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Photonic neural networks are brain-inspired information processing technologyusing photons instead of electrons to perform artificial intelligence (AI)tasks. However, existing architectures are designed for a single task but failto multiplex different tasks in parallel within a single monolithic system dueto the task competition that deteriorates the model performance. This paperproposes a novel optical multi-task learning system by designingmulti-wavelength diffractive deep neural networks (D2NNs) with the jointoptimization method. By encoding multi-task inputs into multi-wavelengthchannels, the system can increase the computing throughput and significantlyalle-viate the competition to perform multiple tasks in parallel with highaccuracy. We design the two-task and four-task D2NNs with two and four spectralchannels, respectively, for classifying different inputs from MNIST, FMNIST,KMNIST, and EMNIST databases. The numerical evaluations demonstrate that, underthe same network size, mul-ti-wavelength D2NNs achieve significantly higherclassification accuracies for multi-task learning than single-wavelength D2NNs.Furthermore, by increasing the network size, the multi-wavelength D2NNs forsimultaneously performing multiple tasks achieve comparable classificationaccuracies with respect to the individual training of multiplesingle-wavelength D2NNs to perform tasks separately. Our work paves the way fordeveloping the wave-length-division multiplexing technology to achievehigh-throughput neuromorphic photonic computing and more general AI systems toperform multiple tasks in parallel.

Optical multi-task learning using multi-wavelength diffractive deep neural networks