  Singular-value-decomposition-based coherent integrated photonic neural
networks (SC-IPNNs) have a large footprint, suffer from high static power
consumption for training and inference, and cannot be pruned using conventional
DNN pruning techniques. We leverage the lottery ticket hypothesis to propose
the first hardware-aware pruning method for SC-IPNNs that alleviates these
challenges by minimizing the number of weight parameters. We prune a
multi-layer perceptron-based SC-IPNN and show that up to 89% of the phase
angles, which correspond to weight parameters in SC-IPNNs, can be pruned with a
negligible accuracy loss (smaller than 5%) while reducing the static power
consumption by up to 86%.
