Max-Pooling PD: A Machine Learning-Based Timing Recovery Method

This paper presents a max-pooling phase detector (PD) designed to significantly reduce power consumption and area compared to conventional baud-rate detectors such as the Mueller-Mueller (MMPD) and Sign-Sign MMPD (SS-MMPD). The PD was originally developed using a convolutional neural network (CNN) model and trained within a machine-learning (ML) framework optimized for the provided training dataset. The design method was completed using a minimum mean square error (MMSE) approach to address discrepancies between training conditions and real-world operation, enabling traditional adaptation methods to enhance the performance and robustness of the PD. Synthesized in the GF 22-nm FD-SOI process, the max-pooling PD consumes 40% less power and occupies 60% less area than a standard SS-MMPD.