Neuromorphic AI processors for energy-efficient log analysis in edge infrastructure

The potential of neuromorphic processors for energy-efficient log analysis and performance monitoring in edge infrastructure is being investigated. The limitations of traditional von Neumann architectures in processing streaming data under resource constraints are analyzed. The expediency of using spike neural networks (SNN) to detect anomalies in logs at the device level is substantiated. The principles of functioning of neuromorphic chips are considered, including asynchronous event processing, low static power, and learning based on synapse plasticity. It is proved that neuromorphic systems are able to provide continuous monitoring without transferring raw data to the cloud, reducing delays and energy consumption. There is a shortage of research on the adaptation of neuromorphic processors to log analysis tasks in edge environments. The results indicate the prospects of this area for energy-critical applications.