Harnessing optoelectronic noises in a photonic generative network.
Sci Adv
; 8(3): eabm2956, 2022 Jan 21.
Article
em En
| MEDLINE
| ID: mdl-35061531
ABSTRACT
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 programable phase-change memory cells to perform four-element vector-vector dot multiplication. The GAN can generate a handwritten number ("7") in experiments and full 10 digits in simulation. We realize an optical random number generator, apply noise-aware training by injecting additional noise, and demonstrate the network's resilience to hardware nonidealities. Our results suggest the resilience and potential of more complex photonic generative networks based on large-scale, realistic photonic hardware.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Sci Adv
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos