Work

Memristor-Based Neuromorphic System for Unsupervised Online Learning and Network Anomaly Detection on Edge Devices

Year: 2025

Type: article

Abstract: An ultra-low-power, high-performance online learning and anomaly detection system has been developed for edge security applications. Designed to support personalized learning without relying on cloud data processing, the system employs sample-wise learning, eliminating the need for storing entire datasets for training. Built using memristor-based analog neuromorphic and in-memory computing techniques, the system integrates two unsupervised autoencoder neural networks—one utilizing optimized crossbar weights and the other performing real-time learning to detect novel intrusions. Threshold optimization and anomaly detection are achieved through a fully analog Euclidean Distance (ED) computation circuit, eliminating the need for floating-point processing units. The system demonstrates 87% anomaly detection accuracy, achieves a performance of 16.1 GOPS—774× faster than the ASUS Tinkerboard edge processor—and delivers an energy efficiency of 783 GOPS/W, consuming only 20.5 mW during anomaly detection.

Source: Information

DOI: 10.3390/info16030222

Authors: M. Shahanur Alam, Chris Yakopcic, Raqibul Hasan, Tarek M. Taha

Institutions: University of Dayton, International University of Sarajevo, Independent University, Bangladesh

Language: English

Topic: Advanced Memory and Neural Computing

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,930

Funder: National Science Foundation