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
• 负责人: Joseph Friedman
• 金额: $ 50万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146439&HistoricalAwards=false
• 关键词: CAREER; Bottom; Up; Localized; Online

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Artificial intelligence (AI) and neural networks have leveraged inspiration from the human brain to enable machine-learning systems that deeply impact society. The capability of an AI system to continually learn after system deployment is particularly promising, as this online learning provides the potential to develop new functionalities and adapt to changing environments. However, conventional machine-learning algorithms require the application of an enormous quantity of mathematical operations to large data sets, requiring complex hardware and large energy consumption that hinders the development of AI systems with post-deployment online learning. This project therefore proposes taking further inspiration from neurobiology, with energy-efficient online learning algorithms that emerge from local synapse activity. This localized learning approach will significantly advance the development of online learning systems, impacting a wide range of autonomy applications such as self-driving cars and health-monitoring devices. This project will also broaden participation in computing through K-12 educational outreach, undergraduate research, graduate education, and the involvement of the local and international communities.To enable energy-efficient online learning, this project will apply a bottom-up approach to the design of neuromorphic networks. Rather than the conventional top-down approach in which supervised learning algorithms (such as backpropagation) are implemented in computationally-expensive circuits, this bottom-up approach will interconnect artificial neurons and synapses such that energy-efficient unsupervised learning algorithms emerge from localized synaptic updating rules. This project will focus on spintronic neuromorphic components with analog and hysteretic behaviors, leveraging the remarkable recent progress in foundry fabrication capabilities. In particular, the learning algorithms that emerge from this bottom-up approach will be mathematically characterized, permitting device-circuit-algorithm co-design of spintronic neuromorphic learning networks. These spintronic neuromorphic networks will be experimentally demonstrated to generate effective learning algorithms from localized learning rules, and targets for device and system optimization will be developed to provide a roadmap for translation to practical AI systems. Altogether, this project will deepen knowledge of spintronic physics, increase scientific understanding of the mechanisms through which learning is achieved by neural systems, and open a pathway for revolutionary AI systems with online learning.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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。人工智能（AI）和神经网络利用人类大脑的灵感，使机器学习系统能够深刻影响社会。人工智能系统在系统部署后持续学习的能力特别有前途，因为这种在线学习提供了开发新功能和适应不断变化的环境的潜力。然而，传统的机器学习算法需要将大量的数学运算应用于大型数据集，需要复杂的硬件和大量的能耗，这阻碍了具有部署后在线学习的AI系统的开发。因此，该项目建议从神经生物学中获得进一步的灵感，通过从局部突触活动中产生的节能在线学习算法。这种本地化的学习方法将大大推动在线学习系统的发展，影响广泛的自动驾驶应用，如自动驾驶汽车和健康监测设备。该项目还将通过K-12教育推广、本科生研究、研究生教育以及当地和国际社区的参与来扩大对计算的参与。为了实现节能的在线学习，该项目将采用自下而上的方法来设计神经形态网络。与传统的自上而下的方法不同，在传统的自上而下的方法中，监督学习算法（如反向传播）是在计算昂贵的电路中实现的，这种自下而上的方法将互连人工神经元和突触，使得节能的无监督学习算法从局部突触更新规则中出现。该项目将集中于具有模拟和滞后行为的自旋电子神经元元件，利用铸造制造能力的显着最新进展。特别是，从这种自下而上的方法中出现的学习算法将在数学上表征，允许自旋电子神经形态学习网络的设备-电路-算法协同设计。这些自旋电子神经形态网络将通过实验证明，从局部学习规则中生成有效的学习算法，并将开发设备和系统优化目标，为转化为实用的AI系统提供路线图。总之，该项目将深化自旋电子物理学的知识，提高对神经系统实现学习的机制的科学理解，并为革命性的人工智能系统打开一条在线学习的道路。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Magnetic skyrmions and domain walls for logical and neuromorphic computing

用于逻辑和神经形态计算的磁性斯格明子和畴壁

• DOI: 10.1088/2634-4386/acc6e8
• 发表时间: 2023
• 期刊: Neuromorphic Computing and Engineering
• 作者: Hu, Xuan;Cui, Can;Liu, Samuel;Garcia-Sanchez, Felipe;Brigner, Wesley H;Walker, Benjamin W;Edwards, Alexander J;Xiao, T Patrick;Bennett, Christopher H;Hassan, Naimul
• 通讯作者: Hassan, Naimul

Roadmap for unconventional computing with nanotechnology

• DOI: 10.1088/2399-1984/ad299a
• 发表时间: 2024-03-01
• 期刊: NANO FUTURES
• 影响因子: 2.1
• 作者: Finocchio，Giovanni;Incorvia，Jean Anne C.;Bandyopadhyay，Supriyo
• 通讯作者: Bandyopadhyay，Supriyo