- 批准号:2343607
- 负责人:
- 金额:$ 25万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2024
- 资助国家:美国
- 起止时间:2024-03-15 至 2027-02-28
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
As the increasing pervasiveness of computers throughout society has led to drastic increases in the energy consumed by computers, there is a strong need to improve the energy efficiency of computers. In addition to minimizing the economic and environmental costs resulting from computer energy consumption, enhancing the energy efficiency of computers will also allow for increased computing capabilities with beneficial impacts throughout society. This project will therefore design and experimentally demonstrate computing systems with extreme energy efficiency, both for high-performance computing and for artificial intelligence. This extreme energy efficiency will be achieved by leveraging magnetic skyrmions, which are magnetic quasiparticles that have been predicted to be suitable for energy-efficient conventional computing in the reversible computing paradigm as well as for energy-efficient artificial intelligence through the reservoir computing paradigm. This project will significantly advance the development of computers with extreme energy efficiency, thereby reducing environmental harm, facilitating economic development, and enabling revolutionary computing applications that require minimal power dissipation. This project will also have beneficial impacts on workforce development through the inclusion of undergraduate research participants and the vertical training of graduate students from devices to systems.This project will design and experimentally demonstrate reversible and reservoir computers with magnetic skyrmions. Magnetic skyrmions are swirls of magnetic spin texture that are energy-protected once created. They are tunable in size, can operate at room temperature, and have dynamical response to current, voltage, and field, making them a good choice for use in future computing paradigms. Based on preliminary designs and simulations of the PIs on the efficient use of skyrmions in reversible and reservoir computing, this project has four main objectives. Firstly, skyrmion reversible computer co-design will be carried out to efficiently drive the skyrmions, to determine optimal parameters, and to develop a roadmap for the future of skyrmion reversible computing. Secondly, skyrmion reversible computer fabrication will be carried out to demonstrate and analyze skyrmion stability, voltage-driven skyrmion propagation, skyrmion interactions mediated by the skyrmion-Hall effect, and reversible skyrmion logic gates. Thirdly, skyrmion reservoir computing co-design will be carried out to maximize the reservoir expressivity and energy efficiency, to determine optimal parameters, and to develop a roadmap for the future of skyrmion reservoir computing. And fourthly, skyrmion reservoir computer fabrication will be carried out to measure multi-skyrmion interactions, to control repeatability through pinning sites, and to demonstrate and characterize skyrmion reservoir computers. This project will advance the science of thin film magnetism, improve understanding of the dynamics of magnetic nanostructures, develop new device designs and fabrication methods for skyrmion-based devices, and develop and implement circuits and systems to leverage those dynamics. This project will thus advance knowledge in materials, devices, and computing.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.
随着整个社会的计算机的普遍性日益增加导致计算机消耗的能源的急剧增加,因此非常需要提高计算机的能源效率。除了最大程度地减少计算机能源消耗所产生的经济和环境成本外,提高计算机的能源效率还将允许在整个社会中提高计算能力并产生有益的影响。因此,该项目将设计并实验展示具有极端能源效率的计算系统,包括高性能计算和人工智能。这种极端的能源效率将通过利用磁性天空来实现,这些磁性准颗粒预测,这些准颗粒适用于可逆计算范式中的节能常规计算以及通过储层计算范式通过储层范式进行能源效率的人工智能。该项目将大大推动具有极端能源效率的计算机的开发,从而减少环境危害,促进经济发展,并实现需要最少功率耗散的革命计算应用程序。该项目还将通过包括本科研究参与者以及从设备到系统的研究生进行垂直培训对劳动力发展产生有益的影响。该项目将设计并实验证明具有磁性天空的可逆计算机。磁性天空是磁性自旋纹理的漩涡,一旦产生能量保护。它们的尺寸可调节,可以在室温下运行,并且对当前,电压和场具有动态响应,这使它们成为将来计算范式使用的理想选择。基于PI的初步设计和模拟在可逆和储层计算中有效使用天际,该项目具有四个主要目标。首先,将进行Skyrmion可逆的计算机共同设计,以有效地推动Skyrmions,确定最佳参数,并为Skyrmion可逆计算的未来开发路线图。其次,将进行Skyrmion可逆的计算机制造,以证明和分析天际稳定性,电压驱动的天空传播,由Skyrmion-Hall效应介导的Skyrmion相互作用以及可逆的Skyrmion逻辑。第三,将执行Skyrmion储层计算共同设计,以最大程度地提高储层的表达性和能源效率,以确定最佳参数,并为Skyrmion Reservoir Computing的未来开发路线图。第四,将进行Skyrmion Reservoir计算机制造,以测量多形相互作用,以通过固定位点来控制重复性,并演示和表征Skyrmion Reservoir计算机。该项目将推动薄膜磁性的科学,改善对磁性纳米结构动态的理解,为基于天际的设备开发新的设备设计和制造方法,并开发和实施电路和系统以利用这些动力学。因此,该项目将在材料,设备和计算方面提高知识。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影响的审查标准来评估值得支持的。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Joseph Friedman其他文献
Prevalence of cogwheel phenomenon in Parkinson's disease
- DOI:
10.1016/j.jns.2023.121735 - 发表时间:
2023-12-01 - 期刊:
- 影响因子:
- 作者:
Soumitri Barua;Wafae Chouhani;Anelyssa D'Abreu;Joseph Friedman;Umer Akbar - 通讯作者:
Umer Akbar
Improving the estimation of educational attainment: New methods for assessing average years of schooling from binned data
改进教育程度的估计:根据分箱数据评估平均受教育年限的新方法
- DOI:
- 发表时间:
2018 - 期刊:
- 影响因子:3.7
- 作者:
Joseph Friedman;Nicholas Graetz;E. Gakidou - 通讯作者:
E. Gakidou
ANISOTROPY AND TRACTOGRAPHY IN THE INTERNAL CAPSULE IN THE SCHIZOPHRENIA SPECTRUM
- DOI:
10.1016/s0920-9964(08)70223-8 - 发表时间:
2008-06-01 - 期刊:
- 影响因子:
- 作者:
Igor Nenadic;Erin Hazlett;Joseph Friedman;Mehmet Haznedar;King-Wai Chu;Jonathan Entis;Chelain R. Goodman;Randall Newmark;Adam Robson;Jing Zhang;Emily Canfield;Monte Buchsbaum - 通讯作者:
Monte Buchsbaum
594 - A preliminary study of the safety and efficacy of guanfacine for the treatment of cognitive and negative symptoms in schizophrenia
- DOI:
10.1016/s0920-9964(97)82602-3 - 发表时间:
1997-01-01 - 期刊:
- 影响因子:
- 作者:
Peter Powchik;Joseph Friedman;Leonid Remenson;Mark Smith - 通讯作者:
Mark Smith
Looking Back on COVID-19 and the Evolving Drug Overdose Crisis: Updated Trends Through 2022.
回顾 COVID-19 和不断演变的药物过量危机:2022 年的最新趋势。
- DOI:
- 发表时间:
2024 - 期刊:
- 影响因子:12.7
- 作者:
Joseph Friedman - 通讯作者:
Joseph Friedman
Joseph Friedman的其他文献
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{{ truncateString('Joseph Friedman', 18)}}的其他基金
Collaborative Research: 2D Ambipolar Machine Learning & Logical Computing Systems
合作研究:2D 双极机器学习
- 批准号:
2154314 - 财政年份:2022
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
CAREER: Bottom-Up Localized Online Learning with Spintronic Neuromorphic Networks
职业:利用自旋电子神经形态网络进行自下而上的本地化在线学习
- 批准号:
2146439 - 财政年份:2022
- 资助金额:
$ 25万 - 项目类别:
Continuing Grant
FET: Small: Collaborative Research: Integrated Spintronic Synapses and Neurons for Neuromorphic Computing Circuits - I(SNC)^2
FET:小型:协作研究:用于神经形态计算电路的集成自旋电子突触和神经元 - I(SNC)^2
- 批准号:
1910800 - 财政年份:2019
- 资助金额:
$ 25万 - 项目类别:
Standard Grant
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- 批准号:51879192
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双向连通条件下供水水库群预报优化调度研究
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- 批准号:51509102
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- 批准号:41404078
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基于非线性实证和社会计算的水库移民社会融合机制研究
- 批准号:71401090
- 批准年份:2014
- 资助金额:20.0 万元
- 项目类别:青年科学基金项目
相似海外基金
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合作研究:FET:小型:基于离子通道忆阻器的储层计算
- 批准号:
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Collaborative Research: Reversible Computing and Reservoir Computing with Magnetic Skyrmions for Energy-Efficient Boolean Logic and Artificial Intelligence Hardware
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- 批准号:
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EU-Funded
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