QKFormer: Hierarchical Spiking Transformer using Q-K Attention

Spiking Transformers, which integrate Spiking Neural Networks (SNNs) with Transformer architectures, have attracted significant attention due to their potential for low energy consumption and high performance. However, there remains a substantial gap in performance between SNNs and Artificial Neural Networks (ANNs). To narrow this gap, we have developed QKFormer, a direct training spiking transformer with the following features: i) Linear complexity and high energy efficiency, the novel spike-form Q-K attention module efficiently models the token or channel attention through binary vectors and enables the construction of larger models.