Energy-efficient Knowledge Distillation for Spiking Neural Networks

Understand

Spiking neural networks (SNNs) have been gaining interest as energy-efficient alternatives of conventional artificial neural networks (ANNs) due to their event-driven computation.

• Considering the future deployment of SNN models to constrained neuromorphic devices, many studies have applied techniques originally used for ANN model compression, such as network quantization, pruning, and knowledge distillation, to SNNs.
• Among them, existing works on knowledge distillation reported accuracy improvements of student SNN model.
• However, analysis on energy efficiency, which is also an important feature of SNN, was absent.