Real-Time Neuromorphic Processing of Spatiotemporal Data for Scientific Discovery

Technical Report · Thu Jan 18 04:00:00 EST 2024

DOI:https://doi.org/10.2172/2281801· OSTI ID:2281801

Li, Peng ^[1]

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1. Univ. of California, Santa Barbara, CA (United States); University of California, Santa Barbara

Spiking Neural Networks (SNNs) are brain-inspired computing models incorporating unique temporal dynamics and event-driven processing. Rich dynamics in both space and time offer great challenges and opportunities for efficient processing of sparse spatiotemporal data compared with conventional artificial neural networks (ANNs). Under this context, the goal of this project is to develop spiking neural network based neuromorphic computing to enable energy-efficient real-time learning and processing of spatiotemporal data. This report summarizes the key results on network architecture design, training methods, and SNN hardware acceleration achieved under this project, demonstrating the promise of spiking neural networks.

• Research Organization: Univ. of California, Santa Barbara, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
• DOE Contract Number: SC0021319
• OSTI ID: 2281801
• Report Number(s): DOE-UCSB--SC0021319-Final
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
hardware acceleration
network architectures
neuromorphic computing
spiking neural networks
training algorithms