Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization
Equalizer parameter optimization is critical for signal integrity in high-speed memory systems operating at multi-gigabit data rates. However, existing methods suffer from computationally expensive eye diagram evaluation, optimization of expected rather than worst-case performance, and absence of uncertainty quantification for deployment decisions. In this paper, we propose a distributional risk-sensitive reinforcement learning framework integrating Information Bottleneck latent representations with Conditional Value-at-Risk optimization. We introduce rate-distortion optimal signal compression achieving 51 times speedup over eye diagrams while quantifying epistemic uncertainty through Mon...
| Authors | Muhammad Usama, Dong Eui Chang |
|---|---|
| Primary category | cs.LG |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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