Spikformer Architecture

Spikformer architecture integrates spiking neural networks (SNNs) with the transformer's self-attention mechanism to create energy-efficient deep learning models. Current research focuses on improving Spikformer's efficiency and accuracy through alternative attention mechanisms (e.g., Fourier or Wavelet transforms), architectural optimizations (e.g., pruning techniques, automated architecture search), and training strategies (e.g., self-supervised learning). This work is significant because it bridges biologically-inspired SNNs with the high performance of transformers, potentially leading to more efficient and powerful AI systems for resource-constrained applications.