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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| Published in | Digest of the LEOS Summer Topical Meetings pp. 1 - 2 |
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| Main Authors | , , , , , , , , , , , , , , , |
| Format | Conference Proceeding |
| Language | English |
| Published |
IEEE
15.07.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2376-8614 |
| DOI | 10.1109/SUM60964.2024.10614557 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Baek, Jongseo Xiao, Xian Descos, Antoine Fiorentino, Marco Liang, Di Singaraju, Prerana London, Yanir Beausoleil, Raymond G. Tossoun, Bassem Cheung, Stanley Yuan, Yuan Kurzveil, Geza Hu, Yingtao Van Vaerenbergh, Thomas Srinivasan, Sudharsanan Peng, Yiwei |
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| Snippet | The convergence of deep learning models and the availability of large datasets has spurred significant interest in developing new hardware that can run AI... |
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| SubjectTerms | Artificial intelligence Convergence Deep learning Energy efficiency Hardware neuromorphic computing Next generation networking photonic computing Silicon photonics |
| Title | Large-Scale Integrated Photonics for Energy-Efficient AI Hardware |
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