4 papers · 1 filter
Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization
Muhammad Usama, Dong Eui Chang
Equalizer parameter optimization is critical for signal integrity in high-speed memory systems operating at multi-gigabit data rates. However, existing methods suffer from computat…
Deep Reinforcement Learning-Based DRAM Equalizer Parameter Optimization Using Latent Representations
Muhammad Usama, Dong Eui Chang
Equalizer parameter optimization for signal integrity in high-speed Dynamic Random Access Memory systems is crucial but often computationally demanding or model-reliant. This paper…
Learning High-Quality Latent Representations for Anomaly Detection and Signal Integrity Enhancement in High-Speed Signals
Muhammad Usama, Hee-Deok Jang, Soham Shanbhag +3
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…
Towards Robust Neural Networks with Lipschitz Continuity
Muhammad Usama, Dong Eui Chang
Deep neural networks have shown remarkable performance across a wide range of vision-based tasks, particularly due to the availability of large-scale datasets for training and bett…