Deep Reinforcement Learning-Based DRAM Equalizer Parameter Optimization Using Latent Representations

TL;DR

This paper presents a novel data-driven reinforcement learning framework using latent signal representations to optimize DRAM equalizer parameters efficiently, improving signal integrity and outperforming existing methods.

Contribution

It introduces a new latent signal integrity metric and a model-free reinforcement learning approach for DRAM equalizer optimization, demonstrating superior performance and efficiency.

Findings

Achieved 42.7% improvement in eye-opening window area for certain equalizer structures.

Demonstrated 36.8% improvement for Decision Feedback Equalizer-only configurations.

Outperformed existing techniques in robustness and computational efficiency.

Abstract

Equalizer parameter optimization for signal integrity in high-speed Dynamic Random Access Memory systems is crucial but often computationally demanding or model-reliant. This paper introduces a data-driven framework employing learned latent signal representations for efficient signal integrity evaluation, coupled with a model-free Advantage Actor-Critic reinforcement learning agent for parameter optimization. The latent representation captures vital signal integrity features, offering a fast alternative to direct eye diagram analysis during optimization, while the reinforcement learning agent derives optimal equalizer settings without explicit system models. Applied to industry-standard Dynamic Random Access Memory waveforms, the method achieved significant eye-opening window area improvements: 42.7\% for cascaded Continuous-Time Linear Equalizer and Decision Feedback Equalizer structures, and 36.8\% for Decision Feedback Equalizer-only configurations. These results demonstrate superior performance, computational efficiency, and robust generalization across diverse Dynamic Random Access Memory units compared to existing techniques. Core contributions include an efficient latent signal integrity metric for optimization, a robust model-free reinforcement learning strategy, and validated superior performance for complex equalizer architectures.