  Integrated optoelectronics is emerging as a promising platform of neural
network accelerator, which affords efficient in-memory computing and high
bandwidth interconnectivity. The inherent optoelectronic noises, however, make
the photonic systems error-prone in practice. It is thus imperative to devise
strategies to mitigate and, if possible, harness noises in photonic computing
systems. Here, we demonstrate a photonic generative network as a part of a
generative adversarial network (GAN). This network is implemented with a
photonic core consisting of an array of four programable phase-change memory
cells to perform 4-elements vector-vector dot multiplication. We demonstrate
that the GAN can generate a handwritten number ("7") in experiments and full
ten digits in simulation. We realize an optical random number generator derived
from the amplified spontaneous emission noise, apply noise-aware training by
injecting additional noise and demonstrate the network's resilience to hardware
non-idealities. Our results suggest the resilience and potential of more
complex photonic generative networks based on large-scale, realistic photonic
hardware.
