Statistical Analysis of Synaptic Weights in Spiking Neural Network Trained on the DVS128 Gesture Dataset

The application of compression techniques, such as quantization and pruning, to reduce neural network weight size and complexity is gaining momentum due to the significant savings they provide in both hardware and software resources, particularly important for edge devices with limited capabilities. To fully leverage the advantages of these techniques, their design should account for the amplitude dynamics and the probability distribution of the parameters to be compressed. Therefore, it is essential to analyze the statistical characteristics of these parameters prior to compression. Although this type of analysis has been extensively conducted in the context of Artificial Neural Networks (ANNs), it has received limited attention in the case of Spiking Neural Networks (SNNs). Motivated by this gap, this paper examines the statistical properties of the weights of an SNN trained on the DVS128 Gesture dataset. The results of this analysis are valuable not only for enabling a well-founded selection and effective design of appropriate quantization models, but also for supporting the efficient application of pruning techniques, which could benefit from using synaptic weight histograms as valuable guiding information.