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DOE OSTI · 2281801

Real-Time Neuromorphic Processing of Spatiotemporal Data for Scientific Discovery

Li, Peng
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Abstract

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.

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97 MATHEMATICS AND COMPUTINGhardware accelerationnetwork architecturesneuromorphic computingspiking neural networkstraining algorithms

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Li, Peng. 2024-01-18. Real-Time Neuromorphic Processing of Spatiotemporal Data for Scientific Discovery. https://doi.org/10.2172/2281801

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