  Photonic neural networks are brain-inspired information processing technology
using photons instead of electrons to perform artificial intelligence (AI)
tasks. However, existing architectures are designed for a single task but fail
to multiplex different tasks in parallel within a single monolithic system due
to the task competition that deteriorates the model performance. This paper
proposes a novel optical multi-task learning system by designing
multi-wavelength diffractive deep neural networks (D2NNs) with the joint
optimization method. By encoding multi-task inputs into multi-wavelength
channels, the system can increase the computing throughput and significantly
alle-viate the competition to perform multiple tasks in parallel with high
accuracy. We design the two-task and four-task D2NNs with two and four spectral
channels, respectively, for classifying different inputs from MNIST, FMNIST,
KMNIST, and EMNIST databases. The numerical evaluations demonstrate that, under
the same network size, mul-ti-wavelength D2NNs achieve significantly higher
classification accuracies for multi-task learning than single-wavelength D2NNs.
Furthermore, by increasing the network size, the multi-wavelength D2NNs for
simultaneously performing multiple tasks achieve comparable classification
accuracies with respect to the individual training of multiple
single-wavelength D2NNs to perform tasks separately. Our work paves the way for
developing the wave-length-division multiplexing technology to achieve
high-throughput neuromorphic photonic computing and more general AI systems to
perform multiple tasks in parallel.
