Fixed-Point Analysis and FPGA Implementation of Deep Neural Network Based Equalizers for High-Speed PON

Abstract

Adeep neural network based equalizer is proposed to mitigate the intersymbol interference observed in next generation high speed passive optical network (PON) links. The DNN based equalizer is shown to outperform the best known conventional equalizer, the maximum likelihood sequence estimator (MLSE) both in back-back and through fiber experiments. To reduce the hardware complexity of DNN based equalizer for PON systems, we investigate the use of embedded parallelization within a DNN structure having multiple symbol outputs from one DNN. We further investigate using a classification output stage with cross entropy cost to perform joint decision on multiple symbol outputs and demonstrated that the sensitivity gain of such scheme over regression output. To understand the complexity of hardware implementation, the fixed-point DNN based equalizers are developed and implemented in FPGA. The impact of fixed-point resolution on the receiver sensitivity and hardware resource utilization in FPGA implementation is analyzed and reported in detail. We show that a reduction of over 40% in LUTs (look up table) utilization is possible by reducing the DNN's weight resolution from 8-bit to 4-bit while incurring a small penalty in receiver sensitivity.