TY - GEN
T1 - Large-Scale Integrated Photonics for Energy-Efficient AI Hardware
AU - Tossoun, Bassem
AU - Liang, Di
AU - Xiao, Xian
AU - Cheung, Stanley
AU - Singaraju, Prerana
AU - Srinivasan, Sudharsanan
AU - Descos, Antoine
AU - Hu, Yingtao
AU - Baek, Jongseo
AU - London, Yanir
AU - Yuan, Yuan
AU - Peng, Yiwei
AU - Van Vaerenbergh, Thomas
AU - Kurzveil, Geza
AU - Fiorentino, Marco
AU - Beausoleil, Raymond G.
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
KW - neuromorphic computing
KW - photonic computing
KW - silicon photonics
UR - http://www.scopus.com/inward/record.url?scp=85201209204&partnerID=8YFLogxK
U2 - 10.1109/SUM60964.2024.10614557
DO - 10.1109/SUM60964.2024.10614557
M3 - Conference contribution
AN - SCOPUS:85201209204
T3 - LEOS Summer Topical Meeting
BT - 2024 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2024 - Proceedings
PB - Institute of Electrical and Electronics Engineers
T2 - 2024 IEEE Photonics Society Summer Topicals Meeting Series, SUM 2024
Y2 - 15 July 2024 through 17 July 2024
ER -