具有信息瓶颈的分布式强化学习用于不确定性感知的DRAM均衡
Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization
AI总结：
本文提出了一种结合信息瓶颈与条件价值-at-风险优化的分布式强化学习框架，用于具有不确定性感知的DRAM均衡优化，实现了显著的性能提升和可靠性改进。
摘要：
在多吉比特数据速率下运行的高速内存系统中，均衡器参数优化对信号完整性至关重要。然而，现有方法存在计算成本高、眼图评估昂贵、仅优化预期而非最坏情况性能以及缺乏对部署决策的不确定性量化等问题。在本文中，我们提出了一种整合信息瓶颈潜在表示与条件价值-at-风险优化的分布式风险敏感强化学习框架。我们引入了速率-失真最优信号压缩，其速度比眼图快51倍，同时通过蒙特卡洛滴落量化epistemic不确定性。分布式强化学习与分位数回归使显式最坏情况优化成为可能，而PAC-Bayesian正则化则保证了泛化界限。在八个内存单元的240万个波形上的实验验证显示，4级和8级均衡器配置的平均改进分别为37.1%和41.5%，最坏情况保证分别为33.8%和38.2%，比Q-learning基线分别提高了80.7%和89.1%。该框架实现了62.5%的高可靠性分类，消除了大多数配置的手动验证。这些结果表明，所提出的框架为大规模生产均衡器优化提供了实用的解决方案，具有认证的最坏情况保证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
