Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization

• URL: http://arxiv.org/abs/2603.04768v1
• Date: Thu, 05 Mar 2026 03:34:25 GMT
• Title: Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization
• Authors: Muhammad Usama, Dong Eui Chang,
• Abstract summary: We propose a distributional risk-sensitive reinforcement learning framework integrating Information Bottleneck latent representations with Conditional Value-at-Risk optimization.<n>We introduce rate-distortion optimal signal compression achieving 51 times speedup over eye diagrams.<n>We show that the proposed framework provides a practical solution for production-scale equalizer optimization with certified worst-case guarantees.
• Score: 8.695939803795499
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 Monte Carlo dropout. Distributional reinforcement learning with quantile regression enables explicit worst-case optimization, while PAC-Bayesian regularization certifies generalization bounds. Experimental validation on 2.4 million waveforms from eight memory units demonstrated mean improvements of 37.1\% and 41.5\% for 4-tap and 8-tap equalizer configurations with worst-case guarantees of 33.8\% and 38.2\%, representing 80.7\% and 89.1\% improvements over Q-learning baselines. The framework achieved 62.5\% high-reliability classification eliminating manual validation for most configurations. These results suggest the proposed framework provides a practical solution for production-scale equalizer optimization with certified worst-case guarantees.

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