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.
Sequential policy network-based optimal passive equalizer design for an arbitrary channel of high bandwidth memory using advantage actor critic,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.