  With the rapid development of deep learning, Deep Spiking Neural Networks
(DSNNs) have emerged as promising due to their unique spike event processing
and asynchronous computation. When deployed on neuromorphic chips, DSNNs offer
significant power advantages over Deep Artificial Neural Networks (DANNs) and
eliminate time and energy consuming multiplications due to the binary nature of
spikes (0 or 1). Additionally, DSNNs excel in processing temporal information,
making them potentially superior for handling temporal data compared to DANNs.
However, their deep network structure and numerous parameters result in high
computational costs and energy consumption, limiting real-life deployment. To
enhance DSNNs efficiency, researchers have adapted methods from DANNs, such as
pruning, quantization, and knowledge distillation, and developed specific
techniques like reducing spike firing and pruning time steps. While previous
surveys have covered DSNNs algorithms, hardware deployment, and general
overviews, focused research on DSNNs compression and efficiency has been
lacking. This survey addresses this gap by concentrating on efficient DSNNs and
their compression methods. It begins with an exploration of DSNNs' biological
background and computational units, highlighting differences from DANNs. It
then delves into various compression methods, including pruning, quantization,
knowledge distillation, and reducing spike firing, and concludes with
suggestions for future research directions.
