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

神经形态计算架构试图通过模拟计算单元的某些方面以及大脑在底层算法和硬件衬底中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--如何使生物可信的尖峰神经网络(SNN)可扩展且高效地执行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好处？目前，SNN仍然与非尖峰网络非常相似，在时间方面仍然很大程度上未被利用。该项目的动机是，SNN效率指标(识别精度、硬件功率、能量和区域效率)中目前的差距将通过对时间域中的尖峰信息编码进行变革性的重新思考，以及探索适用于此类替代尖峰编码方案的纳米电子设备来弥合，这些方案利用其固有的随机物理来进行类似大脑的概率推理。结合这两种观点，在时间域编码信息的随机仿生硬件有可能实现新一代受大脑启发的计算平台，该平台利用计算神经科学的两个互补见解--信息如何在大脑中编码和计算如何在大脑中发生--的相关优势。该项目的跨层性质，从设备设计、电路、系统和算法探索，将成为一个理想的平台，使研究生和本科生，包括女性和代表性不足的少数族裔社区，能够进行跨学科培训和教育。研究涉及硬件和软件交叉的变革性研究议程，开发从设备到算法和潜在学习方法的跨层设计工作。该项目跨越以下主要领域的交叉探索：(I)推力1研究自旋设备物理，并提出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-电路原语。(2)推力2考虑了在随机磁性装置中固有地利用信息的时间编码的系统开发。(3)由推力1和推力2产生的硬件-算法联合设计将在推力3中达到高潮，该推力将考虑在基准应用程序套件中进行大规模系统级模拟和性能评估。这样的端到端框架可以将适当的神经形态计算范例与底层硬件的内在操作相融合，以提高其性能(分类精度)和复杂机器学习任务的效率。该项目的成功完成为通过追求跨越设备、电路、系统、机器学习和计算神经科学的多学科视角来实现机器智能的大脑规模效率方面的重大飞跃奠定了基础。该奖项反映了NSF的法定使命，并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，被认为值得支持。

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.