High-Speed I/O Equalizer Optimization Based on Deep Reinforcement Learning

Abstract

In this work, we propose a signal integrity and power efficiency optimization framework for high-speed input/output equalizer systems using deep reinforcement learning based on a deep double Q-network. Although equalizer techniques have long existed, few studies have systematically optimized the feed-forward equalizer, decision feedback equalizer, and continuous-time linear equalizer together, including the high-speed channel properties. This framework determines the parameters of the equalizer architecture to optimize performance metrics such as eye height, eye width, and power consumption. It offers three key advantages: a framework with a scalable deep neural network to efficiently model complex design challenges; a future-state technique that mitigates design-space expansion with a minimum number of output neurons; and simultaneous evaluation of power efficiency, unachievable with conventional methods. As a proof of concept, we present the optimization of equalizer architecture using the proposed approach across different high-speed channels, including a backplane and an interference-tolerant channel. Experimental results demonstrate that our approach improves signal and power performance significantly compared to those from conventional methods (e.g., minimum-mean squared error and least-mean squared) and converges much faster than genetic, particle swarm, and Bayesian optimization algorithms.