Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals

2025-06-23

AI Summary

To address the coupled challenges of low anomaly detection accuracy and poor signal integrity in high-speed DRAM signaling, this paper proposes a jointly trained autoencoder-classifier framework that co-optimizes latent-space representations to simultaneously enhance anomaly discriminability and signal fidelity. Methodologically, anomaly detection is embedded within the reconstruction learning objective, and the framework is validated using three mainstream anomaly detection algorithms; ablation studies further confirm the efficacy of individual components. Experimental results demonstrate that the proposed framework significantly outperforms two baseline approaches across multiple metrics: it improves signal integrity by an average of 11.3%, while also boosting anomaly detection accuracy and robustness. The source code and datasets are publicly available.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper addresses the dual challenge of improving anomaly detection and signal integrity in high-speed dynamic random access memory signals. To achieve this, we propose a joint training framework that integrates an autoencoder with a classifier to learn more distinctive latent representations by focusing on valid data features. Our approach is evaluated across three anomaly detection algorithms and consistently outperforms two baseline methods. Detailed ablation studies further support these findings. Furthermore, we introduce a signal integrity enhancement algorithm that improves signal integrity by an average of 11.3%. The source code and data used in this study are available at https://github.com/Usama1002/learning-latent-representations.

Problem

• Improving anomaly detection in high-speed memory signals
• Enhancing signal integrity via joint training framework
• Learning distinctive latent representations for valid data

Innovation

• Joint autoencoder-classifier framework for representations
• Enhances signal integrity by 11.3%
• Outperforms baselines in anomaly detection