TY - GEN
T1 - Energy-Efficient Integrated Photonics for Next-Generation Computing
AU - Tossoun, Bassem
AU - Liang, Di
AU - Xiao, Xian
AU - Jha, Aashu
AU - Giamougiannis, George
AU - Cheung, Stanley
AU - London, Yanir
AU - Yuan, Yuan
AU - Peng, Yiwei
AU - Descos, Antoine
AU - Van Vaerenbergh, Thomas
AU - Kurczveil, Geza
AU - Fiorentino, Marco
AU - Beausoleil, Raymond G.
N1 - Publisher Copyright:
© 2024 SPIE.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Integrated photonic computing promises revolutionary strides in processing power, energy efficiency, and speed, propelling us into an era of unprecedented computational capabilities. By harnessing the innate properties of light, such as high-speed propagation, inherent parallel processing capabilities, and the ability to carry vast amounts of information, photonic computing transcends the limitations of traditional electronic architectures. Furthermore, silicon photonic neural networks hold promise to transform artificial intelligence by enabling faster training and inference with significantly reduced power consumption. This potential leap in efficiency could revolutionize data centers, high-performance computing, and edge computing, minimizing environmental impact while expanding the boundaries of computational possibilities. The latest research on our silicon photonic platform for next-generation optical compute accelerators will be presented and discussed.
AB - Integrated photonic computing promises revolutionary strides in processing power, energy efficiency, and speed, propelling us into an era of unprecedented computational capabilities. By harnessing the innate properties of light, such as high-speed propagation, inherent parallel processing capabilities, and the ability to carry vast amounts of information, photonic computing transcends the limitations of traditional electronic architectures. Furthermore, silicon photonic neural networks hold promise to transform artificial intelligence by enabling faster training and inference with significantly reduced power consumption. This potential leap in efficiency could revolutionize data centers, high-performance computing, and edge computing, minimizing environmental impact while expanding the boundaries of computational possibilities. The latest research on our silicon photonic platform for next-generation optical compute accelerators will be presented and discussed.
KW - integrated photonics
KW - neuromorphic computing
KW - optical computing
KW - optoelectronic devices
KW - silicon photonics
UR - https://www.scopus.com/pages/publications/85208648457
U2 - 10.1117/12.3008172
DO - 10.1117/12.3008172
M3 - Conference contribution
AN - SCOPUS:85208648457
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Optical Interconnects XXIV
A2 - Chen, Ray T.
A2 - Schroder, Henning
PB - SPIE
T2 - Optical Interconnects XXIV 2024
Y2 - 29 January 2024 through 31 January 2024
ER -