CAREER: Bottom-Up Localized Online Learning with Spintronic Neuromorphic Networks

University Of Texas At Dallas, Richardson TX

Investigators

Abstract

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 increase 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.