This paper proposes a novel three-layer parallel deep neural network (DNN) equalizer that achieves superior performance and resource efficiency compared to conventional decision feedback equalizers (DFEs). Evaluated in a 56 GBd PAM4 transmission system over a 306 mm channel, the proposed equalizer reduces the bit error rate (BER) by an order of magnitude relative to a first-order DFE while matching the performance of a second-order DFE, all with comparable computational complexity. Notably, the parallel architecture accelerates equalization processing without compromising BER performance and fundamentally eliminates the inherent feedback delay element in DFE structures, thereby overcoming a key timing bottleneck of traditional equalizers. Simulation results demonstrate that under similar power constraints, the DNN equalizer achieves higher throughput than its second-order DFE counterpart, highlighting its significant advantages in resource utilization efficiency.
Du et al. (Fri,) studied this question.