Collaborative Research: Spintronics Enabled Stochastic Spiking Neural Networks with Temporal Information Encoding

合作研究：自旋电子学支持具有时间信息编码的随机尖峰神经网络

• 批准号: 2333882
• 负责人: Weigang Wang
• 金额: $ 25万
• 依托单位: University of Arizona
• 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: 2024-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2333882&HistoricalAwards=false
• 关键词: Collaborative; Research; Spintronics; Enabled; Stochastic

Neuromorphic computing architectures attempt to bridge the computational efficiency gap of Artificial Intelligence platforms by emulating certain facets of the computational units and in-situ synaptic storage of the brain in the underlying algorithms and hardware substrate. This research addresses one of the main challenges facing neuromorphic computing today -- How to make bio-plausible spiking neural networks (SNNs) scalable and efficient for large-scale machine learning tasks while persevering the benefits of sparse, event-driven computation and learning? Currently, SNNs remain very similar to non-spiking networks with the temporal aspect remaining largely unexploited. The project is driven by the motivation that the current gap in SNN efficiency metrics (recognition accuracy, hardware power, energy and area efficiency) will be bridged by a transformative rethinking of spike information encoding in the temporal domain along with exploring nanoelectronic devices amenable for such alternate spike encoding schemes that leverage its inherent stochastic physics for brain-like probabilistic inference. Combining these two perspectives, stochastic biomimetic hardware, encoding information in the temporal domain, has the potential of enabling a new generation of brain-inspired computing platforms that leverages the associated advantages of two complementary insights from computational neuroscience -- how information is encoded in the brain and how computing occurs in the brain. The cross-layer nature of the project ranging from device design, circuit, system and algorithm explorations will serve as an ideal platform to enable interdisciplinary training and education of graduate and undergraduate students including women and underrepresented minority communities.The research involves a transformative research agenda, at the intersection of hardware and software, that develops a cross-layer design effort from devices to algorithms and underlying learning methodologies. The project spans cross-cutting explorations across the following thrust areas: (i) Thrust 1 investigates spin device physics and proposes device-circuit primitives suitable for temporal information encoding and learning in stochastic neuromorphic computing platforms. (ii) Thrust 2 considers system development that inherently exploits the temporal encoding of information in stochastic magnetic devices. (iii) Hardware-algorithm co-design resulting from Thrusts 1 and 2 will culminate in Thrust 3 that will consider large-scale system level simulations and performance evaluation across a benchmark application suite. Such an end-to-end framework can enable the fusion of appropriate neuromorphic computing paradigms with the intrinsic operation of the underlying hardware to improve its performance (classification accuracy) and efficiency for complex machine learning tasks. Successful completion of the project offers the basis for a significant leap in the quest to implement machine intelligence with brain-scale efficiency by pursuing a multi-disciplinary perspective spanning devices, circuits, systems, machine learning and computational neuroscience.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.

神经形态计算架构试图通过在底层算法和硬件基板中模拟计算单元的某些方面和大脑的原位突触存储来弥合人工智能平台的计算效率差距。这项研究解决了当今神经形态计算面临的主要挑战之一-如何使生物似然尖峰神经网络（SNN）可扩展且高效地用于大规模机器学习任务，同时保持稀疏，事件驱动的计算和学习的优势？目前，SNN仍然非常类似于非尖峰网络，其时间方面在很大程度上尚未开发。该项目的动机是SNN效率指标（识别准确性，硬件功率，能源和面积效率）的当前差距将被弥合的尖峰信息编码在时间域的一个变革性的反思沿着探索纳米电子设备适合这种替代尖峰编码方案，利用其固有的随机物理学的类脑概率推理。结合这两个观点，随机仿生硬件，在时间域中编码信息，有可能实现新一代的大脑启发计算平台，利用计算神经科学的两个互补见解的相关优势-信息如何在大脑中编码以及计算如何在大脑中发生。该项目的跨层性质，从器件设计，电路，系统和算法探索，将作为一个理想的平台，使跨学科的培训和教育的研究生和本科生，包括妇女和代表性不足的少数民族社区。该研究涉及一个变革性的研究议程，在硬件和软件的交叉点，开发从设备到算法和底层学习方法的跨层设计工作。该项目跨越了以下主要领域的交叉探索：（i）第一个重点研究自旋器件物理学，并提出了适合于随机神经形态计算平台中的时间信息编码和学习的器件电路基元。(ii)推力2认为系统的发展，固有地利用随机磁设备中的信息的时间编码。(iii)Thrust 1和2产生的硬件算法协同设计将在Thrust 3中达到高潮，Thrust 3将考虑跨基准应用套件的大规模系统级仿真和性能评估。这样的端到端框架可以使适当的神经形态计算范例与底层硬件的内在操作融合，以提高其性能（分类准确性）和复杂机器学习任务的效率。该项目的成功完成为实现具有大脑规模效率的机器智能提供了重要的基础，通过追求跨设备，电路，系统，机器学习和计算神经科学的多学科视角，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而被认为值得支持。