Spiking Neural Networks (SNNs) is a practical approach toward more
data-efficient deep learning by simulating neurons leverage on temporal
information. In this paper, we propose the Temporal-Channel Joint Attention
(TCJA) architectural unit, an efficient SNN technique that depends on attention
mechanisms, by effectively enforcing the relevance of spike sequence along both
spatial and temporal dimensions. Our essential technical contribution lies on:
1) compressing the spike stream into an average matrix by employing the squeeze
operation, then using two local attention mechanisms with an efficient 1-D
convolution to establish temporal-wise and channel-wise relations for feature
extraction in a flexible fashion. 2) utilizing the Cross Convolutional Fusion
(CCF) layer for modeling inter-dependencies between temporal and channel scope,
which breaks the independence of the two dimensions and realizes the
interaction between features. By virtue of jointly exploring and recalibrating
data stream, our method outperforms the state-of-the-art (SOTA) by up to 15.7%
in terms of top-1 classification accuracy on all tested mainstream static and
neuromorphic datasets, including Fashion-MNIST, CIFAR10-DVS, N-Caltech 101, and
DVS128 Gesture.

本论文提出了一种基于注意力机制的高效脉冲神经网络技术 —— 时间 - 通道联合注意 (TCJA) 架构单元，通过在空间和时间维度上有效地执行脉冲序列的相关性来实现更为数据有效的深度学习，运用压缩脉冲流，本地注意机制，1-D 卷积，交叉卷积融合等技术可以灵活地提取特征，同时，该方法在所有测试的静态和神经形态学数据集上的分类准确性 (SOTA) 最高可达 15.7%。