SpikeLogBERT

SpikeLogBERT: Spiking transformer network for energy-efficient log parsing via knowledge distillation

SpikeLogBERT is a spiking neural network framework for log parsing that combines a spiking transformer architecture with knowledge distillation from a BERT teacher model. Evaluated on the HDFS dataset, it achieves a parsing accuracy of 0.99997 while reducing estimated theoretical energy consumption by up to 62.6% compared to standard ANN-based approaches. The work targets the inference efficiency bottleneck in neural log analysis pipelines used for anomaly detection and system monitoring.