A Hybrid Pruning-Quantization Framework for Compact and Efficient Spiking Neural Networks

Spiking Neural Networks, or SNNs, are event driven and suitable for energy-efficient and high-throughput computing. Pruning unnecessary synaptic connections or neurons helps reduce model complexity, decreasing computation and memory requirements whilst preserving inference accuracy. Quantization is also effective in reducing model size by mapping high-precision weights to lower bit-width representations. In this paper, we propose three methods of reducing model size and complexity including pruning and quantization, as well as a hybrid pruning and quantization method. We aim to use these methods to significantly reduce the SNN model size while still maintaining high predictive performance. Our experimental results on oceanographic data indicate that the methods achieve competitive accuracy with a substantial decrease in model size.