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Xingting Yao

2 accepted papers

2024

Towards Efficient Spiking Transformer: a Token Sparsification Framework for Training and Inference Acceleration

ICML 2024poster

Nowadays Spiking Transformers have exhibited remarkable performance close to Artificial Neural Networks (ANNs), while enjoying the inherent energy-efficiency of Spiking Neural Networks (SNNs). However, training Spiking Transformers on GPUs is considerably more time-consuming compared to the ANN coun…

Cited by 0SourcePDFScholar
2022

GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

NeurIPS 2022accept

Spiking Neural Networks (SNNs) have been studied over decades to incorporate their biological plausibility and leverage their promising energy efficiency. Throughout existing SNNs, the leaky integrate-and-fire (LIF) model is commonly adopted to formulate the spiking neuron and evolves into numerous…