Spiking Transformer

Spiking Transformers integrate the energy efficiency of spiking neural networks (SNNs) with the powerful attention mechanisms of Transformers, aiming to create high-performance, low-power deep learning models. Current research focuses on developing novel spiking self-attention mechanisms, improving training methods (including direct training and ANN-to-SNN conversion), and exploring various architectures like Spikformer and its variants for diverse applications such as image classification, video action recognition, and audio-visual processing. This research area is significant because it offers a potential pathway towards more energy-efficient and biologically plausible artificial intelligence, particularly relevant for resource-constrained devices and applications requiring real-time processing.