SEENN: Towards Temporal Spiking Early-Exit Neural Networks

Understand

Spiking Neural Networks (SNNs) have recently become more popular as a biologically plausible substitute for traditional Artificial Neural Networks (ANNs).

• SNNs are cost-efficient and deployment-friendly because they process input in both spatial and temporal manner using binary spikes.
• However, we observe that the information capacity in SNNs is affected by the number of timesteps, leading to an accuracy-efficiency tradeoff.
• In this work, we study a fine-grained adjustment of the number of timesteps in SNNs.