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

2025-07-03

AI Summary

Conventional equalizer parameter optimization in high-speed DRAM systems relies heavily on accurate channel models and incurs substantial computational overhead. Method: This paper proposes a model-free, end-to-end reinforcement learning framework that employs an autoencoder to learn compact, implicit signal representations as lightweight signal integrity metrics, coupled with the Advantage Actor-Critic (A2C) algorithm for global parameter search—eliminating the need for explicit system modeling. Contribution/Results: Evaluated on real-world DRAM waveform data, the method increases eye-opening area by 42.7% for cascaded equalization and by 36.8% for pure decision-feedback equalization (DFE), significantly outperforming conventional model-based approaches. It achieves high optimization efficiency and demonstrates strong cross-device generalizability, establishing a novel paradigm for automated signal integrity optimization in high-speed memory interfaces.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Optimization
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

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.

Problem

• Optimizes DRAM equalizer parameters for signal integrity efficiently
• Uses latent representations to speed up signal integrity evaluation
• Improves eye-opening window area without explicit system models

Innovation

• Learned latent signal representations for integrity evaluation
• Model-free Advantage Actor-Critic RL for optimization
• Efficient metric and robust strategy for equalizers