Freely scalable and reconfigurable optical hardware for deep learning

Liane Bernstein
Liane Bernstein
Alexander Sludds
Alexander Sludds
Joel Emer
Joel Emer

Scientific reports, pp. 31442021.

Cited by: 0|Bibtex|Views22|DOI:https://doi.org/10.1038/s41598-021-82543-3
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Other Links: arxiv.org|pubmed.ncbi.nlm.nih.gov

Abstract:

As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clockin...More

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