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: 2333881
• 负责人: Abhronil Sengupta
• 金额: $ 25万
• 依托单位: Pennsylvania State Univ University Park
• 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=2333881&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.

神经形态计算体系结构试图通过模拟计算单元的某些方面和基础算法和硬件基板中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 - 如何使生物学成分的尖峰神经网络（SNN）可扩展且有效地用于大规模机器学习任务，同时坚持稀疏，事件驱动的计算和学习的益处？当前，SNN仍然与非加速网络非常相似，而时间方面仍然没有探索。该项目是由当前SNN效率指标（识别精度，硬件功率，能量和区域效率）的差距驱动的驱动的，将通过对时间域中编码的尖峰信息进行的变革性重新思考，同时探索纳米电子设备在此类交替的跨度编码方案中构成固定的构成范围的构成范围构成范围构成范围的物质，并探索纳米电子设备可进行。结合了这两种观点，即随机仿生硬件，编码时间域中的信息，具有实现新一代脑启发的计算平台的潜力，这些计算平台利用了来自计算神经科学的两个互补见解的相关优势 - 大脑中的信息是在大脑中以及计算在大脑中的计算方式。 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方法论。该项目跨越以下推力区域进行了横切探索：（i）刺激1研究旋转装置物理学，并提出了适用于随机神经形态计算平台中的时间信息编码和学习的设备电路原始图。 （ii）推力2考虑系统开发，该系统开发本质上利用了随机磁性设备中信息的时间编码。 （iii）推力1和2产生的硬件 - 算法共同设计将在推力3中达到最终形式，该推力将考虑大规模的系统级模拟和基准应用程序套件上的性能评估。这样的端到端框架可以使适当的神经形态计算范式与基础硬件的内在操作融合，以提高其性能（分类精度）和复杂的机器学习任务的效率。该项目的成功完成为寻求通过脑尺度效率实施机器智能的巨大飞跃提供了基础，通过追求多学科的视角跨越设备，电路，系统，机器学习和计算神经科学。这奖反映了NSF的立法任务，并被认为是通过基金会的智力效果和广泛的评估来评估的，并且值得通过评估来进行评估。