TIM: An Efficient Temporal Interaction Module for Spiking Transformer

arXiv:2401.11687v3 Announce Type: replace-cross
Abstract: Spiking Neural Networks (SNNs), as the third generation of neural networks, have gained prominence for their biological plausibility and computational efficiency, especially in processing diverse datasets. The integration of attention mechanisms, inspired by advancements in neural network architectures, has led to the development of Spiking Transformers. These have shown promise in enhancing SNNs' capabilities, particularly in the realms of both static and neuromorphic datasets. Despite their progress, a discernible gap exists in these systems, specifically …

arxiv cs.cv cs.lg cs.ne temporal transformer type