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Spiking Neural Operators for Scientific Machine Learning by Kahana, Adar; Zhang, Qian; Gleyzer, Leonard; Karniadakis, George Em is a scholarly article available to read on EtoBox.

What is Spiking Neural Operators for Scientific Machine Learning about?

The main computational task of Scientific Machine Learning (SciML) is function regression, required both for inputs as well as outputs of a simulation. Physics-Informed Neural Networks (PINNs) and neural operators (such as DeepONet) have been very effective in solving Partial Differential Equations (PDEs), but they tax computational resources heavily and cannot be readily adopted for edge computing. Here, we address this issue by considering Spiking Neural Networks (SNNs), which have shown promise in reducing energy consumption by two orders of magnitude or more. We present a SNN-based method to perform regression, which has been a challenge due to the inherent difficulty in representing a function's input domain and continuous output values as spikes. We first propose a new method for encoding continuous values into spikes based on a triangular matrix in space and time, and demonstrate its better performance compared to the existing methods. Next, we demonstrate that using a simple SNN architecture consisting of Leaky Integrate and Fire (LIF) activation and two dense layers, we can achieve relatively accurate function regression results. Moreover, we can replace the LIF with a tra

Author

Kahana, Adar; Zhang, Qian; Gleyzer, Leonard; Karniadakis, George Em

Published

2022

Language

EN