  Spiking neural networks (SNNs) that mimic information transmission in the
brain can energy-efficiently process spatio-temporal information through
discrete and sparse spikes, thereby receiving considerable attention. To
improve accuracy and energy efficiency of SNNs, most previous studies have
focused solely on training methods, and the effect of architecture has rarely
been studied. We investigate the design choices used in the previous studies in
terms of the accuracy and number of spikes and figure out that they are not
best-suited for SNNs. To further improve the accuracy and reduce the spikes
generated by SNNs, we propose a spike-aware neural architecture search
framework called AutoSNN. We define a search space consisting of architectures
without undesirable design choices. To enable the spike-aware architecture
search, we introduce a fitness that considers both the accuracy and number of
spikes. AutoSNN successfully searches for SNN architectures that outperform
hand-crafted SNNs in accuracy and energy efficiency. We thoroughly demonstrate
the effectiveness of AutoSNN on various datasets including neuromorphic
datasets.
