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.

该奖项是根据2021年《美国救援计划法》（公法117-2）的全部或部分资助的。人工智能（AI），神经网络从人脑中利用了灵感来启用对社会产生深远影响社会的机器学习系统。 AI系统在系统部署后不断学习的能力特别有前途，因为本在线学习提供了开发新功能并适应不断变化的环境的潜力。但是，传统的机器学习算法要求将大量数学操作应用于大型数据集，需要复杂的硬件和大量的能耗，以阻止使用后部署后在线学习的AI系统的开发。因此，该项目提议从神经生物学中获得进一步的灵感，并从局部突触活动中出现节能的在线学习算法。这种本地化的学习方法将大大推动在线学习系统的开发，从而影响广泛的自治应用，例如自动驾驶汽车和健康监控设备。该项目还将通过K-12教育外展，本科研究，研究生教育以及本地和国际社区的参与来扩大计算的参与。为了启用节能在线学习，该项目将采用自下而上的方法来设计神经型网络的设计。这种自下而上的方法不是在计算廉价的电路中实现监督学习算法（例如向后流动）的常规自上而下的方法，而是将互连的人工神经元和突触互连，从而使能源不足的学习算法从本地化的更新更新规则中出现。该项目将集中于具有类似物和滞后行为的自旋神经形态成分，利用了铸造厂制造能力的显着进步。特别是，从这种自下而上的方法中出现的学习算法将是数学表征的，允许使用Spintronic神经态学习网络的设备电路 - 电路 - 算法共同设计。这些自旋神经形态网络将在实验上证明，以从局部学习规则中生成有效的学习算法，并将开发用于设备和系统优化的目标，以提供将路线图转换为实用AI系统的路线图。总的来说，该项目将加深对旋转物理学的了解，增加对神经系统实现学习的机制的科学理解，并为具有在线学习的革命性AI系统开辟了一条途径。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛的影响来评估的支持。

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