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

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.