Analog nanophotonic computing going practical: silicon photonic deep learning engines for tiled optical matrix multiplication with dynamic precision

Analog photonic computing comprises a promising candidate for accelerating the linear operations of deep neural networks (DNNs), since it provides ultrahigh bandwidth, low footprint and low power consumption computing capabilities. However, the confined photonic hardware size, along with the limited...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Giamougiannis George, Tsakyridis Apostolos, Moralis-Pegios Miltiadis, Pappas Christos, Kirtas Manos, Passalis Nikolaos, Lazovsky David, Tefas Anastasios, Pleros Nikos
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Wiley 2023-01-01
Sarja:	Nanophotonics
Aiheet:	analog computing deep learning dynamic precision inference photonic computing silicon photonics tiled matrix multiplication
Linkit:	https://doi.org/10.1515/nanoph-2022-0423
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