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

基本信息

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

项目摘要

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.

该奖项的全部或部分资金来源于《2021 年美国救援计划法案》（公法 117-2）。人工智能 (AI) 和神经网络利用人脑的灵感，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对大数据集进行大量的数学运算，需要复杂的硬件和大量的能耗，这阻碍了部署后在线学习的人工智能系统的发展。因此，该项目建议从神经生物学中进一步汲取灵感，利用从局部突触活动中产生的节能在线学习算法。这种本地化学习方法将显着推进在线学习系统的发展，影响广泛的自主应用，例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。为了实现节能在线学习，该项目将采用自下而上的方法神经形态网络的设计。这种自下而上的方法不同于传统的自上而下的方法，即在计算成本高昂的电路中实现监督学习算法（例如反向传播），而是将人工神经元和突触互连起来，从而从局部突触更新中产生节能的无监督学习算法规则。该项目将重点研究具有模拟和迟滞行为的自旋电子神经形态组件，利用铸造制造能力方面最近取得的显着进展。特别是，从这种自下而上的方法中产生的学习算法将在数学上进行表征，从而允许自旋电子神经形态学习网络的设备-电路-算法共同设计。这些自旋电子神经形态网络将通过实验证明能够根据本地化学习规则生成有效的学习算法，并且将开发设备和系统优化的目标，以提供转化为实际人工智能系统的路线图。总而言之，该项目将加深对自旋电子物理学的了解，增进对神经系统实现学习机制的科学理解，并为具有在线学习的革命性人工智能系统开辟道路。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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