  Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to
represent information and process them in an asynchronous event-driven manner,
offering an energy-efficient paradigm for the next generation of machine
intelligence. However, the current focus within the SNN community prioritizes
accuracy optimization through the development of large-scale models, limiting
their viability in resource-constrained and low-power edge devices. To address
this challenge, we introduce a lightweight and hardware-friendly Quantized SNN
(Q-SNN) that applies quantization to both synaptic weights and membrane
potentials. By significantly compressing these two key elements, the proposed
Q-SNNs substantially reduce both memory usage and computational complexity.
Moreover, to prevent the performance degradation caused by this compression, we
present a new Weight-Spike Dual Regulation (WS-DR) method inspired by
information entropy theory. Experimental evaluations on various datasets,
including static and neuromorphic, demonstrate that our Q-SNNs outperform
existing methods in terms of both model size and accuracy. These
state-of-the-art results in efficiency and efficacy suggest that the proposed
method can significantly improve edge intelligent computing.
