A distributional RL framework with information bottleneck achieves 37-41% better DRAM equalizer performance than baselines, with 51x speedup and uncertainty quantification via Monte Carlo dropout and CVaR.
Reinforcement-learning-based signal integrity optimization and analysis of a scalable 3-d x-point array structure,
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Distributional Reinforcement Learning with Information Bottleneck for Uncertainty-Aware DRAM Equalization
A distributional RL framework with information bottleneck achieves 37-41% better DRAM equalizer performance than baselines, with 51x speedup and uncertainty quantification via Monte Carlo dropout and CVaR.