Optimizations and investigations for transfer learning of iteratively pruned neural network equalizers for data center networking.

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In this work, for the first time to the best of our knowledge, we introduce the iterative pruning technique into the transfer learning (TL) of neural network equalizers (NNE) deployed in optical links with different length. For the purpose of time saving during the training period of NNE, TL migrates the NNE parameters which have been already trained on the source link to the newly-routed link (the target link), which has been proved to outperform the training initialized with the random state. Based on simulations, we proved that iterative pruning technique could further enhance the convergence speed during TL between the source and target links. Moreover, we quantitatively investigate the marginal effects of pruned threshold and pruned span on the convergence performance in various transmission distance scenarios. In addition, we observed a trade-off between performance stability and complexity of NNE, which requires to be optimized compromisingly by choosing an appropriate equalizer scale.