用脉冲神经网络实现低功耗日志解析，精度超99.9%且能耗降62.6%
SpikeLogBERT: Energy-Efficient Log Parsing Using Spiking Transformer Networks
- 采用脉冲变压器架构与BERT知识蒸馏，实现稀疏脉冲计算
- 在HDFS数据集上达到0.99997的解析准确率，能耗降低62.6%
- 适合部署在边缘设备的低功耗日志分析场景
日志解析是自动化日志分析的关键步骤，将原始系统日志转化为结构化事件模板，用于异常检测和系统监控等下游任务。现有方法包括基于规则、聚类及神经模型的方法，但神经模型通常依赖密集矩阵乘法，导致计算成本高、能耗大。本文提出SpikeLogBERT，一种基于脉冲神经网络的日志解析框架。该模型结合脉冲变压器架构与来自BERT教师模型的知识蒸馏，实现脉冲驱动计算的同时保持语义表示能力。通过稀疏脉冲激活和事件驱动处理，推理过程中的有效操作数显著减少。初步基准测试显示，在HDFS数据集上，SpikeLogBERT的解析准确率达到0.99997，相较于基于人工神经网络的模型，在标准45nm CMOS假设下理论能耗最多降低62.6%。
原文摘要 · Abstract (English)
Log parsing is a fundamental step in automated log analysis, transforming raw system logs into structured event templates for downstream tasks such as anomaly detection and system monitoring. Existing log parsing methods range from rule-based and clustering-based approaches to neural models that learn semantic representations from log messages. However, neural approaches typically rely on dense matrix multiplications, which can result in high computational cost and energy consumption. This paper presents SpikeLogBERT, a spiking neural network framework for energy-efficient log parsing. The proposed model integrates a spiking transformer architecture with knowledge distillation from a BERT teacher model, enabling spike-driven computation while preserving semantic representation capability. By leveraging sparse spike activations and event-driven processing, the number of active operations during inference can be significantly reduced. As an initial benchmark study, experiments on the HDFS dataset demonstrate that SpikeLogBERT outperforms ANN-based neural log parsing models with a parsing accuracy of 0.99997, while reducing estimated theoretical energy consumption by up to 62.6% under standard 45nm CMOS assumptions.
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