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Nov 18, 2023 · We investigate the unique synergy of SNNs with LTH and design two novel spiking winning tickets to push the boundaries of sparsity within SNNs.
Pursing the Sparse Limitation of Spiking Deep Learning Structures · Effective Surrogate Gradient Learning With High-Order Information Bottleneck for Spike ...
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Pursing the Sparse Limitation of Spiking Deep Learning Structures ... It posits that within dense neural networks, there exist winning tickets or subnetworks that ...
Pursing the Sparse Limitation of Spiking Deep Learning Structures. ... Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, are garnering increased ...
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain.
Pursing the Sparse Limitation of Spiking Deep Learning Structures, Hao Cheng et.al. 2311.12060v1, null. 2023-11-20, Asynchronous Bioplausible Neuron for Spiking ...
Nov 18, 2023 · Pursing the Sparse Limitation of Spiking Deep Learning Structures ... spines improves learning of sparse spiking neural networks. In ICML ...
This work trains low-latency SNN through knowledge distillation with Kullback-Leibler divergence (KL divergence) and performs the fastest inference without ...
Inspired by this, this paper explores the spiking-based LTs (SLTs), examining their unique properties and potential for extreme efficiency. Then, two ...
In this review, we address the opportunities that deep spiking networks offer and investigate in detail the challenges associated with training SNNs.
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