FPGA Implementation of Multi-Layer Machine Learning Equalizer with On-Chip Training

Abstract

We design and implement an adaptive machine learning equalizer that alternates multiple linear and nonlinear computational layers on an FPGA. On-chip training via gradient backpropagation is shown to allow for real-time adaptation to time-varying channel impairments.