Large-Scale Integrated Photonics for Energy-Efficient AI Hardware

The convergence of deep learning models and the availability of large datasets has spurred significant interest in developing new hardware that can run AI algorithms more energy-efficiently. At Hewlett Packard Labs, we've developed an energy-efficient silicon photonics platform, a foundational...

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Bibliographic Details
Published inDigest of the LEOS Summer Topical Meetings pp. 1 - 2
Main Authors Tossoun, Bassem, Liang, Di, Xiao, Xian, Cheung, Stanley, Singaraju, Prerana, Srinivasan, Sudharsanan, Descos, Antoine, Hu, Yingtao, Baek, Jongseo, London, Yanir, Yuan, Yuan, Peng, Yiwei, Van Vaerenbergh, Thomas, Kurzveil, Geza, Fiorentino, Marco, Beausoleil, Raymond G.
Format Conference Proceeding
LanguageEnglish
Published IEEE 15.07.2024
Subjects
Artificial intelligence
Convergence
Deep learning
Energy efficiency
Hardware
neuromorphic computing
Next generation networking
photonic computing
Silicon photonics
Online AccessGet full text
ISSN2376-8614
DOI10.1109/SUM60964.2024.10614557

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Summary:The convergence of deep learning models and the availability of large datasets has spurred significant interest in developing new hardware that can run AI algorithms more energy-efficiently. At Hewlett Packard Labs, we've developed an energy-efficient silicon photonics platform, a foundational technology for next-generation AI hardware.
ISSN:2376-8614
DOI:10.1109/SUM60964.2024.10614557