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

arXiv:2603.04768v1 Announce Type: new 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 memo

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Muhammad Usama, Dong Eui Chang · · 1 min read · 3 views
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arXiv:2603.04768v1 Announce Type: new 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.

Executive Summary

This article proposes a novel distributional reinforcement learning framework for equalizer parameter optimization in high-speed memory systems. The framework integrates Information Bottleneck latent representations with Conditional Value-at-Risk optimization, achieving significant improvements in worst-case performance and uncertainty quantification. Experimental validation demonstrates the framework's effectiveness, with mean improvements of 37.1% and 41.5% for 4-tap and 8-tap equalizer configurations, and worst-case guarantees of 33.8% and 38.2%.

Key Points

  • ▸ Distributional reinforcement learning for equalizer parameter optimization
  • ▸ Integration of Information Bottleneck latent representations with Conditional Value-at-Risk optimization
  • ▸ Uncertainty quantification through Monte Carlo dropout and PAC-Bayesian regularization

Merits

Improved Performance

The proposed framework achieves significant improvements in worst-case performance and uncertainty quantification, making it a practical solution for production-scale equalizer optimization.

Efficient Signal Compression

The framework introduces rate-distortion optimal signal compression, achieving a 51 times speedup over eye diagrams.

Demerits

Computational Complexity

The framework's computational complexity may be a limitation, particularly for large-scale memory systems.

Limited Generalizability

The framework's performance may not generalize to other types of memory systems or operating conditions.

Expert Commentary

The proposed framework represents a significant advancement in the field of distributional reinforcement learning, particularly in the context of high-speed memory systems. The integration of Information Bottleneck latent representations with Conditional Value-at-Risk optimization enables efficient and effective optimization of equalizer parameters, while the use of uncertainty quantification through Monte Carlo dropout and PAC-Bayesian regularization provides a robust and reliable framework for decision-making. The experimental validation demonstrates the framework's effectiveness, and its potential for practical application is substantial.

Recommendations

  • ✓ Further research should be conducted to explore the framework's generalizability to other types of memory systems and operating conditions.
  • ✓ The framework's computational complexity should be optimized to enable efficient deployment in large-scale memory systems.

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