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

TL;DR

This paper introduces a distributional reinforcement learning framework with information bottleneck and uncertainty quantification, significantly improving equalizer optimization speed and reliability in high-speed memory systems.

Contribution

It presents a novel risk-sensitive RL approach combining information bottleneck, quantile regression, and PAC-Bayesian regularization for efficient, uncertainty-aware equalizer parameter tuning.

Findings

Achieved 51x speedup over eye diagram evaluation.

Improved equalizer performance by over 37% on average.

Provided worst-case guarantees up to 38.2% improvement.

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.