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A noise based novel strategy for faster SNN training. (arXiv:2211.05453v1 [cs.NE])
Nov. 11, 2022, 2:11 a.m. | Chunming Jiang, Yilei Zhang
cs.LG updates on arXiv.org arxiv.org
Spiking neural networks (SNNs) are receiving increasing attention due to
their low power consumption and strong bio-plausibility. Optimization of SNNs
is a challenging task. Two main methods, artificial neural network (ANN)-to-SNN
conversion and spike-based backpropagation (BP), both have their advantages and
limitations. For ANN-to-SNN conversion, it requires a long inference time to
approximate the accuracy of ANN, thus diminishing the benefits of SNN. With
spike-based BP, training high-precision SNNs typically consumes dozens of times
more computational resources and time than …
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