To the best of our knowledge, for the first time, we propose adaptive moment estimation (Adam) algorithm based on batch gradient descent (BGD) to design a time-domain equalizer (TDE) for PAM-based optical interconnects. Adam algorithm has been widely applied in the fields of artificial intelligence. For TDE, BGD-based Adam algorithm can obtain globally optimal tap coefficients without being trapped in locally optimal tap coefficients. Therefore, fast and stable convergence can be achieved by BGD-based Adam algorithm with low mean square error. Meanwhile, BGD-based Adam algorithm is implemented by parallel processing, which is more efficient than conventional serial algorithms, such as least mean square and recursive least square algorithms. The experimental results demonstrate that BGD-based Adam feed-forward equalizer works well in 120-Gbit/s PAM8 optical interconnects. In conclusion, BGD-based Adam algorithm shows great potential for converging the tap coefficients of TDE in future optical interconnects.
翻译:就我们所知,我们第一次提议基于批量梯度下降(BGD)的适应性瞬间估计(Adam)算法,为基于PAM的光学连接设计一个时间域平衡器(TDE) 。 Adam 算法已经在人工智能领域被广泛应用。 对于TDE, BGD的亚当算法可以在不困于当地最佳自控系数的情况下获得全球最佳自控系数。 因此,基于BGD的亚当算法可以实现快速和稳定的趋同。 与此同时,基于BGD 的亚当算法是通过平行处理实现的,比常规的序列算法效率更高,例如最小的平方和递归性最小的平方算法。 实验结果显示,基于BGD的Adam feed-ward 平衡器在120-Gbit/s PAM8光学连接中运作良好。 最后,基于BGD Adam 的算法显示在未来光学互联中极有可能将TDE的自控系数相融合。