Training Full Spike Neural Networks via Auxiliary Accumulation Pathway

Understand

Due to the binary spike signals making converting the traditional high-power multiply-accumulation (MAC) into a low-power accumulation (AC) available, the brain-inspired Spiking Neural Networks (SNNs) are gaining more and more attention.

• However, the binary spike propagation of the Full-Spike Neural Networks (FSNN) with limited time steps is prone to significant information loss.
• To improve performance, several state-of-the-art SNN models trained from scratch inevitably bring many non-spike operations.
• The non-spike operations cause additional computational consumption and may not be deployed on some neuromorphic hardware where only spike operation is allowed.