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

• URL: http://arxiv.org/abs/2506.18288v1
• Date: Mon, 23 Jun 2025 04:48:22 GMT
• Title: Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals
• Authors: Muhammad Usama, Hee-Deok Jang, Soham Shanbhag, Yoo-Chang Sung, Seung-Jun Bae, Dong Eui Chang,
• Abstract summary: This paper addresses the dual challenge of improving anomaly detection and signal integrity in high-speed dynamic random access memory signals.<n>We propose a joint training framework that integrates an autoencoder with a classifier to learn more distinctive latent representations.<n>We introduce a signal integrity enhancement algorithm that improves signal integrity by an average of 11.3%.
• Score: 3.0017241250121387
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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